From 7569920be0a7ff9119aaede0d2dcda8e9f4db2f0 Mon Sep 17 00:00:00 2001
From: cephi <cephi_sui@protonmail.com>
Date: Wed, 18 Dec 2024 21:26:39 -0500
Subject: [PATCH] ahhhhh

---
 pytorch/batch.py                              |    2 +-
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 ..._16_csr_10_10_10_synthetic_5000_1e-05.json |    2 +-
 ...6_csr_10_10_10_synthetic_5000_1e-05.output |  583 ++--
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 ...6_csr_10_10_10_synthetic_5000_5e-05.output |  112 +-
 ..._coo_10_10_10_synthetic_100000_0.0001.json |    1 +
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 ..._coo_10_10_10_synthetic_10000_1e-05.output |  962 ++++++
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 ...coo_10_10_10_synthetic_50000_0.0001.output |   56 +
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 ..._coo_10_10_10_synthetic_50000_0.001.output |   56 +
 ..._16_coo_10_10_10_synthetic_50000_0.01.json |    1 +
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 ...6_coo_10_10_10_synthetic_50000_0.05.output |   39 +
 ...16_coo_10_10_10_synthetic_50000_1e-05.json |    1 +
 ..._coo_10_10_10_synthetic_50000_1e-05.output |   73 +
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 ..._coo_10_10_10_synthetic_50000_5e-05.output |   56 +
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 ..._coo_10_10_10_synthetic_5000_0.0001.output |   73 +
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 ...6_coo_10_10_10_synthetic_5000_1e-05.output |  429 +++
 ..._16_coo_10_10_10_synthetic_5000_5e-05.json |    1 +
 ...6_coo_10_10_10_synthetic_5000_5e-05.output |   73 +
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 ...sr_10_10_10_synthetic_100000_0.0001.output |   96 +-
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 ..._csr_10_10_10_synthetic_100000_0.01.output |   65 +
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 ...6_csr_10_10_10_synthetic_10000_0.0001.json |    2 +-
 ...csr_10_10_10_synthetic_10000_0.0001.output |   66 +-
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 ..._csr_10_10_10_synthetic_10000_0.001.output |   66 +-
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 ...16_csr_10_10_10_synthetic_10000_0.5.output |   65 +
 ...16_csr_10_10_10_synthetic_10000_1e-05.json |    2 +-
 ..._csr_10_10_10_synthetic_10000_1e-05.output | 2645 ++++++++-------
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 ..._csr_10_10_10_synthetic_10000_5e-05.output |   64 +-
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 ...sr_10_10_10_synthetic_500000_0.0001.output |   83 +-
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 ...csr_10_10_10_synthetic_500000_1e-05.output |  126 +-
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 ...csr_10_10_10_synthetic_50000_0.0001.output |   66 +-
 ...16_csr_10_10_10_synthetic_50000_0.001.json |    2 +-
 ..._csr_10_10_10_synthetic_50000_0.001.output |   75 +-
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 ...6_csr_10_10_10_synthetic_50000_0.01.output |   98 +-
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 ...6_csr_10_10_10_synthetic_50000_0.05.output |   65 +
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 ..._csr_10_10_10_synthetic_50000_1e-05.output |   66 +-
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 ..._csr_10_10_10_synthetic_50000_5e-05.output |   66 +-
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 ..._csr_10_10_10_synthetic_5000_0.0001.output |   86 +-
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 ..._16_csr_10_10_10_synthetic_5000_0.3.output |   74 +-
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 ..._16_csr_10_10_10_synthetic_5000_0.4.output |   78 +-
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 ..._16_csr_10_10_10_synthetic_5000_0.5.output |   74 +-
 ..._16_csr_10_10_10_synthetic_5000_1e-05.json |    2 +-
 ...6_csr_10_10_10_synthetic_5000_1e-05.output |  692 ++--
 ..._16_csr_10_10_10_synthetic_5000_5e-05.json |    2 +-
 ...6_csr_10_10_10_synthetic_5000_5e-05.output |   66 +-
 ..._csr_10_10_10_synthetic_100000_0.0001.json |    1 +
 ...sr_10_10_10_synthetic_100000_0.0001.output |   65 +
 ...6_csr_10_10_10_synthetic_100000_0.001.json |    1 +
 ...csr_10_10_10_synthetic_100000_0.001.output |   65 +
 ...6_csr_10_10_10_synthetic_100000_1e-05.json |    1 +
 ...csr_10_10_10_synthetic_100000_1e-05.output |   85 +
 ...6_csr_10_10_10_synthetic_100000_5e-05.json |    1 +
 ...csr_10_10_10_synthetic_100000_5e-05.output |   85 +
 ...6_csr_10_10_10_synthetic_10000_0.0001.json |    1 +
 ...csr_10_10_10_synthetic_10000_0.0001.output |   81 +
 ...16_csr_10_10_10_synthetic_10000_0.001.json |    1 +
 ..._csr_10_10_10_synthetic_10000_0.001.output |   85 +
 ..._16_csr_10_10_10_synthetic_10000_0.01.json |    1 +
 ...6_csr_10_10_10_synthetic_10000_0.01.output |   65 +
 ..._16_csr_10_10_10_synthetic_10000_0.05.json |    1 +
 ...6_csr_10_10_10_synthetic_10000_0.05.output |   45 +
 ...a_16_csr_10_10_10_synthetic_10000_0.1.json |    1 +
 ...16_csr_10_10_10_synthetic_10000_0.1.output |   45 +
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 ...16_csr_10_10_10_synthetic_10000_0.3.output |   45 +
 ...16_csr_10_10_10_synthetic_10000_1e-05.json |    1 +
 ..._csr_10_10_10_synthetic_10000_1e-05.output | 1307 +++++++
 ...16_csr_10_10_10_synthetic_10000_5e-05.json |    1 +
 ..._csr_10_10_10_synthetic_10000_5e-05.output |   81 +
 ..._csr_10_10_10_synthetic_500000_0.0001.json |    1 +
 ...sr_10_10_10_synthetic_500000_0.0001.output |   68 +
 ...6_csr_10_10_10_synthetic_500000_1e-05.json |    1 +
 ...csr_10_10_10_synthetic_500000_1e-05.output |   68 +
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 ...csr_10_10_10_synthetic_500000_5e-05.output |   68 +
 ...6_csr_10_10_10_synthetic_50000_0.0001.json |    1 +
 ...csr_10_10_10_synthetic_50000_0.0001.output |   85 +
 ...16_csr_10_10_10_synthetic_50000_0.001.json |    1 +
 ..._csr_10_10_10_synthetic_50000_0.001.output |   85 +
 ..._16_csr_10_10_10_synthetic_50000_0.01.json |    1 +
 ...6_csr_10_10_10_synthetic_50000_0.01.output |   45 +
 ...16_csr_10_10_10_synthetic_50000_1e-05.json |    1 +
 ..._csr_10_10_10_synthetic_50000_1e-05.output |   81 +
 ...16_csr_10_10_10_synthetic_50000_5e-05.json |    1 +
 ..._csr_10_10_10_synthetic_50000_5e-05.output |   85 +
 ...16_csr_10_10_10_synthetic_5000_0.0001.json |    1 +
 ..._csr_10_10_10_synthetic_5000_0.0001.output |   82 +
 ..._16_csr_10_10_10_synthetic_5000_0.001.json |    1 +
 ...6_csr_10_10_10_synthetic_5000_0.001.output |   81 +
 ...a_16_csr_10_10_10_synthetic_5000_0.01.json |    1 +
 ...16_csr_10_10_10_synthetic_5000_0.01.output |   85 +
 ...a_16_csr_10_10_10_synthetic_5000_0.05.json |    1 +
 ...16_csr_10_10_10_synthetic_5000_0.05.output |   65 +
 ...ra_16_csr_10_10_10_synthetic_5000_0.1.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.1.output |   65 +
 ...ra_16_csr_10_10_10_synthetic_5000_0.2.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.2.output |   45 +
 ...ra_16_csr_10_10_10_synthetic_5000_0.3.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.3.output |   45 +
 ...ra_16_csr_10_10_10_synthetic_5000_0.4.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.4.output |   45 +
 ...ra_16_csr_10_10_10_synthetic_5000_0.5.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.5.output |   45 +
 ..._16_csr_10_10_10_synthetic_5000_1e-05.json |    1 +
 ...6_csr_10_10_10_synthetic_5000_1e-05.output |  329 ++
 ..._16_csr_10_10_10_synthetic_5000_5e-05.json |    1 +
 ...6_csr_10_10_10_synthetic_5000_5e-05.output |   81 +
 ..._csr_10_10_10_synthetic_100000_0.0001.json |    1 +
 ...sr_10_10_10_synthetic_100000_0.0001.output |   85 +
 ...6_csr_10_10_10_synthetic_100000_0.001.json |    1 +
 ...csr_10_10_10_synthetic_100000_0.001.output |   85 +
 ...6_csr_10_10_10_synthetic_100000_1e-05.json |    1 +
 ...csr_10_10_10_synthetic_100000_1e-05.output |   85 +
 ...6_csr_10_10_10_synthetic_100000_5e-05.json |    1 +
 ...csr_10_10_10_synthetic_100000_5e-05.output |   85 +
 ...6_csr_10_10_10_synthetic_10000_0.0001.json |    1 +
 ...csr_10_10_10_synthetic_10000_0.0001.output |   81 +
 ...16_csr_10_10_10_synthetic_10000_0.001.json |    1 +
 ..._csr_10_10_10_synthetic_10000_0.001.output |  105 +
 ..._16_csr_10_10_10_synthetic_10000_0.01.json |    1 +
 ...6_csr_10_10_10_synthetic_10000_0.01.output |   85 +
 ..._16_csr_10_10_10_synthetic_10000_0.05.json |    1 +
 ...6_csr_10_10_10_synthetic_10000_0.05.output |   85 +
 ...p_16_csr_10_10_10_synthetic_10000_0.1.json |    1 +
 ...16_csr_10_10_10_synthetic_10000_0.1.output |   65 +
 ...p_16_csr_10_10_10_synthetic_10000_0.2.json |    1 +
 ...16_csr_10_10_10_synthetic_10000_0.2.output |   85 +
 ...p_16_csr_10_10_10_synthetic_10000_0.3.json |    1 +
 ...16_csr_10_10_10_synthetic_10000_0.3.output |   65 +
 ...16_csr_10_10_10_synthetic_10000_1e-05.json |    1 +
 ..._csr_10_10_10_synthetic_10000_1e-05.output | 1307 +++++++
 ...16_csr_10_10_10_synthetic_10000_5e-05.json |    1 +
 ..._csr_10_10_10_synthetic_10000_5e-05.output |   81 +
 ..._csr_10_10_10_synthetic_500000_0.0001.json |    1 +
 ...sr_10_10_10_synthetic_500000_0.0001.output |   68 +
 ...6_csr_10_10_10_synthetic_500000_1e-05.json |    1 +
 ...csr_10_10_10_synthetic_500000_1e-05.output |   89 +
 ...6_csr_10_10_10_synthetic_500000_5e-05.json |    1 +
 ...csr_10_10_10_synthetic_500000_5e-05.output |   68 +
 ...6_csr_10_10_10_synthetic_50000_0.0001.json |    1 +
 ...csr_10_10_10_synthetic_50000_0.0001.output |  105 +
 ...16_csr_10_10_10_synthetic_50000_0.001.json |    1 +
 ..._csr_10_10_10_synthetic_50000_0.001.output |   85 +
 ..._16_csr_10_10_10_synthetic_50000_0.01.json |    1 +
 ...6_csr_10_10_10_synthetic_50000_0.01.output |   85 +
 ...16_csr_10_10_10_synthetic_50000_1e-05.json |    1 +
 ..._csr_10_10_10_synthetic_50000_1e-05.output |   81 +
 ...16_csr_10_10_10_synthetic_50000_5e-05.json |    1 +
 ..._csr_10_10_10_synthetic_50000_5e-05.output |   85 +
 ...16_csr_10_10_10_synthetic_5000_0.0001.json |    1 +
 ..._csr_10_10_10_synthetic_5000_0.0001.output |   81 +
 ..._16_csr_10_10_10_synthetic_5000_0.001.json |    1 +
 ...6_csr_10_10_10_synthetic_5000_0.001.output |   81 +
 ...p_16_csr_10_10_10_synthetic_5000_0.01.json |    1 +
 ...16_csr_10_10_10_synthetic_5000_0.01.output |   85 +
 ...p_16_csr_10_10_10_synthetic_5000_0.05.json |    1 +
 ...16_csr_10_10_10_synthetic_5000_0.05.output |   85 +
 ...3p_16_csr_10_10_10_synthetic_5000_0.1.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.1.output |   85 +
 ...3p_16_csr_10_10_10_synthetic_5000_0.2.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.2.output |   85 +
 ...3p_16_csr_10_10_10_synthetic_5000_0.3.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.3.output |   85 +
 ...3p_16_csr_10_10_10_synthetic_5000_0.4.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.4.output |   85 +
 ...3p_16_csr_10_10_10_synthetic_5000_0.5.json |    1 +
 ..._16_csr_10_10_10_synthetic_5000_0.5.output |   85 +
 ..._16_csr_10_10_10_synthetic_5000_1e-05.json |    1 +
 ...6_csr_10_10_10_synthetic_5000_1e-05.output |  329 ++
 ..._16_csr_10_10_10_synthetic_5000_5e-05.json |    1 +
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 ..._csr_10_10_10_synthetic_10000_1e-05.output | 1307 +++++++
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 ...6_csr_10_10_10_synthetic_5000_1e-05.output |  356 ++
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 ..._coo_10_10_10_synthetic_10000_1e-05.output |  962 ++++++
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 ..._csr_10_10_10_synthetic_10000_1e-05.output | 2538 +++++++-------
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 ...1_csr_10_10_10_synthetic_5000_1e-05.output |  664 ++--
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 ..._coo_10_10_10_synthetic_10000_1e-05.output |  962 ++++++
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 ..._csr_10_10_10_synthetic_10000_1e-05.output | 1200 +++++++
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 ...1_csr_10_10_10_synthetic_5000_1e-05.output |  329 ++
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 ...sr_10_10_10_synthetic_100000_0.0001.output |   85 +
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 ..._csr_10_10_10_synthetic_10000_0.001.output |  105 +
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 ..._csr_10_10_10_synthetic_10000_1e-05.output | 1521 +++++++++
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 ...1_csr_10_10_10_synthetic_5000_1e-05.output |  329 ++
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 ..._csr_10_10_10_synthetic_100000_0.0001.json |    1 +
 ...sr_10_10_10_synthetic_100000_0.0001.output |   85 +
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 ..._1_csr_10_10_10_synthetic_10000_0.3.output |   45 +
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 ..._csr_10_10_10_synthetic_10000_1e-05.output | 1307 +++++++
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 ..._csr_10_10_10_synthetic_10000_5e-05.output |   81 +
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 ...sr_10_10_10_synthetic_500000_0.0001.output |   47 +
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 ..._csr_10_10_10_synthetic_5000_0.0001.output |   81 +
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 ...1_csr_10_10_10_synthetic_5000_1e-05.output |  329 ++
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 ..._coo_10_10_10_synthetic_100000_0.0001.json |    1 +
 ...oo_10_10_10_synthetic_100000_0.0001.output |   74 +
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 ..._coo_10_10_10_synthetic_100000_0.01.output |   39 +
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 ...coo_10_10_10_synthetic_100000_1e-05.output |   73 +
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 ...x_coo_10_10_10_synthetic_10000_0.0001.json |    1 +
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 ..._coo_10_10_10_synthetic_10000_1e-05.output |  855 +++++
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 ..._coo_10_10_10_synthetic_10000_5e-05.output |   74 +
 ..._coo_10_10_10_synthetic_500000_0.0001.json |    1 +
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 ...coo_10_10_10_synthetic_500000_1e-05.output |   90 +
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 ...coo_10_10_10_synthetic_50000_0.0001.output |   73 +
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 ..._coo_10_10_10_synthetic_50000_0.001.output |   73 +
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 ..._coo_10_10_10_synthetic_50000_5e-05.output |   90 +
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 ..._coo_10_10_10_synthetic_5000_0.0001.output |   73 +
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 ...x_coo_10_10_10_synthetic_5000_1e-05.output |  429 +++
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 ..._csr_10_10_10_synthetic_100000_0.0001.json |    2 +-
 ...sr_10_10_10_synthetic_100000_0.0001.output |   60 +-
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 ..._csr_10_10_10_synthetic_100000_0.01.output |   65 +
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 ..._csr_10_10_10_synthetic_10000_0.001.output |   72 +-
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 ...ax_csr_10_10_10_synthetic_10000_0.5.output |   45 +
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 ..._csr_10_10_10_synthetic_10000_1e-05.output | 2621 +++++++-------
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 ..._csr_10_10_10_synthetic_10000_5e-05.output |   66 +-
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 ...sr_10_10_10_synthetic_500000_0.0001.output |   90 +-
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 ..._csr_10_10_10_synthetic_50000_5e-05.output |   66 +-
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 ..._max_csr_10_10_10_synthetic_5000_0.01.json |    2 +-
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 ..._coo_10_10_10_synthetic_100000_0.0001.json |    1 +
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 ...coo_10_10_10_synthetic_100000_0.001.output |   39 +
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 ..._coo_10_10_10_synthetic_100000_0.01.output |   39 +
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 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.json
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.output
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.json
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.output
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.json
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.output
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.json
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.output
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.json
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.output
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.json
 create mode 100644 pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.output

diff --git a/pytorch/batch.py b/pytorch/batch.py
index 8978d39..ff01165 100644
--- a/pytorch/batch.py
+++ b/pytorch/batch.py
@@ -117,7 +117,7 @@ elif args.matrix_type == MatrixType.SYNTHETIC:
     parameter_list = enumerate([(size, density)
         for size in args.synthetic_size
         for density in args.synthetic_density
-        if size ** 2 * density <= 50000000])
+        if size ** 2 * density <= 100000000])
 
 #for i, matrix in enumerate(glob.glob(f'{args.matrix_dir.rstrip("/")}/*.mtx')):
 for i, parameter in parameter_list:
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_010.json b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_010.json
index e69de29..200315d 100644
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_010.json
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_010.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 4950, "MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.512001991271973, "TIME_S_1KI": 2.12363676591353, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 850.9319224691392, "W": 64.26, "J_1KI": 171.90543888265438, "W_1KI": 12.981818181818182, "W_D": 29.119500000000002, "J_D": 385.600873270154, "W_D_1KI": 5.882727272727273, "J_D_1KI": 1.1884297520661158}
diff --git a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_010.output b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_010.output
index 3c8a67d..d142886 100644
--- a/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_010.output
+++ b/pytorch/output_as-caida_1core/epyc_7313p_1_coo_10_10_10_as-caida_G_010.output
@@ -1,11 +1,11 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '100', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.2207937240600586}
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 0.23576855659484863}
 
 tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
                        [    0,     0,     0,  ..., 31353, 31360, 31378]]),
        values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
        size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
-tensor([0.7193, 0.7070, 0.1341,  ..., 0.0458, 0.8165, 0.6087])
+tensor([0.0720, 0.8900, 0.2156,  ..., 0.8187, 0.1034, 0.8459])
 Matrix Type: SuiteSparse
 Matrix: as-caida_G_010
 Matrix Format: coo
@@ -14,16 +14,16 @@ Rows: 31379
 Size: 984641641
 NNZ: 74994
 Density: 7.616375021864427e-05
-Time: 0.2207937240600586 seconds
+Time: 0.23576855659484863 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '4755', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
-{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.089951038360596}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '4453', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 9.444213628768921}
 
 tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
                        [    0,     0,     0,  ..., 31353, 31360, 31378]]),
        values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
        size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
-tensor([0.1581, 0.9236, 0.8715,  ..., 0.0128, 0.7061, 0.4474])
+tensor([0.5426, 0.3622, 0.1360,  ..., 0.9710, 0.4622, 0.4763])
 Matrix Type: SuiteSparse
 Matrix: as-caida_G_010
 Matrix Format: coo
@@ -32,13 +32,16 @@ Rows: 31379
 Size: 984641641
 NNZ: 74994
 Density: 7.616375021864427e-05
-Time: 10.089951038360596 seconds
+Time: 9.444213628768921 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'suitesparse', 'coo', '4950', '-m', 'matrices/as-caida_pruned/as-caida_G_010.mtx', '-c', '1']
+{"MATRIX_TYPE": "SuiteSparse", "MATRIX_FILE": "as-caida_G_010", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [31379, 31379], "MATRIX_ROWS": 31379, "MATRIX_SIZE": 984641641, "MATRIX_NNZ": 74994, "MATRIX_DENSITY": 7.616375021864427e-05, "TIME_S": 10.512001991271973}
 
 tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
                        [    0,     0,     0,  ..., 31353, 31360, 31378]]),
        values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
        size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
-tensor([0.1581, 0.9236, 0.8715,  ..., 0.0128, 0.7061, 0.4474])
+tensor([0.9719, 0.1948, 0.3482,  ..., 0.1448, 0.3015, 0.3034])
 Matrix Type: SuiteSparse
 Matrix: as-caida_G_010
 Matrix Format: coo
@@ -47,9 +50,28 @@ Rows: 31379
 Size: 984641641
 NNZ: 74994
 Density: 7.616375021864427e-05
-Time: 10.089951038360596 seconds
+Time: 10.512001991271973 seconds
 
-[44.57, 39.27, 38.78, 39.37, 39.08, 39.0, 39.29, 40.29, 38.94, 38.6]
-[64.64]
-12.744337558746338
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 4755, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.089951038360596, 'TIME_S_1KI': 2.1219665695816183, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 823.7939797973632, 'W': 64.64}
+tensor(indices=tensor([[ 1040,  2020,  2054,  ...,   160,   160, 12170],
+                       [    0,     0,     0,  ..., 31353, 31360, 31378]]),
+       values=tensor([3., 1., 1.,  ..., 3., 3., 3.]),
+       size=(31379, 31379), nnz=74994, layout=torch.sparse_coo)
+tensor([0.9719, 0.1948, 0.3482,  ..., 0.1448, 0.3015, 0.3034])
+Matrix Type: SuiteSparse
+Matrix: as-caida_G_010
+Matrix Format: coo
+Shape: torch.Size([31379, 31379])
+Rows: 31379
+Size: 984641641
+NNZ: 74994
+Density: 7.616375021864427e-05
+Time: 10.512001991271973 seconds
+
+[39.44, 39.17, 38.57, 39.04, 39.83, 39.07, 38.63, 38.66, 40.52, 38.52]
+[64.26]
+13.242015600204468
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 4950, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.512001991271973, 'TIME_S_1KI': 2.12363676591353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 850.9319224691392, 'W': 64.26}
+[39.44, 39.17, 38.57, 39.04, 39.83, 39.07, 38.63, 38.66, 40.52, 38.52, 39.2, 38.77, 38.71, 38.46, 38.74, 39.68, 39.05, 39.32, 38.59, 38.84]
+702.8100000000001
+35.1405
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 4950, 'MATRIX_TYPE': 'SuiteSparse', 'MATRIX_FILE': 'as-caida_G_010', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [31379, 31379], 'MATRIX_ROWS': 31379, 'MATRIX_SIZE': 984641641, 'MATRIX_NNZ': 74994, 'MATRIX_DENSITY': 7.616375021864427e-05, 'TIME_S': 10.512001991271973, 'TIME_S_1KI': 2.12363676591353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 850.9319224691392, 'W': 64.26, 'J_1KI': 171.90543888265438, 'W_1KI': 12.981818181818182, 'W_D': 29.119500000000002, 'J_D': 385.600873270154, 'W_D_1KI': 5.882727272727273, 'J_D_1KI': 1.1884297520661158}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..39f928f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 367, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.419839143753052, "TIME_S_1KI": 28.39193227180668, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 397.99056369781505, "W": 27.315883432832518, "J_1KI": 1084.4429528550818, "W_1KI": 74.43020008946189, "W_D": 12.377883432832519, "J_D": 180.34491972160353, "W_D_1KI": 33.727202814257545, "J_D_1KI": 91.89973518871265}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..fbabafc
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,73 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 100000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3841264247894287}
+
+tensor(indices=tensor([[12468,  7286, 42070,  ..., 41435, 93609, 87646],
+                       [40435, 70110, 80235,  ..., 72237, 42645,  8439]]),
+       values=tensor([0.4478, 0.9727, 0.1236,  ..., 0.3635, 0.6500, 0.0042]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.3810, 0.0198, 0.2872,  ..., 0.7346, 0.9919, 0.8495])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.3841264247894287 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 273 -ss 100000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.802507400512695}
+
+tensor(indices=tensor([[66870, 12119, 38015,  ..., 43745, 48193, 42393],
+                       [24231, 38254, 74788,  ..., 95676, 54345, 18633]]),
+       values=tensor([0.5489, 0.7321, 0.1915,  ..., 0.2369, 0.8050, 0.8821]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.9003, 0.7844, 0.6361,  ..., 0.6011, 0.0782, 0.7656])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 7.802507400512695 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 367 -ss 100000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.419839143753052}
+
+tensor(indices=tensor([[49924, 69461, 63269,  ..., 50062, 11998, 67211],
+                       [44612, 71174, 43698,  ..., 38509, 65750, 86040]]),
+       values=tensor([0.0322, 0.9610, 0.0897,  ..., 0.9817, 0.7794, 0.1450]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.2779, 0.4359, 0.1613,  ..., 0.8529, 0.4594, 0.0392])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.419839143753052 seconds
+
+tensor(indices=tensor([[49924, 69461, 63269,  ..., 50062, 11998, 67211],
+                       [44612, 71174, 43698,  ..., 38509, 65750, 86040]]),
+       values=tensor([0.0322, 0.9610, 0.0897,  ..., 0.9817, 0.7794, 0.1450]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.2779, 0.4359, 0.1613,  ..., 0.8529, 0.4594, 0.0392])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.419839143753052 seconds
+
+[16.36, 16.36, 16.48, 16.6, 16.68, 16.72, 16.76, 16.64, 16.56, 16.68]
+[16.88, 16.8, 17.76, 17.76, 19.72, 23.36, 28.64, 32.92, 36.04, 38.68, 38.72, 38.96, 38.72, 38.76]
+14.569931983947754
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 367, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.419839143753052, 'TIME_S_1KI': 28.39193227180668, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 397.99056369781505, 'W': 27.315883432832518}
+[16.36, 16.36, 16.48, 16.6, 16.68, 16.72, 16.76, 16.64, 16.56, 16.68, 16.4, 16.64, 16.6, 16.88, 16.76, 16.64, 16.64, 16.56, 16.28, 16.48]
+298.76
+14.937999999999999
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..b140396
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 34, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.180678606033325, "TIME_S_1KI": 299.4317237068625, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 422.515793838501, "W": 25.557128131432332, "J_1KI": 12426.935112897088, "W_1KI": 751.6802391597745, "W_D": 10.417128131432332, "J_D": 172.2181435775757, "W_D_1KI": 306.38612151271565, "J_D_1KI": 9011.356515079871}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..6364c6c
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.001.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 100000 -sd 0.001 -c 16']
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+                       [17388,  8501, 29360,  ..., 49103, 79345, 61915]]),
+       values=tensor([0.5711, 0.4325, 0.6219,  ..., 0.4496, 0.6456, 0.2954]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.6293, 0.9496, 0.1593,  ..., 0.6227, 0.5166, 0.2304])
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+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
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+Time: 3.0579380989074707 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 34 -ss 100000 -sd 0.001 -c 16']
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..6e99db4
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.01.json
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+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 10, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 30.31324076652527, "TIME_S_1KI": 3031.324076652527, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1621.7109812927251, "W": 22.805805675157114, "J_1KI": 162171.09812927252, "W_1KI": 2280.580567515711, "W_D": 7.854805675157115, "J_D": 558.5518354740149, "W_D_1KI": 785.4805675157115, "J_D_1KI": 78548.05675157116}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..8174072
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_0.01.output
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+                       [94305, 46771, 25673,  ..., 59297, 21852, 23926]]),
+       values=tensor([0.3778, 0.8729, 0.5621,  ..., 0.9145, 0.2094, 0.6532]),
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..956e898
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3314, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.203248262405396, "TIME_S_1KI": 3.0788317025966796, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 375.60593753814703, "W": 27.842859053307212, "J_1KI": 113.33914832171003, "W_1KI": 8.401586920128912, "W_D": 12.732859053307209, "J_D": 171.76890681743623, "W_D_1KI": 3.8421421404065206, "J_D_1KI": 1.159366970551153}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..37d7d3d
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_1e-05.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 100000 -sd 1e-05 -c 16']
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+       values=tensor([0.1394, 0.0752, 0.4473,  ..., 0.7415, 0.9621, 0.2206]),
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+tensor([0.0051, 0.8634, 0.8851,  ..., 0.0171, 0.3637, 0.0740])
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..243c2a4
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..40c7d73
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_100000_5e-05.output
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+       size=(100000, 100000), nnz=500000, layout=torch.sparse_coo)
+tensor([0.0261, 0.4519, 0.6566,  ..., 0.0991, 0.5006, 0.4804])
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..8f61976
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.0001.json
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..5d9036e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.0001.output
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..27cd1bb
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.001.json
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..faa7fdb
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.001.output
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..de8892c
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 483, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.741916418075562, "TIME_S_1KI": 22.239992584007375, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 276.1104020309449, "W": 19.461832909928876, "J_1KI": 571.6571470619976, "W_1KI": 40.29364991703701, "W_D": 4.5208329099288775, "J_D": 64.13830588579185, "W_D_1KI": 9.359902505028732, "J_D_1KI": 19.378680134635054}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..ac4464e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,73 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.2565734386444092}
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+tensor(indices=tensor([[3040, 1975, 8185,  ..., 4197, 2982, 9717],
+                       [2548, 7576, 6395,  ..., 4840, 4908,  516]]),
+       values=tensor([0.8984, 0.8219, 0.3232,  ..., 0.0275, 0.0542, 0.2107]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.4740, 0.4212, 0.7772,  ..., 0.6354, 0.9914, 0.8150])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.2565734386444092 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 409 -ss 10000 -sd 0.01 -c 16']
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+                       [9525, 7935,   62,  ..., 8942, 8068, 5683]]),
+       values=tensor([0.2394, 0.6993, 0.1720,  ..., 0.6730, 0.4942, 0.9458]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.0813, 0.6209, 0.8868,  ..., 0.0609, 0.7307, 0.2857])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
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+                       [ 139, 5237, 3565,  ..., 4894, 7373, 9358]]),
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+tensor([0.6523, 0.3723, 0.1460,  ..., 0.5424, 0.6615, 0.0631])
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+                       [ 139, 5237, 3565,  ..., 4894, 7373, 9358]]),
+       values=tensor([0.0056, 0.8699, 0.9380,  ..., 0.1444, 0.5055, 0.8646]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.6523, 0.3723, 0.1460,  ..., 0.5424, 0.6615, 0.0631])
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..dc7bd78
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 95, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.398029565811157, "TIME_S_1KI": 109.45294279801219, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 302.1709572029114, "W": 19.739419615702108, "J_1KI": 3180.746917925383, "W_1KI": 207.78336437581166, "W_D": 5.013419615702105, "J_D": 76.74540759706494, "W_D_1KI": 52.77283806002216, "J_D_1KI": 555.5035585265491}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..0d64315
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 1.1015262603759766}
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+tensor(indices=tensor([[5975, 1448,  909,  ..., 2303,   64, 4395],
+                       [3014, 2842, 4837,  ..., 5281, 7605, 6977]]),
+       values=tensor([0.1469, 0.9843, 0.0934,  ..., 0.3480, 0.5731, 0.6993]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_coo)
+tensor([0.4636, 0.4700, 0.7576,  ..., 0.5837, 0.3639, 0.2563])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 1.1015262603759766 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 95 -ss 10000 -sd 0.05 -c 16']
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+                       [2637, 6322, 1951,  ..., 9020, 3345, 4258]]),
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+tensor(indices=tensor([[5766, 7593, 1135,  ..., 8989, 4526,  639],
+                       [2637, 6322, 1951,  ..., 9020, 3345, 4258]]),
+       values=tensor([0.3933, 0.8952, 0.2235,  ..., 0.1950, 0.5530, 0.4784]),
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..d434265
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 48, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.384194135665894, "TIME_S_1KI": 216.33737782637277, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 329.3590560531616, "W": 20.245232558826288, "J_1KI": 6861.647001107534, "W_1KI": 421.775678308881, "W_D": 4.979232558826286, "J_D": 81.00451949262619, "W_D_1KI": 103.7340116422143, "J_D_1KI": 2161.125242546131}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..dbb56c5
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.1 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 2.1636908054351807}
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+tensor(indices=tensor([[9377, 3039,  357,  ...,  252, 8928, 7869],
+                       [5085, 6961, 3696,  ..., 1339, 3476, 6424]]),
+       values=tensor([0.9709, 0.1419, 0.4987,  ..., 0.9197, 0.7910, 0.9313]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.8336, 0.3321, 0.0806,  ..., 0.9733, 0.5686, 0.4957])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
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+Density: 0.1
+Time: 2.1636908054351807 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 48 -ss 10000 -sd 0.1 -c 16']
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+                       [3742,   49, 4010,  ..., 1461, 8112,  474]]),
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+tensor([0.4396, 0.1326, 0.6146,  ..., 0.3496, 0.5544, 0.0468])
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+                       [3742,   49, 4010,  ..., 1461, 8112,  474]]),
+       values=tensor([0.4683, 0.1972, 0.1221,  ..., 0.3478, 0.8050, 0.6367]),
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..3235224
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.2.json
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+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 24, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.480116844177246, "TIME_S_1KI": 436.67153517405194, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 396.95239097595214, "W": 20.533611291083126, "J_1KI": 16539.68295733134, "W_1KI": 855.5671371284635, "W_D": 5.676611291083127, "J_D": 109.73931437063219, "W_D_1KI": 236.52547046179694, "J_D_1KI": 9855.227935908206}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..6d19006
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.2.output
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+                       [5432, 8646, 1435,  ..., 1579, 7665, 2705]]),
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+tensor([0.3474, 0.9695, 0.8806,  ..., 0.9277, 0.9905, 0.4679])
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+Shape: torch.Size([10000, 10000])
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..1fd20d4
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 16, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.457778215408325, "TIME_S_1KI": 653.6111384630203, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 462.69632364273076, "W": 20.647427058166027, "J_1KI": 28918.520227670673, "W_1KI": 1290.4641911353767, "W_D": 5.6004270581660265, "J_D": 125.50217532396319, "W_D_1KI": 350.02669113537667, "J_D_1KI": 21876.668195961043}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..7631221
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.3.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.3 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 6.5346596240997314}
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+                       [3626, 9686, 5386,  ..., 3395, 5291, 3142]]),
+       values=tensor([0.3625, 0.1114, 0.3966,  ..., 0.2089, 0.4672, 0.6258]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_coo)
+tensor([0.4665, 0.2779, 0.7138,  ..., 0.8048, 0.7923, 0.8202])
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
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+Time: 6.5346596240997314 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 16 -ss 10000 -sd 0.3 -c 16']
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..7ffe2f3
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 12, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.456308841705322, "TIME_S_1KI": 871.3590701421102, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 534.4857844734192, "W": 21.000350357084837, "J_1KI": 44540.48203945161, "W_1KI": 1750.0291964237365, "W_D": 6.247350357084837, "J_D": 159.00305945897108, "W_D_1KI": 520.6125297570698, "J_D_1KI": 43384.377479755814}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..87d80ed
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.4.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.4 -c 16']
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+                       [5997, 6274, 6063,  ..., 6327,  874, 3023]]),
+       values=tensor([0.9143, 0.7056, 0.2664,  ..., 0.4979, 0.2124, 0.5956]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_coo)
+tensor([0.4532, 0.5300, 0.4786,  ..., 0.4756, 0.7807, 0.1145])
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+Shape: torch.Size([10000, 10000])
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 12 -ss 10000 -sd 0.4 -c 16']
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..de2a970
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 10, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.909107446670532, "TIME_S_1KI": 1090.9107446670532, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 612.7037763309478, "W": 21.449824734352383, "J_1KI": 61270.37763309478, "W_1KI": 2144.9824734352383, "W_D": 6.421824734352382, "J_D": 183.43628977870932, "W_D_1KI": 642.1824734352383, "J_D_1KI": 64218.24734352383}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..8ba8261
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_0.5.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.5 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.909107446670532}
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+                       [8671, 9730, 4445,  ..., 1398, 3205, 9692]]),
+       values=tensor([0.1559, 0.5245, 0.4425,  ..., 0.4269, 0.1155, 0.3969]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_coo)
+tensor([0.3163, 0.5754, 0.1899,  ..., 0.5042, 0.8957, 0.5433])
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+Time: 10.909107446670532 seconds
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+tensor(indices=tensor([[4804, 7773, 4408,  ..., 1833, 4291, 2965],
+                       [8671, 9730, 4445,  ..., 1398, 3205, 9692]]),
+       values=tensor([0.1559, 0.5245, 0.4425,  ..., 0.4269, 0.1155, 0.3969]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_coo)
+tensor([0.3163, 0.5754, 0.1899,  ..., 0.5042, 0.8957, 0.5433])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 10.909107446670532 seconds
+
+[16.6, 16.72, 16.68, 16.68, 16.68, 16.68, 16.88, 16.84, 16.84, 16.96]
+[17.0, 16.72, 17.76, 18.56, 20.4, 21.08, 22.24, 22.64, 22.64, 24.88, 24.8, 24.96, 24.84, 23.04, 21.04, 20.8, 20.88, 20.92, 21.16, 21.12, 23.88, 24.24, 24.24, 25.2, 25.16, 23.8, 22.72, 23.0]
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+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 10, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.909107446670532, 'TIME_S_1KI': 1090.9107446670532, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 612.7037763309478, 'W': 21.449824734352383}
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+300.56
+15.028
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 10, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.909107446670532, 'TIME_S_1KI': 1090.9107446670532, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 612.7037763309478, 'W': 21.449824734352383, 'J_1KI': 61270.37763309478, 'W_1KI': 2144.9824734352383, 'W_D': 6.421824734352382, 'J_D': 183.43628977870932, 'W_D_1KI': 642.1824734352383, 'J_D_1KI': 64218.24734352383}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..8aedb00
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 329192, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.653273582458496, "TIME_S_1KI": 0.03236188480418265, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 261.9017709636688, "W": 18.418336412038514, "J_1KI": 0.7955897195669057, "W_1KI": 0.05595013369716917, "W_D": 3.6753364120385132, "J_D": 52.261892369985574, "W_D_1KI": 0.01116471971384029, "J_D_1KI": 3.39155256319725e-05}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..8728a5e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,907 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.006676435470581055}
+
+tensor(indices=tensor([[6770, 3309, 8056,  ...,  269, 5308, 6845],
+                       [6596, 3896, 4483,  ..., 3312, 7741,  330]]),
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+                      0.8630, 0.8973, 0.1106, 0.1243, 0.9820, 0.5929]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_coo)
+tensor([0.2430, 0.4800, 0.1603,  ..., 0.2671, 0.3314, 0.3631])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.006676435470581055 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 15726 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.5300266742706299}
+
+tensor(indices=tensor([[5997, 1278, 1062,  ..., 9685, 5347, 4668],
+                       [3395, 3427, 4627,  ..., 7441, 3913,  814]]),
+       values=tensor([6.2407e-01, 1.2538e-01, 4.1784e-01, 9.8965e-01,
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..b6e1857
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_5e-05.json
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+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 88995, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.443303108215332, "TIME_S_1KI": 0.11734707689437981, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 270.51667034149176, "W": 19.057403527564766, "J_1KI": 3.039683918663877, "W_1KI": 0.2141401598692597, "W_D": 3.6324035275647653, "J_D": 51.56136334061629, "W_D_1KI": 0.0408158158049864, "J_D_1KI": 0.0004586304377210675}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..635beaf
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_10000_5e-05.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 5e-05 -c 16']
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+       values=tensor([0.8049, 0.4289, 0.7593,  ..., 0.5596, 0.6808, 0.2640]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_coo)
+tensor([0.5931, 0.9373, 0.6013,  ..., 0.5668, 0.9421, 0.1475])
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
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+Density: 5e-05
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 13482 -ss 10000 -sd 5e-05 -c 16']
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..e0dbcb2
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_0.0001.json
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+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 10, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 12.64857006072998, "TIME_S_1KI": 1264.857006072998, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 612.8634984588622, "W": 24.05277526457356, "J_1KI": 61286.34984588622, "W_1KI": 2405.277526457356, "W_D": 9.193775264573558, "J_D": 234.25692921972256, "W_D_1KI": 919.3775264573558, "J_D_1KI": 91937.75264573557}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..8dab665
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_0.0001.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 500000 -sd 0.0001 -c 16']
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+tensor([0.3817, 0.5445, 0.8793,  ..., 0.8491, 0.3699, 0.3958])
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+                       [274216, 175918, 137273,  ..., 106291,  40355, 344584]]),
+       values=tensor([0.5214, 0.8195, 0.0840,  ..., 0.5932, 0.8280, 0.8598]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_coo)
+tensor([0.3817, 0.5445, 0.8793,  ..., 0.8491, 0.3699, 0.3958])
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..6ad14ae
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 85, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.22414517402649, "TIME_S_1KI": 120.28406087089988, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 441.509245891571, "W": 30.226765643160533, "J_1KI": 5194.226422253776, "W_1KI": 355.6090075665945, "W_D": 15.141765643160534, "J_D": 221.1691985011101, "W_D_1KI": 178.13841933130038, "J_D_1KI": 2095.746109780004}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..6f69b65
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 500000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.2221043109893799}
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+tensor(indices=tensor([[453456,  92215, 427412,  ..., 418685, 103637, 233573],
+                       [ 33636,  48718, 269177,  ..., 336146, 311811, 477976]]),
+       values=tensor([0.0230, 0.0242, 0.1102,  ..., 0.5701, 0.0679, 0.6937]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_coo)
+tensor([0.0691, 0.5190, 0.0132,  ..., 0.3549, 0.1733, 0.7132])
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+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 1.2221043109893799 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 85 -ss 500000 -sd 1e-05 -c 16']
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+                       [344184, 213565, 477901,  ..., 246665, 325062,  34759]]),
+       values=tensor([0.5697, 0.5352, 0.0767,  ..., 0.7613, 0.3405, 0.0952]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_coo)
+tensor([0.6035, 0.7626, 0.1866,  ..., 0.6682, 0.2071, 0.1192])
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+tensor(indices=tensor([[260240,  85351, 123903,  ..., 321649,  47553, 249584],
+                       [344184, 213565, 477901,  ..., 246665, 325062,  34759]]),
+       values=tensor([0.5697, 0.5352, 0.0767,  ..., 0.7613, 0.3405, 0.0952]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_coo)
+tensor([0.6035, 0.7626, 0.1866,  ..., 0.6682, 0.2071, 0.1192])
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..0e74a30
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 16, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.14887547492981, "TIME_S_1KI": 634.3047171831131, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 446.6620703125, "W": 24.244680563469874, "J_1KI": 27916.37939453125, "W_1KI": 1515.2925352168672, "W_D": 9.130680563469873, "J_D": 168.21540185546877, "W_D_1KI": 570.6675352168671, "J_D_1KI": 35666.720951054194}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..867dfb1
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 500000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.252246856689453}
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+tensor(indices=tensor([[168482, 109157, 139135,  ...,  23544, 376992, 400952],
+                       [430905, 452074, 310643,  ..., 282410, 427036, 174169]]),
+       values=tensor([0.7070, 0.6863, 0.4236,  ..., 0.8628, 0.0765, 0.6124]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_coo)
+tensor([0.3127, 0.2848, 0.1569,  ..., 0.6228, 0.9606, 0.0654])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 6.252246856689453 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 16 -ss 500000 -sd 5e-05 -c 16']
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+tensor(indices=tensor([[416108,   2264, 471967,  ...,    939, 148738, 423683],
+                       [  3445, 337531, 280778,  ...,  50285, 129885,  10398]]),
+       values=tensor([0.8474, 0.6306, 0.8958,  ..., 0.7968, 0.8541, 0.8366]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_coo)
+tensor([0.5104, 0.4137, 0.5956,  ..., 0.5821, 0.0614, 0.8299])
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+tensor(indices=tensor([[416108,   2264, 471967,  ...,    939, 148738, 423683],
+                       [  3445, 337531, 280778,  ...,  50285, 129885,  10398]]),
+       values=tensor([0.8474, 0.6306, 0.8958,  ..., 0.7968, 0.8541, 0.8366]),
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+tensor([0.5104, 0.4137, 0.5956,  ..., 0.5821, 0.0614, 0.8299])
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..55dea33
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1778, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.44342303276062, "TIME_S_1KI": 5.873691244522284, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 359.84609330177307, "W": 26.690882073452276, "J_1KI": 202.3881289661266, "W_1KI": 15.011744698229625, "W_D": 11.780882073452275, "J_D": 158.82968491315842, "W_D_1KI": 6.625917926576083, "J_D_1KI": 3.7266130070731625}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..23ed816
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,73 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 50000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.09349679946899414}
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+tensor(indices=tensor([[11244, 30890, 16389,  ..., 29148,  5026,   856],
+                       [10492, 33035, 45125,  ..., 39727, 41928,  8324]]),
+       values=tensor([0.4818, 0.4973, 0.6152,  ..., 0.1205, 0.4026, 0.7356]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_coo)
+tensor([0.5690, 0.0428, 0.6445,  ..., 0.6439, 0.9256, 0.1962])
+Matrix Type: synthetic
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+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
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+Time: 0.09349679946899414 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 1123 -ss 50000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.628721237182617}
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+                       [37715,  8739, 28481,  ..., 39488,  2329,  6895]]),
+       values=tensor([0.7313, 0.0416, 0.7748,  ..., 0.4902, 0.4676, 0.4179]),
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+tensor([0.8552, 0.4811, 0.9521,  ..., 0.6838, 0.6988, 0.7549])
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+                       [38506, 15721, 34837,  ..., 48627, 30893, 13903]]),
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+                       [38506, 15721, 34837,  ..., 48627, 30893, 13903]]),
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..d13001e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 184, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.326260328292847, "TIME_S_1KI": 56.120980045069814, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 411.99043563842775, "W": 28.23306413648923, "J_1KI": 2239.0784545566726, "W_1KI": 153.4405659591806, "W_D": 13.442064136489229, "J_D": 196.15305773043633, "W_D_1KI": 73.0546963939632, "J_D_1KI": 397.0363934454522}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..9a3e8b7
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 50000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.5684177875518799}
+
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..3eecef1
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.01.json
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..f5624bc
--- /dev/null
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..c4000b3
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_1e-05.json
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..1315030
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_1e-05.output
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..d19b064
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_5e-05.json
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+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3481, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.402421712875366, "TIME_S_1KI": 2.9883429224002773, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 353.7775730323791, "W": 26.24712784244181, "J_1KI": 101.63101782027553, "W_1KI": 7.540111416961163, "W_D": 11.136127842441809, "J_D": 150.10070072150228, "W_D_1KI": 3.199117449710373, "J_D_1KI": 0.9190225365442037}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..9f73158
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_50000_5e-05.output
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..df2aa61
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 168281, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.558050394058228, "TIME_S_1KI": 0.06274059694236561, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 258.021646194458, "W": 18.156079475026804, "J_1KI": 1.5332785412165248, "W_1KI": 0.10789144035884506, "W_D": 3.2230794750268057, "J_D": 45.80417667293551, "W_D_1KI": 0.01915296126732552, "J_D_1KI": 0.00011381535210347883}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..f840f81
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.0001.output
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..4a3f027
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.001.json
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..e1ac0b4
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.001.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.001 -c 16']
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..77e8d55
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1968, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.277064561843872, "TIME_S_1KI": 5.222085651343431, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 273.486970872879, "W": 19.269726615017994, "J_1KI": 138.9669567443491, "W_1KI": 9.791527751533533, "W_D": 4.205726615017992, "J_D": 59.6900752792358, "W_D_1KI": 2.1370562068180856, "J_D_1KI": 1.0859025441148809}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..c0a6151
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.01.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.01 -c 16']
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+                       [ 999, 1582, 2374,  ..., 1857, 3396,  926]]),
+       values=tensor([0.2793, 0.4750, 0.9003,  ..., 0.4581, 0.2120, 0.3581]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_coo)
+tensor([0.4895, 0.6805, 0.6750,  ..., 0.8029, 0.3171, 0.4262])
+Matrix Type: synthetic
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+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
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+Density: 0.01
+Time: 0.0581815242767334 seconds
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+       values=tensor([0.5038, 0.7305, 0.7346,  ..., 0.1644, 0.1385, 0.4258]),
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..24be72e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 370, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.596680641174316, "TIME_S_1KI": 28.639677408579235, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 258.78833953857423, "W": 19.654690540248374, "J_1KI": 699.4279446988493, "W_1KI": 53.120785243914526, "W_D": 4.792690540248376, "J_D": 63.10414423942572, "W_D_1KI": 12.953217676346963, "J_D_1KI": 35.00869642255936}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..42ea30e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.05 -c 16']
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+                       [ 724, 4244, 4649,  ..., 3333, 3645,  147]]),
+       values=tensor([0.8581, 0.8007, 0.7199,  ..., 0.3825, 0.8322, 0.5968]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_coo)
+tensor([0.5296, 0.9285, 0.1851,  ..., 0.3657, 0.2915, 0.3561])
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+Shape: torch.Size([5000, 5000])
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..7cb3694
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 188, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.026696681976318, "TIME_S_1KI": 53.33349298923574, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 278.07759874343867, "W": 19.571934998478163, "J_1KI": 1479.136163528929, "W_1KI": 104.10603722594767, "W_D": 4.928934998478162, "J_D": 70.03019419622416, "W_D_1KI": 26.217739353607246, "J_D_1KI": 139.45606039152793}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..8a03cf4
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.1 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.5578980445861816}
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+                       [1304, 3255, 2512,  ..., 2111, 1142, 1638]]),
+       values=tensor([0.4560, 0.5837, 0.9413,  ..., 0.9865, 0.1177, 0.2599]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_coo)
+tensor([0.1895, 0.4537, 0.0116,  ..., 0.1378, 0.1077, 0.1212])
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+                       [2623, 4026, 3385,  ..., 2226, 3224, 3278]]),
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+                       [2623, 4026, 3385,  ..., 2226, 3224, 3278]]),
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..cbbfff1
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 96, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.222102403640747, "TIME_S_1KI": 106.48023337125778, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 283.6666414546967, "W": 18.589544841781695, "J_1KI": 2954.860848486424, "W_1KI": 193.64109210189264, "W_D": 3.844544841781696, "J_D": 58.66572487235075, "W_D_1KI": 40.04734210189267, "J_D_1KI": 417.15981356138195}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..23cc18c
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.2.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.2 -c 16']
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+                       [3400, 4600, 1739,  ...,  224,  551, 4286]]),
+       values=tensor([0.1494, 0.6110, 0.0642,  ..., 0.9959, 0.2030, 0.5785]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_coo)
+tensor([0.4794, 0.4904, 0.5821,  ..., 0.5127, 0.0773, 0.9625])
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+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 96 -ss 5000 -sd 0.2 -c 16']
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+                       [ 444, 3717, 4556,  ..., 2406, 4953,  659]]),
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+                       [ 444, 3717, 4556,  ..., 2406, 4953,  659]]),
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..b356e55
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 63, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.021673440933228, "TIME_S_1KI": 159.07418160211472, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 296.1811933517456, "W": 19.37098300965613, "J_1KI": 4701.288783361042, "W_1KI": 307.4759207881925, "W_D": 4.480983009656132, "J_D": 68.51396723270419, "W_D_1KI": 71.12671443898623, "J_D_1KI": 1128.9954672854956}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..fa8e0f5
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.3.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.3 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.6492033004760742}
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+tensor(indices=tensor([[ 945, 4589, 2774,  ..., 2909,  736, 2667],
+                       [2560, 3363, 4131,  ..., 2105, 3960, 2928]]),
+       values=tensor([0.2201, 0.4510, 0.7924,  ..., 0.8323, 0.0651, 0.5160]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_coo)
+tensor([0.2493, 0.4630, 0.6009,  ..., 0.6971, 0.1027, 0.3339])
+Matrix Type: synthetic
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+Shape: torch.Size([5000, 5000])
+Rows: 5000
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+Time: 1.6492033004760742 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 63 -ss 5000 -sd 0.3 -c 16']
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+                       [1294, 2850, 2607,  ...,  362, 1048, 4735]]),
+       values=tensor([0.0149, 0.1469, 0.7394,  ..., 0.7181, 0.3269, 0.1660]),
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..a8b662b
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 48, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.146132707595825, "TIME_S_1KI": 211.37776474157968, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.9102397632598, "W": 20.01824681068372, "J_1KI": 6789.79666173458, "W_1KI": 417.04680855591084, "W_D": 5.35524681068372, "J_D": 87.18694441962234, "W_D_1KI": 111.56764188924417, "J_D_1KI": 2324.325872692587}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..c604cad
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.4 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 2.167170524597168}
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+tensor(indices=tensor([[ 488, 4773, 4527,  ..., 2629, 4924, 3969],
+                       [ 860, 3190, 4868,  ..., 1859, 4874, 4035]]),
+       values=tensor([0.1016, 0.7267, 0.8882,  ..., 0.9839, 0.5575, 0.6801]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.8969, 0.8054, 0.0829,  ..., 0.5187, 0.3613, 0.7237])
+Matrix Type: synthetic
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+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
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+Time: 2.167170524597168 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 48 -ss 5000 -sd 0.4 -c 16']
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+                       [ 103, 2605, 2675,  ..., 1234, 3413,  938]]),
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+                       [ 103, 2605, 2675,  ..., 1234, 3413,  938]]),
+       values=tensor([0.9440, 0.3445, 0.4659,  ..., 0.6576, 0.2575, 0.7871]),
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..1c8d177
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 39, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.389892101287842, "TIME_S_1KI": 266.4074897766113, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 356.30051391601563, "W": 20.603235750156045, "J_1KI": 9135.91061323117, "W_1KI": 528.2880961578473, "W_D": 5.519235750156046, "J_D": 95.44648996162417, "W_D_1KI": 141.51886538861655, "J_D_1KI": 3628.688856118373}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..197ce63
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.5 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 2.680294990539551}
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..0081da2
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 652466, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.900923252105713, "TIME_S_1KI": 0.016707266358868836, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 277.6165154457092, "W": 19.57926332943202, "J_1KI": 0.42548809508190344, "W_1KI": 0.030008097478538377, "W_D": 4.625263329432018, "J_D": 65.58211445093146, "W_D_1KI": 0.007088895558438322, "J_D_1KI": 1.0864773886207592e-05}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..f9929ad
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,535 @@
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+Shape: torch.Size([5000, 5000])
+Rows: 5000
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diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..a5b1d25
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 286349, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.45237112045288, "TIME_S_1KI": 0.03650220926370576, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 261.584655122757, "W": 18.391800548576306, "J_1KI": 0.9135169151027488, "W_1KI": 0.06422861804503004, "W_D": 3.7758005485763064, "J_D": 53.702816192627004, "W_D_1KI": 0.013186009200577989, "J_D_1KI": 4.604873493735961e-05}
diff --git a/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..70417c4
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_coo_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,90 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.0067138671875}
+
+tensor(indices=tensor([[2960, 3352, 2272,  ..., 4354, 3613, 3081],
+                       [1683,  910, 4825,  ..., 4503, 3569, 3443]]),
+       values=tensor([0.7470, 0.7400, 0.2423,  ..., 0.7452, 0.6241, 0.1819]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.8861, 0.1443, 0.1411,  ..., 0.6596, 0.1915, 0.7341])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.0067138671875 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 15639 -ss 5000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.6672103404998779}
+
+tensor(indices=tensor([[2657,   10,  213,  ..., 3459, 1898, 1412],
+                       [1570,  991, 1711,  ..., 3008, 4849, 3361]]),
+       values=tensor([0.0477, 0.3374, 0.7312,  ..., 0.1165, 0.6978, 0.1653]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.8830, 0.3419, 0.0938,  ..., 0.1921, 0.2616, 0.4458])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.6672103404998779 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 246113 -ss 5000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.024578332901001}
+
+tensor(indices=tensor([[3242,  153,  953,  ..., 3164, 1173, 3828],
+                       [1636,  461, 1866,  ..., 3350, 1390, 1427]]),
+       values=tensor([0.1174, 0.8189, 0.7761,  ..., 0.6490, 0.4361, 0.8132]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.9981, 0.3203, 0.0360,  ..., 0.8201, 0.6163, 0.8541])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 9.024578332901001 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 286349 -ss 5000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.45237112045288}
+
+tensor(indices=tensor([[4546,  268, 2475,  ..., 4578, 2693, 1114],
+                       [2810, 1887, 1881,  ..., 2898, 4458, 2685]]),
+       values=tensor([0.3289, 0.4846, 0.8504,  ..., 0.7188, 0.8266, 0.2598]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.3695, 0.7282, 0.1314,  ..., 0.6685, 0.6494, 0.9188])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.45237112045288 seconds
+
+tensor(indices=tensor([[4546,  268, 2475,  ..., 4578, 2693, 1114],
+                       [2810, 1887, 1881,  ..., 2898, 4458, 2685]]),
+       values=tensor([0.3289, 0.4846, 0.8504,  ..., 0.7188, 0.8266, 0.2598]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.3695, 0.7282, 0.1314,  ..., 0.6685, 0.6494, 0.9188])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.45237112045288 seconds
+
+[16.56, 16.44, 16.2, 16.36, 16.12, 16.08, 16.36, 16.4, 16.48, 16.4]
+[16.4, 16.28, 16.4, 17.72, 18.88, 21.72, 22.28, 22.24, 22.04, 20.92, 20.2, 20.04, 20.12, 20.12]
+14.222895383834839
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 286349, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.45237112045288, 'TIME_S_1KI': 0.03650220926370576, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 261.584655122757, 'W': 18.391800548576306}
+[16.56, 16.44, 16.2, 16.36, 16.12, 16.08, 16.36, 16.4, 16.48, 16.4, 16.36, 16.08, 15.96, 16.04, 15.84, 16.12, 16.32, 16.16, 16.44, 16.52]
+292.32
+14.616
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 286349, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.45237112045288, 'TIME_S_1KI': 0.03650220926370576, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 261.584655122757, 'W': 18.391800548576306, 'J_1KI': 0.9135169151027488, 'W_1KI': 0.06422861804503004, 'W_D': 3.7758005485763064, 'J_D': 53.702816192627004, 'W_D_1KI': 0.013186009200577989, 'J_D_1KI': 4.604873493735961e-05}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.json
index e9b3498..ddab6be 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1748, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.143786191940308, "TIME_S_1KI": 5.8030813455036085, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 530.3414198303223, "W": 36.295146632325824, "J_1KI": 303.3989815962942, "W_1KI": 20.763813862886625, "W_D": 17.602146632325823, "J_D": 257.20098424220083, "W_D_1KI": 10.069877936113171, "J_D_1KI": 5.760799734618519}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1752, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.485095500946045, "TIME_S_1KI": 5.984643550768291, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 512.186183013916, "W": 34.91048158652564, "J_1KI": 292.3437117659338, "W_1KI": 19.926073964911897, "W_D": 16.22748158652564, "J_D": 238.08012596821786, "W_D_1KI": 9.262261179523769, "J_D_1KI": 5.286678755435941}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.output
index 2da7ef7..89c6336 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,14 +1,34 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6005632877349854}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6985688209533691}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       7,      17,  ...,  999970,
+tensor(crow_indices=tensor([      0,      13,      22,  ...,  999981,
+                             999991, 1000000]),
+       col_indices=tensor([ 3841,  8582,  8659,  ..., 46850, 51232, 80903]),
+       values=tensor([0.4815, 0.2359, 0.4039,  ..., 0.8218, 0.1508, 0.2420]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4121, 0.4277, 0.6441,  ..., 0.2185, 0.3156, 0.3986])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.6985688209533691 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1503 -ss 100000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.003351211547852}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,      22,  ...,  999979,
                              999989, 1000000]),
-       col_indices=tensor([27708, 32922, 35240,  ..., 82805, 88487, 98517]),
-       values=tensor([0.0088, 0.7733, 0.0012,  ..., 0.6420, 0.7382, 0.2177]),
+       col_indices=tensor([16506, 20710, 37506,  ..., 59060, 78382, 91823]),
+       values=tensor([0.6315, 0.8392, 0.3677,  ..., 0.4353, 0.5265, 0.8972]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1129, 0.5965, 0.7496,  ..., 0.0902, 0.9107, 0.7724])
+tensor([0.3202, 0.2384, 0.4035,  ..., 0.5180, 0.1753, 0.3933])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 0.6005632877349854 seconds
+Time: 9.003351211547852 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1748 -ss 100000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.143786191940308}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1752 -ss 100000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.485095500946045}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      10,      23,  ...,  999980,
-                             999992, 1000000]),
-       col_indices=tensor([ 8017, 17251, 18992,  ..., 72823, 91334, 91663]),
-       values=tensor([0.4596, 0.1797, 0.9797,  ..., 0.0499, 0.7967, 0.0183]),
+tensor(crow_indices=tensor([      0,      10,      27,  ...,  999981,
+                             999986, 1000000]),
+       col_indices=tensor([27575, 38852, 42502,  ..., 91134, 92148, 97111]),
+       values=tensor([0.1986, 0.8938, 0.8330,  ..., 0.0983, 0.0891, 0.3928]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8873, 0.5523, 0.3791,  ..., 0.8812, 0.4027, 0.2259])
+tensor([0.2333, 0.4383, 0.7667,  ..., 0.5123, 0.4425, 0.6550])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.143786191940308 seconds
+Time: 10.485095500946045 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      10,      23,  ...,  999980,
-                             999992, 1000000]),
-       col_indices=tensor([ 8017, 17251, 18992,  ..., 72823, 91334, 91663]),
-       values=tensor([0.4596, 0.1797, 0.9797,  ..., 0.0499, 0.7967, 0.0183]),
+tensor(crow_indices=tensor([      0,      10,      27,  ...,  999981,
+                             999986, 1000000]),
+       col_indices=tensor([27575, 38852, 42502,  ..., 91134, 92148, 97111]),
+       values=tensor([0.1986, 0.8938, 0.8330,  ..., 0.0983, 0.0891, 0.3928]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8873, 0.5523, 0.3791,  ..., 0.8812, 0.4027, 0.2259])
+tensor([0.2333, 0.4383, 0.7667,  ..., 0.5123, 0.4425, 0.6550])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.143786191940308 seconds
+Time: 10.485095500946045 seconds
 
-[20.48, 20.6, 20.64, 20.68, 21.12, 21.12, 21.24, 21.12, 21.04, 21.04]
-[20.76, 20.76, 21.04, 22.36, 24.68, 32.36, 38.56, 45.04, 50.12, 51.88, 51.52, 52.0, 52.0, 51.84]
-14.611910104751587
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1748, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.143786191940308, 'TIME_S_1KI': 5.8030813455036085, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 530.3414198303223, 'W': 36.295146632325824}
-[20.48, 20.6, 20.64, 20.68, 21.12, 21.12, 21.24, 21.12, 21.04, 21.04, 20.36, 20.64, 20.56, 20.72, 20.68, 20.52, 20.72, 20.76, 20.52, 20.48]
-373.86
-18.693
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1748, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.143786191940308, 'TIME_S_1KI': 5.8030813455036085, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 530.3414198303223, 'W': 36.295146632325824, 'J_1KI': 303.3989815962942, 'W_1KI': 20.763813862886625, 'W_D': 17.602146632325823, 'J_D': 257.20098424220083, 'W_D_1KI': 10.069877936113171, 'J_D_1KI': 5.760799734618519}
+[21.12, 20.96, 20.8, 20.6, 20.8, 20.8, 20.8, 20.84, 20.72, 20.68]
+[20.76, 20.8, 21.92, 21.92, 22.88, 27.32, 33.44, 40.64, 45.96, 51.28, 52.0, 51.52, 51.4, 51.84]
+14.671415567398071
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1752, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.485095500946045, 'TIME_S_1KI': 5.984643550768291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 512.186183013916, 'W': 34.91048158652564}
+[21.12, 20.96, 20.8, 20.6, 20.8, 20.8, 20.8, 20.84, 20.72, 20.68, 21.0, 21.04, 20.92, 20.76, 20.48, 20.48, 20.48, 20.64, 20.64, 21.0]
+373.65999999999997
+18.683
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1752, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.485095500946045, 'TIME_S_1KI': 5.984643550768291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 512.186183013916, 'W': 34.91048158652564, 'J_1KI': 292.3437117659338, 'W_1KI': 19.926073964911897, 'W_D': 16.22748158652564, 'J_D': 238.08012596821786, 'W_D_1KI': 9.262261179523769, 'J_D_1KI': 5.286678755435941}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.json
index fdbe85f..c7f2b4c 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 175, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.183927774429321, "TIME_S_1KI": 63.90815871102469, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 628.162894821167, "W": 35.42748603907242, "J_1KI": 3589.5022561209544, "W_1KI": 202.44277736612813, "W_D": 16.71548603907242, "J_D": 296.38140530395515, "W_D_1KI": 95.51706308041383, "J_D_1KI": 545.8117890309362}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 183, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.392640113830566, "TIME_S_1KI": 56.790383135686156, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 621.8855728912355, "W": 35.106665438111044, "J_1KI": 3398.2818190777894, "W_1KI": 191.83970184760133, "W_D": 16.547665438111046, "J_D": 293.1282214522363, "W_D_1KI": 90.42440130115325, "J_D_1KI": 494.12241148171177}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.output
index 887a602..676e0dc 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.9738054275512695}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.7204060554504395}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      100,      229,  ...,  9999825,
-                             9999913, 10000000]),
-       col_indices=tensor([ 2839,  3131,  5153,  ..., 92533, 94576, 98932]),
-       values=tensor([0.4697, 0.9996, 0.7875,  ..., 0.5192, 0.5202, 0.9540]),
+tensor(crow_indices=tensor([       0,       98,      204,  ...,  9999786,
+                             9999897, 10000000]),
+       col_indices=tensor([ 2168,  2221,  3670,  ..., 97171, 97920, 99173]),
+       values=tensor([0.5868, 0.2768, 0.5813,  ..., 0.2211, 0.2231, 0.3014]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.9598, 0.0952, 0.8851,  ..., 0.3844, 0.8104, 0.5939])
+tensor([0.6888, 0.8719, 0.0407,  ..., 0.0271, 0.8141, 0.6850])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 5.9738054275512695 seconds
+Time: 5.7204060554504395 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 175 -ss 100000 -sd 0.001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.183927774429321}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 183 -ss 100000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.392640113830566}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       95,      193,  ...,  9999801,
-                             9999901, 10000000]),
-       col_indices=tensor([ 2869,  4015,  6080,  ..., 94953, 95635, 98117]),
-       values=tensor([0.0857, 0.9758, 0.7363,  ..., 0.8151, 0.8595, 0.7723]),
+tensor(crow_indices=tensor([       0,       97,      203,  ...,  9999791,
+                             9999906, 10000000]),
+       col_indices=tensor([  466,  1594,  2031,  ..., 98883, 99435, 99456]),
+       values=tensor([0.5174, 0.4660, 0.4037,  ..., 0.7630, 0.3193, 0.8740]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.7586, 0.5970, 0.0221,  ..., 0.6721, 0.2659, 0.4588])
+tensor([0.4869, 0.9209, 0.1121,  ..., 0.4031, 0.7408, 0.7156])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 11.183927774429321 seconds
+Time: 10.392640113830566 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       95,      193,  ...,  9999801,
-                             9999901, 10000000]),
-       col_indices=tensor([ 2869,  4015,  6080,  ..., 94953, 95635, 98117]),
-       values=tensor([0.0857, 0.9758, 0.7363,  ..., 0.8151, 0.8595, 0.7723]),
+tensor(crow_indices=tensor([       0,       97,      203,  ...,  9999791,
+                             9999906, 10000000]),
+       col_indices=tensor([  466,  1594,  2031,  ..., 98883, 99435, 99456]),
+       values=tensor([0.5174, 0.4660, 0.4037,  ..., 0.7630, 0.3193, 0.8740]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.7586, 0.5970, 0.0221,  ..., 0.6721, 0.2659, 0.4588])
+tensor([0.4869, 0.9209, 0.1121,  ..., 0.4031, 0.7408, 0.7156])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 11.183927774429321 seconds
+Time: 10.392640113830566 seconds
 
-[20.52, 20.84, 20.72, 20.68, 20.8, 20.68, 20.68, 20.72, 20.64, 20.64]
-[20.64, 20.76, 20.84, 24.4, 27.0, 28.6, 31.96, 32.96, 34.0, 38.76, 44.2, 47.96, 51.6, 50.84, 50.88, 50.6, 50.76]
-17.730947494506836
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 175, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.183927774429321, 'TIME_S_1KI': 63.90815871102469, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 628.162894821167, 'W': 35.42748603907242}
-[20.52, 20.84, 20.72, 20.68, 20.8, 20.68, 20.68, 20.72, 20.64, 20.64, 20.64, 20.64, 20.64, 20.72, 20.76, 20.76, 21.0, 20.92, 21.36, 21.56]
-374.24
-18.712
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 175, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.183927774429321, 'TIME_S_1KI': 63.90815871102469, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 628.162894821167, 'W': 35.42748603907242, 'J_1KI': 3589.5022561209544, 'W_1KI': 202.44277736612813, 'W_D': 16.71548603907242, 'J_D': 296.38140530395515, 'W_D_1KI': 95.51706308041383, 'J_D_1KI': 545.8117890309362}
+[20.4, 20.28, 20.32, 20.28, 20.48, 20.48, 20.88, 20.84, 20.76, 20.76]
+[20.72, 20.64, 21.04, 22.32, 24.12, 25.84, 28.68, 31.24, 36.6, 42.2, 42.2, 45.8, 51.08, 52.16, 52.0, 52.08, 52.32]
+17.714173793792725
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 183, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.392640113830566, 'TIME_S_1KI': 56.790383135686156, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 621.8855728912355, 'W': 35.106665438111044}
+[20.4, 20.28, 20.32, 20.28, 20.48, 20.48, 20.88, 20.84, 20.76, 20.76, 20.8, 20.68, 20.96, 21.0, 20.84, 20.72, 20.64, 20.56, 20.32, 20.32]
+371.17999999999995
+18.558999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 183, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.392640113830566, 'TIME_S_1KI': 56.790383135686156, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 621.8855728912355, 'W': 35.106665438111044, 'J_1KI': 3398.2818190777894, 'W_1KI': 191.83970184760133, 'W_D': 16.547665438111046, 'J_D': 293.1282214522363, 'W_D_1KI': 90.42440130115325, 'J_D_1KI': 494.12241148171177}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..4c7e6de
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 55.23988056182861, "TIME_S_1KI": 552.3988056182861, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3392.229231271745, "W": 34.21525226903433, "J_1KI": 33922.29231271745, "W_1KI": 342.1525226903433, "W_D": 15.519252269034329, "J_D": 1538.6372364158642, "W_D_1KI": 155.19252269034328, "J_D_1KI": 1551.9252269034328}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..ca8594f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 55.23988056182861}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       975,      2002,  ...,
+                             99998038,  99999032, 100000000]),
+       col_indices=tensor([  193,   475,   708,  ..., 99676, 99979, 99985]),
+       values=tensor([0.3846, 0.7039, 0.8140,  ..., 0.8966, 0.5567, 0.1696]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.9784, 0.7882, 0.1982,  ..., 0.8110, 0.1934, 0.3487])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 55.23988056182861 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       975,      2002,  ...,
+                             99998038,  99999032, 100000000]),
+       col_indices=tensor([  193,   475,   708,  ..., 99676, 99979, 99985]),
+       values=tensor([0.3846, 0.7039, 0.8140,  ..., 0.8966, 0.5567, 0.1696]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.9784, 0.7882, 0.1982,  ..., 0.8110, 0.1934, 0.3487])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 55.23988056182861 seconds
+
+[21.08, 21.04, 20.84, 21.08, 21.04, 21.04, 20.92, 20.88, 20.88, 20.72]
+[20.8, 20.76, 20.92, 22.24, 24.44, 26.24, 27.2, 27.2, 26.72, 25.88, 27.64, 27.64, 29.6, 30.76, 30.64, 29.12, 27.36, 25.36, 26.56, 29.12, 32.64, 36.12, 36.04, 35.4, 34.08, 34.08, 28.52, 28.84, 28.6, 28.12, 27.72, 28.28, 28.12, 28.84, 29.32, 29.84, 29.72, 29.68, 29.68, 29.48, 29.92, 33.16, 35.88, 39.28, 42.08, 43.0, 42.28, 41.44, 40.48, 40.64, 40.88, 40.72, 40.84, 41.68, 41.32, 41.48, 41.32, 41.32, 41.4, 41.6, 41.04, 40.84, 40.52, 40.48, 41.32, 41.8, 41.56, 41.52, 40.16, 39.76, 39.12, 38.96, 39.96, 40.84, 40.92, 40.92, 41.64, 40.48, 41.04, 40.68, 40.48, 41.48, 42.2, 42.24, 42.56, 42.28, 41.8, 41.24, 40.48, 40.48, 40.96, 41.72, 41.92, 41.4, 41.24, 41.24]
+99.1437737941742
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 55.23988056182861, 'TIME_S_1KI': 552.3988056182861, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3392.229231271745, 'W': 34.21525226903433}
+[21.08, 21.04, 20.84, 21.08, 21.04, 21.04, 20.92, 20.88, 20.88, 20.72, 20.16, 20.16, 20.4, 20.36, 20.56, 20.68, 20.84, 20.88, 20.84, 21.0]
+373.92
+18.696
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 55.23988056182861, 'TIME_S_1KI': 552.3988056182861, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3392.229231271745, 'W': 34.21525226903433, 'J_1KI': 33922.29231271745, 'W_1KI': 342.1525226903433, 'W_D': 15.519252269034329, 'J_D': 1538.6372364158642, 'W_D_1KI': 155.19252269034328, 'J_D_1KI': 1551.9252269034328}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.json
index eff1e86..f4c211f 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 11597, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.436057329177856, "TIME_S_1KI": 0.8998928454926151, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 483.6025880336762, "W": 33.00177532885384, "J_1KI": 41.700662932971994, "W_1KI": 2.845716592985586, "W_D": 14.225775328853839, "J_D": 208.462172027588, "W_D_1KI": 1.2266771862424626, "J_D_1KI": 0.10577538900081596}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 11845, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.452897548675537, "TIME_S_1KI": 0.8824734106100073, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 491.721949520111, "W": 33.55721565186675, "J_1KI": 41.51303921655644, "W_1KI": 2.833027914889552, "W_D": 15.062215651866751, "J_D": 220.71026754021645, "W_D_1KI": 1.2716095949233222, "J_D_1KI": 0.10735412367440457}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.output
index cdfe200..42e30bb 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.12187767028808594}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.19909358024597168}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      2,  ...,  99998,  99999,
+tensor(crow_indices=tensor([     0,      4,      5,  ...,  99997,  99999,
                             100000]),
-       col_indices=tensor([99237, 81965, 52149,  ..., 94819, 50598, 82628]),
-       values=tensor([0.3300, 0.8237, 0.5005,  ..., 0.6469, 0.1010, 0.4687]),
+       col_indices=tensor([12266, 41353, 64119,  ..., 57579, 58990,  6971]),
+       values=tensor([0.6227, 0.7944, 0.7450,  ..., 0.9056, 0.8637, 0.0316]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0038, 0.2456, 0.3182,  ..., 0.7163, 0.7510, 0.9775])
+tensor([0.5310, 0.7756, 0.0968,  ..., 0.3911, 0.0764, 0.5885])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 0.12187767028808594 seconds
+Time: 0.19909358024597168 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 8615 -ss 100000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.799410104751587}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 5273 -ss 100000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.112189292907715}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998,  99999,
+tensor(crow_indices=tensor([     0,      0,      2,  ...,  99998,  99999,
                             100000]),
-       col_indices=tensor([88588, 42232, 90125,  ..., 27244, 80106, 39636]),
-       values=tensor([0.8018, 0.8315, 0.5597,  ..., 0.5532, 0.0030, 0.5793]),
+       col_indices=tensor([18500, 89431, 21652,  ...,  1449, 96967, 19441]),
+       values=tensor([0.9093, 0.3424, 0.7088,  ..., 0.3078, 0.7479, 0.8254]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1929, 0.1411, 0.4568,  ..., 0.6294, 0.2188, 0.4350])
+tensor([0.7902, 0.1848, 0.8411,  ..., 0.5530, 0.0625, 0.5516])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 7.799410104751587 seconds
+Time: 5.112189292907715 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 11597 -ss 100000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.436057329177856}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10830 -ss 100000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.599655628204346}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998,  99998,
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99999, 100000,
                             100000]),
-       col_indices=tensor([24172, 83350, 29274,  ..., 76990, 53592, 71081]),
-       values=tensor([0.7302, 0.8346, 0.3553,  ..., 0.4222, 0.8183, 0.0288]),
+       col_indices=tensor([ 4135, 30090, 75785,  ..., 75263, 77636,  9635]),
+       values=tensor([0.5430, 0.9022, 0.0393,  ..., 0.9343, 0.9359, 0.0313]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8537, 0.9431, 0.0277,  ..., 0.1357, 0.7019, 0.9196])
+tensor([0.4424, 0.3111, 0.3025,  ..., 0.8284, 0.3246, 0.9597])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.436057329177856 seconds
+Time: 9.599655628204346 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 11845 -ss 100000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.452897548675537}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998,  99998,
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99997, 100000,
                             100000]),
-       col_indices=tensor([24172, 83350, 29274,  ..., 76990, 53592, 71081]),
-       values=tensor([0.7302, 0.8346, 0.3553,  ..., 0.4222, 0.8183, 0.0288]),
+       col_indices=tensor([26237,  4052, 39558,  ..., 16301, 35459, 98674]),
+       values=tensor([0.0699, 0.2116, 0.3702,  ..., 0.5467, 0.2088, 0.3545]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8537, 0.9431, 0.0277,  ..., 0.1357, 0.7019, 0.9196])
+tensor([0.6982, 0.4327, 0.4762,  ..., 0.0773, 0.8958, 0.0557])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +76,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.436057329177856 seconds
+Time: 10.452897548675537 seconds
 
-[21.08, 20.64, 20.76, 20.76, 20.92, 21.32, 21.32, 21.36, 21.0, 20.8]
-[20.64, 20.36, 20.72, 23.08, 24.72, 29.44, 35.88, 40.08, 43.56, 45.4, 45.96, 45.6, 45.36, 46.04]
-14.653835535049438
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11597, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.436057329177856, 'TIME_S_1KI': 0.8998928454926151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 483.6025880336762, 'W': 33.00177532885384}
-[21.08, 20.64, 20.76, 20.76, 20.92, 21.32, 21.32, 21.36, 21.0, 20.8, 20.36, 20.32, 20.32, 20.44, 20.64, 20.8, 21.0, 21.16, 21.12, 21.04]
-375.52
-18.776
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11597, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.436057329177856, 'TIME_S_1KI': 0.8998928454926151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 483.6025880336762, 'W': 33.00177532885384, 'J_1KI': 41.700662932971994, 'W_1KI': 2.845716592985586, 'W_D': 14.225775328853839, 'J_D': 208.462172027588, 'W_D_1KI': 1.2266771862424626, 'J_D_1KI': 0.10577538900081596}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99997, 100000,
+                            100000]),
+       col_indices=tensor([26237,  4052, 39558,  ..., 16301, 35459, 98674]),
+       values=tensor([0.0699, 0.2116, 0.3702,  ..., 0.5467, 0.2088, 0.3545]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6982, 0.4327, 0.4762,  ..., 0.0773, 0.8958, 0.0557])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.452897548675537 seconds
+
+[20.44, 20.4, 20.64, 20.88, 20.76, 20.76, 20.88, 20.8, 20.96, 21.04]
+[20.84, 20.88, 21.72, 26.12, 28.0, 32.48, 37.88, 39.16, 42.96, 44.8, 45.0, 45.32, 45.04, 45.04]
+14.653240442276001
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11845, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.452897548675537, 'TIME_S_1KI': 0.8824734106100073, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 491.721949520111, 'W': 33.55721565186675}
+[20.44, 20.4, 20.64, 20.88, 20.76, 20.76, 20.88, 20.8, 20.96, 21.04, 20.68, 20.68, 20.52, 20.24, 20.2, 20.04, 20.28, 20.24, 20.36, 20.36]
+369.9
+18.494999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11845, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.452897548675537, 'TIME_S_1KI': 0.8824734106100073, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 491.721949520111, 'W': 33.55721565186675, 'J_1KI': 41.51303921655644, 'W_1KI': 2.833027914889552, 'W_D': 15.062215651866751, 'J_D': 220.71026754021645, 'W_D_1KI': 1.2716095949233222, 'J_D_1KI': 0.10735412367440457}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.json
index 67cd5e0..5fb8701 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3297, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.891435861587524, "TIME_S_1KI": 3.3034382352403777, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 528.672490940094, "W": 36.05478479295784, "J_1KI": 160.34955745832394, "W_1KI": 10.935633846817664, "W_D": 17.45578479295784, "J_D": 255.95474444794658, "W_D_1KI": 5.2944448871573675, "J_D_1KI": 1.605837090432929}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3160, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.255688428878784, "TIME_S_1KI": 3.2454710217970835, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 463.90646287918094, "W": 34.111621125184975, "J_1KI": 146.8058426832851, "W_1KI": 10.794816811767397, "W_D": 15.559621125184975, "J_D": 211.6055632019043, "W_D_1KI": 4.92393073581803, "J_D_1KI": 1.5582059290563386}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.output
index 3c3d8e5..7c7ecd3 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_100000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.4573814868927002}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.332211971282959}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     10,     16,  ..., 499990, 499995,
+tensor(crow_indices=tensor([     0,     10,     13,  ..., 499990, 499997,
                             500000]),
-       col_indices=tensor([ 5164,  6869,  8448,  ..., 29154, 68140, 97893]),
-       values=tensor([0.8386, 0.0921, 0.7067,  ..., 0.9232, 0.1449, 0.6848]),
+       col_indices=tensor([16831, 31700, 33476,  ..., 20126, 37524, 56641]),
+       values=tensor([0.4034, 0.0732, 0.2390,  ..., 0.1660, 0.9005, 0.3603]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.0246, 0.8160, 0.3295,  ..., 0.0588, 0.6998, 0.9868])
+tensor([0.3728, 0.2661, 0.0172,  ..., 0.8787, 0.3705, 0.6094])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 0.4573814868927002 seconds
+Time: 0.332211971282959 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 2295 -ss 100000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.306779146194458}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3160 -ss 100000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.255688428878784}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      8,  ..., 499989, 499995,
+tensor(crow_indices=tensor([     0,      1,      7,  ..., 499992, 499996,
                             500000]),
-       col_indices=tensor([ 2059, 19971, 54406,  ..., 65065, 65922, 83323]),
-       values=tensor([0.5530, 0.6181, 0.7781,  ..., 0.5380, 0.6243, 0.8378]),
+       col_indices=tensor([64602,   478, 27899,  ..., 42044, 53218, 73264]),
+       values=tensor([0.8097, 0.5983, 0.2516,  ..., 0.0269, 0.0458, 0.5960]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.4055, 0.5945, 0.9428,  ..., 0.6446, 0.1456, 0.3700])
+tensor([0.5290, 0.0200, 0.8761,  ..., 0.9070, 0.8739, 0.1739])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 7.306779146194458 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3297 -ss 100000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.891435861587524}
+Time: 10.255688428878784 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,     11,  ..., 499995, 499995,
+tensor(crow_indices=tensor([     0,      1,      7,  ..., 499992, 499996,
                             500000]),
-       col_indices=tensor([ 8913, 22689, 49331,  ..., 65321, 72756, 72788]),
-       values=tensor([0.7511, 0.0782, 0.7533,  ..., 0.6341, 0.1803, 0.2288]),
+       col_indices=tensor([64602,   478, 27899,  ..., 42044, 53218, 73264]),
+       values=tensor([0.8097, 0.5983, 0.2516,  ..., 0.0269, 0.0458, 0.5960]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.5601, 0.4293, 0.2285,  ..., 0.5137, 0.5400, 0.5797])
+tensor([0.5290, 0.0200, 0.8761,  ..., 0.9070, 0.8739, 0.1739])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,30 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.891435861587524 seconds
+Time: 10.255688428878784 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,     11,  ..., 499995, 499995,
-                            500000]),
-       col_indices=tensor([ 8913, 22689, 49331,  ..., 65321, 72756, 72788]),
-       values=tensor([0.7511, 0.0782, 0.7533,  ..., 0.6341, 0.1803, 0.2288]),
-       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.5601, 0.4293, 0.2285,  ..., 0.5137, 0.5400, 0.5797])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 500000
-Density: 5e-05
-Time: 10.891435861587524 seconds
-
-[20.84, 20.48, 20.44, 20.64, 20.64, 20.68, 20.72, 20.64, 20.64, 20.92]
-[20.6, 20.48, 20.72, 25.12, 26.88, 32.4, 38.84, 42.28, 46.96, 50.12, 51.2, 51.8, 51.56, 51.6]
-14.66303277015686
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3297, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.891435861587524, 'TIME_S_1KI': 3.3034382352403777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 528.672490940094, 'W': 36.05478479295784}
-[20.84, 20.48, 20.44, 20.64, 20.64, 20.68, 20.72, 20.64, 20.64, 20.92, 20.84, 21.04, 20.84, 20.84, 20.76, 20.8, 20.68, 20.44, 20.28, 20.24]
-371.98
-18.599
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3297, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.891435861587524, 'TIME_S_1KI': 3.3034382352403777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 528.672490940094, 'W': 36.05478479295784, 'J_1KI': 160.34955745832394, 'W_1KI': 10.935633846817664, 'W_D': 17.45578479295784, 'J_D': 255.95474444794658, 'W_D_1KI': 5.2944448871573675, 'J_D_1KI': 1.605837090432929}
+[20.4, 20.4, 20.36, 20.48, 20.48, 20.56, 20.72, 20.68, 20.88, 20.92]
+[20.8, 20.96, 22.0, 23.08, 26.64, 32.64, 32.64, 39.84, 44.88, 50.64, 51.68, 51.72, 51.64]
+13.599660396575928
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3160, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.255688428878784, 'TIME_S_1KI': 3.2454710217970835, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 463.90646287918094, 'W': 34.111621125184975}
+[20.4, 20.4, 20.36, 20.48, 20.48, 20.56, 20.72, 20.68, 20.88, 20.92, 20.52, 20.4, 20.36, 20.68, 20.72, 20.8, 20.88, 20.68, 20.56, 20.96]
+371.03999999999996
+18.552
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3160, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.255688428878784, 'TIME_S_1KI': 3.2454710217970835, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 463.90646287918094, 'W': 34.111621125184975, 'J_1KI': 146.8058426832851, 'W_1KI': 10.794816811767397, 'W_D': 15.559621125184975, 'J_D': 211.6055632019043, 'W_D_1KI': 4.92393073581803, 'J_D_1KI': 1.5582059290563386}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.json
index 6fa3845..ce06cb3 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 32636, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.47010850906372, "TIME_S_1KI": 0.32081469877018387, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 319.63640854835506, "W": 22.48260624860275, "J_1KI": 9.793982367580435, "W_1KI": 0.6888897612637195, "W_D": 3.984606248602752, "J_D": 56.64935891771315, "W_D_1KI": 0.12209235962136145, "J_D_1KI": 0.003741033203252894}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 32993, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.722160577774048, "TIME_S_1KI": 0.32498289266735514, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.13037155151375, "W": 22.887281127943623, "J_1KI": 9.854525855530376, "W_1KI": 0.6937011222969607, "W_D": 4.493281127943625, "J_D": 63.8303062057496, "W_D_1KI": 0.13618892273947883, "J_D_1KI": 0.0041278126493340655}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.output
index 21cdb17..f6eb827 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.050879478454589844}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.04047083854675293}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ...,  9998,  9999, 10000]),
-       col_indices=tensor([5382, 2827, 5658,  ..., 9195, 8647, 1137]),
-       values=tensor([0.6423, 0.5656, 0.8194,  ..., 0.3825, 0.7281, 0.0248]),
+tensor(crow_indices=tensor([    0,     3,     4,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([2549, 9361, 9970,  ...,  704, 4011, 7891]),
+       values=tensor([0.5892, 0.1476, 0.4892,  ..., 0.9338, 0.1639, 0.4664]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.6609, 0.7541, 0.4159,  ..., 0.2180, 0.3481, 0.0053])
+tensor([0.8574, 0.7762, 0.9018,  ..., 0.6074, 0.6936, 0.9938])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 0.050879478454589844 seconds
+Time: 0.04047083854675293 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 20637 -ss 10000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.639445781707764}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 25944 -ss 10000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.256449699401855}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ...,  9998, 10000, 10000]),
-       col_indices=tensor([1538, 6690, 5733,  ..., 9607, 7438, 7782]),
-       values=tensor([0.7222, 0.1089, 0.5631,  ..., 0.3116, 0.0243, 0.6999]),
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998, 10000, 10000]),
+       col_indices=tensor([2764, 9413, 5263,  ..., 1959, 4242, 4549]),
+       values=tensor([0.9684, 0.2656, 0.2250,  ..., 0.3440, 0.8382, 0.0353]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.2878, 0.8940, 0.0961,  ..., 0.0631, 0.2895, 0.2219])
+tensor([0.2298, 0.9239, 0.9999,  ..., 0.9160, 0.4053, 0.1195])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 6.639445781707764 seconds
+Time: 8.256449699401855 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32636 -ss 10000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.47010850906372}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32993 -ss 10000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.722160577774048}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ...,  9999, 10000, 10000]),
-       col_indices=tensor([7014, 8766, 3433,  ..., 9466, 1431, 7728]),
-       values=tensor([0.0370, 0.3747, 0.2051,  ..., 0.2901, 0.3737, 0.7201]),
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9998, 10000]),
+       col_indices=tensor([5965, 6204, 1451,  ..., 9267, 1058, 9600]),
+       values=tensor([0.3937, 0.8327, 0.3110,  ..., 0.9645, 0.7301, 0.2055]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.8451, 0.4833, 0.4298,  ..., 0.9015, 0.0937, 0.6764])
+tensor([0.5302, 0.5425, 0.3526,  ..., 0.3577, 0.4587, 0.7959])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.47010850906372 seconds
+Time: 10.722160577774048 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ...,  9999, 10000, 10000]),
-       col_indices=tensor([7014, 8766, 3433,  ..., 9466, 1431, 7728]),
-       values=tensor([0.0370, 0.3747, 0.2051,  ..., 0.2901, 0.3737, 0.7201]),
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9998, 10000]),
+       col_indices=tensor([5965, 6204, 1451,  ..., 9267, 1058, 9600]),
+       values=tensor([0.3937, 0.8327, 0.3110,  ..., 0.9645, 0.7301, 0.2055]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.8451, 0.4833, 0.4298,  ..., 0.9015, 0.0937, 0.6764])
+tensor([0.5302, 0.5425, 0.3526,  ..., 0.3577, 0.4587, 0.7959])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.47010850906372 seconds
+Time: 10.722160577774048 seconds
 
-[20.64, 20.64, 20.48, 20.48, 20.48, 20.4, 20.32, 20.16, 20.24, 20.4]
-[20.72, 20.64, 21.24, 23.32, 25.32, 26.04, 26.72, 26.72, 26.48, 25.08, 23.72, 23.6, 23.56, 23.48]
-14.217053174972534
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32636, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.47010850906372, 'TIME_S_1KI': 0.32081469877018387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 319.63640854835506, 'W': 22.48260624860275}
-[20.64, 20.64, 20.48, 20.48, 20.48, 20.4, 20.32, 20.16, 20.24, 20.4, 20.64, 20.56, 20.48, 20.52, 20.68, 20.84, 20.88, 20.88, 20.84, 20.48]
-369.96
-18.497999999999998
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32636, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.47010850906372, 'TIME_S_1KI': 0.32081469877018387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 319.63640854835506, 'W': 22.48260624860275, 'J_1KI': 9.793982367580435, 'W_1KI': 0.6888897612637195, 'W_D': 3.984606248602752, 'J_D': 56.64935891771315, 'W_D_1KI': 0.12209235962136145, 'J_D_1KI': 0.003741033203252894}
+[20.24, 20.2, 20.16, 20.24, 20.32, 20.88, 20.84, 20.52, 20.88, 20.36]
+[20.32, 20.56, 20.56, 23.56, 25.76, 28.24, 29.12, 29.28, 25.72, 24.52, 23.72, 23.72, 23.64, 23.48]
+14.20572280883789
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.722160577774048, 'TIME_S_1KI': 0.32498289266735514, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.13037155151375, 'W': 22.887281127943623}
+[20.24, 20.2, 20.16, 20.24, 20.32, 20.88, 20.84, 20.52, 20.88, 20.36, 20.28, 20.32, 20.32, 20.44, 20.4, 20.4, 20.4, 20.32, 20.56, 20.48]
+367.88
+18.394
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.722160577774048, 'TIME_S_1KI': 0.32498289266735514, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.13037155151375, 'W': 22.887281127943623, 'J_1KI': 9.854525855530376, 'W_1KI': 0.6937011222969607, 'W_D': 4.493281127943625, 'J_D': 63.8303062057496, 'W_D_1KI': 0.13618892273947883, 'J_D_1KI': 0.0041278126493340655}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.json
index c0d7ab3..c72c25e 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4519, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.300970077514648, "TIME_S_1KI": 2.279479990598506, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.2094431686402, "W": 23.392743273719635, "J_1KI": 73.51392856132777, "W_1KI": 5.176530930232272, "W_D": 4.8837432737196345, "J_D": 69.35593720483783, "W_D_1KI": 1.0807132714582062, "J_D_1KI": 0.2391487655362262}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4704, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.513959884643555, "TIME_S_1KI": 2.235110519694633, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 329.1826779842377, "W": 23.013408423661655, "J_1KI": 69.9793107959689, "W_1KI": 4.892306212513107, "W_D": 4.506408423661657, "J_D": 64.45944753956805, "W_D_1KI": 0.9579949880233115, "J_D_1KI": 0.2036553971137992}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.output
index f0ce63e..e728837 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2727935314178467}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.28493499755859375}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     18,  ...,  99975,  99993,
+tensor(crow_indices=tensor([     0,      6,     15,  ...,  99974,  99987,
                             100000]),
-       col_indices=tensor([2872, 4034, 5620,  ..., 6357, 6556, 9590]),
-       values=tensor([0.7995, 0.0045, 0.2448,  ..., 0.5761, 0.7842, 0.1546]),
+       col_indices=tensor([1008, 1745, 2458,  ..., 8180, 8309, 8725]),
+       values=tensor([0.4541, 0.1167, 0.6157,  ..., 0.2339, 0.7280, 0.6670]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8077, 0.7130, 0.7281,  ..., 0.3829, 0.9486, 0.9162])
+tensor([0.9535, 0.6938, 0.6793,  ..., 0.3504, 0.5915, 0.1345])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 0.2727935314178467 seconds
+Time: 0.28493499755859375 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3849 -ss 10000 -sd 0.001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.942286252975464}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3685 -ss 10000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.224288940429688}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     12,     21,  ...,  99991,  99998,
+tensor(crow_indices=tensor([     0,      8,     23,  ...,  99975,  99993,
                             100000]),
-       col_indices=tensor([ 425,  574,  695,  ..., 9570, 6024, 9715]),
-       values=tensor([0.7410, 0.8879, 0.5840,  ..., 0.6995, 0.9280, 0.9465]),
+       col_indices=tensor([2342, 2426, 3411,  ..., 3261, 4460, 9472]),
+       values=tensor([0.8447, 0.2534, 0.2074,  ..., 0.5724, 0.1389, 0.7449]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2929, 0.5164, 0.5482,  ..., 0.5103, 0.5008, 0.9557])
+tensor([0.2993, 0.9441, 0.0750,  ..., 0.0171, 0.8286, 0.1160])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 8.942286252975464 seconds
+Time: 8.224288940429688 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4519 -ss 10000 -sd 0.001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.300970077514648}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4704 -ss 10000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.513959884643555}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,     16,  ...,  99974,  99990,
+tensor(crow_indices=tensor([     0,      7,     18,  ...,  99977,  99987,
                             100000]),
-       col_indices=tensor([ 582, 1691, 2515,  ..., 7345, 7996, 8295]),
-       values=tensor([0.8177, 0.9283, 0.6030,  ..., 0.2647, 0.3717, 0.8633]),
+       col_indices=tensor([  68, 2486, 2822,  ..., 8793, 8847, 9684]),
+       values=tensor([0.4423, 0.5768, 0.9908,  ..., 0.4103, 0.7568, 0.2801]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2209, 0.7260, 0.0429,  ..., 0.4887, 0.7834, 0.0043])
+tensor([0.3663, 0.2060, 0.8473,  ..., 0.8925, 0.9991, 0.3035])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.300970077514648 seconds
+Time: 10.513959884643555 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,     16,  ...,  99974,  99990,
+tensor(crow_indices=tensor([     0,      7,     18,  ...,  99977,  99987,
                             100000]),
-       col_indices=tensor([ 582, 1691, 2515,  ..., 7345, 7996, 8295]),
-       values=tensor([0.8177, 0.9283, 0.6030,  ..., 0.2647, 0.3717, 0.8633]),
+       col_indices=tensor([  68, 2486, 2822,  ..., 8793, 8847, 9684]),
+       values=tensor([0.4423, 0.5768, 0.9908,  ..., 0.4103, 0.7568, 0.2801]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2209, 0.7260, 0.0429,  ..., 0.4887, 0.7834, 0.0043])
+tensor([0.3663, 0.2060, 0.8473,  ..., 0.8925, 0.9991, 0.3035])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.300970077514648 seconds
+Time: 10.513959884643555 seconds
 
-[20.48, 20.64, 20.44, 20.28, 20.28, 20.6, 20.72, 20.68, 20.68, 20.48]
-[20.4, 20.36, 23.16, 24.08, 27.0, 27.6, 28.72, 28.72, 26.24, 26.28, 24.44, 24.28, 24.24, 24.08]
-14.201388835906982
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.300970077514648, 'TIME_S_1KI': 2.279479990598506, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.2094431686402, 'W': 23.392743273719635}
-[20.48, 20.64, 20.44, 20.28, 20.28, 20.6, 20.72, 20.68, 20.68, 20.48, 20.52, 20.6, 20.68, 20.64, 20.64, 20.52, 20.52, 20.48, 20.68, 20.72]
-370.18
-18.509
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.300970077514648, 'TIME_S_1KI': 2.279479990598506, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.2094431686402, 'W': 23.392743273719635, 'J_1KI': 73.51392856132777, 'W_1KI': 5.176530930232272, 'W_D': 4.8837432737196345, 'J_D': 69.35593720483783, 'W_D_1KI': 1.0807132714582062, 'J_D_1KI': 0.2391487655362262}
+[20.44, 20.44, 20.44, 20.4, 20.24, 20.32, 20.48, 20.48, 20.52, 20.68]
+[20.64, 20.56, 20.48, 24.2, 26.08, 28.48, 29.28, 27.2, 26.72, 24.2, 24.2, 24.12, 24.08, 23.96]
+14.303951501846313
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4704, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.513959884643555, 'TIME_S_1KI': 2.235110519694633, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.1826779842377, 'W': 23.013408423661655}
+[20.44, 20.44, 20.44, 20.4, 20.24, 20.32, 20.48, 20.48, 20.52, 20.68, 20.24, 20.44, 20.28, 20.72, 20.96, 21.08, 20.96, 21.0, 20.48, 20.44]
+370.14
+18.506999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4704, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.513959884643555, 'TIME_S_1KI': 2.235110519694633, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.1826779842377, 'W': 23.013408423661655, 'J_1KI': 69.9793107959689, 'W_1KI': 4.892306212513107, 'W_D': 4.506408423661657, 'J_D': 64.45944753956805, 'W_D_1KI': 0.9579949880233115, 'J_D_1KI': 0.2036553971137992}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.json
index 6d63757..5526a0d 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 490, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.49430251121521, "TIME_S_1KI": 21.416943900439204, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 322.6961988353729, "W": 22.678695903983957, "J_1KI": 658.5636710925977, "W_1KI": 46.283052865273376, "W_D": 4.3196959039839555, "J_D": 61.46515012335775, "W_D_1KI": 8.815705926497868, "J_D_1KI": 17.991236584689528}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 466, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.031994819641113, "TIME_S_1KI": 21.527885879058182, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.8022685432434, "W": 22.86777901563946, "J_1KI": 699.1464990198356, "W_1KI": 49.07248715802459, "W_D": 4.6127790156394575, "J_D": 65.71927542924874, "W_D_1KI": 9.898667415535316, "J_D_1KI": 21.241775569818273}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.output
index 8697ed9..89008b5 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.01 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.140977382659912}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.2486960887908936}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     103,     212,  ...,  999798,
-                             999901, 1000000]),
-       col_indices=tensor([  63,  140,  146,  ..., 9691, 9771, 9918]),
-       values=tensor([0.8748, 0.2571, 0.8906,  ..., 0.1504, 0.2890, 0.7825]),
+tensor(crow_indices=tensor([      0,      77,     175,  ...,  999811,
+                             999910, 1000000]),
+       col_indices=tensor([  35,  141,  347,  ..., 9617, 9713, 9775]),
+       values=tensor([0.5684, 0.4118, 0.8956,  ..., 0.8300, 0.5668, 0.8186]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5882, 0.3416, 0.1892,  ..., 0.3016, 0.5220, 0.0626])
+tensor([0.7862, 0.9671, 0.1334,  ..., 0.0132, 0.8938, 0.7920])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 2.140977382659912 seconds
+Time: 2.2486960887908936 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 490 -ss 10000 -sd 0.01 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.49430251121521}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 466 -ss 10000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.031994819641113}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     113,     202,  ...,  999820,
-                             999916, 1000000]),
-       col_indices=tensor([   2,   35,   39,  ..., 9519, 9605, 9656]),
-       values=tensor([0.0992, 0.7724, 0.9238,  ..., 0.3639, 0.9758, 0.0697]),
+tensor(crow_indices=tensor([      0,     101,     225,  ...,  999830,
+                             999902, 1000000]),
+       col_indices=tensor([ 154,  165,  205,  ..., 9812, 9815, 9915]),
+       values=tensor([0.8739, 0.8341, 0.5427,  ..., 0.3042, 0.6360, 0.3675]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2199, 0.1288, 0.7757,  ..., 0.1449, 0.2950, 0.2928])
+tensor([0.1667, 0.6012, 0.2305,  ..., 0.2181, 0.2842, 0.9004])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.49430251121521 seconds
+Time: 10.031994819641113 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     113,     202,  ...,  999820,
-                             999916, 1000000]),
-       col_indices=tensor([   2,   35,   39,  ..., 9519, 9605, 9656]),
-       values=tensor([0.0992, 0.7724, 0.9238,  ..., 0.3639, 0.9758, 0.0697]),
+tensor(crow_indices=tensor([      0,     101,     225,  ...,  999830,
+                             999902, 1000000]),
+       col_indices=tensor([ 154,  165,  205,  ..., 9812, 9815, 9915]),
+       values=tensor([0.8739, 0.8341, 0.5427,  ..., 0.3042, 0.6360, 0.3675]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2199, 0.1288, 0.7757,  ..., 0.1449, 0.2950, 0.2928])
+tensor([0.1667, 0.6012, 0.2305,  ..., 0.2181, 0.2842, 0.9004])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.49430251121521 seconds
+Time: 10.031994819641113 seconds
 
-[20.68, 20.72, 20.56, 20.52, 20.48, 20.2, 19.92, 19.96, 19.92, 19.92]
-[19.96, 20.36, 20.52, 22.04, 24.08, 26.92, 27.96, 27.96, 26.64, 25.04, 24.52, 24.4, 24.4, 24.52]
-14.229045629501343
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 490, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.49430251121521, 'TIME_S_1KI': 21.416943900439204, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.6961988353729, 'W': 22.678695903983957}
-[20.68, 20.72, 20.56, 20.52, 20.48, 20.2, 19.92, 19.96, 19.92, 19.92, 20.44, 20.36, 20.4, 20.52, 20.4, 20.64, 20.76, 20.52, 20.52, 20.52]
-367.18
-18.359
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 490, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.49430251121521, 'TIME_S_1KI': 21.416943900439204, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.6961988353729, 'W': 22.678695903983957, 'J_1KI': 658.5636710925977, 'W_1KI': 46.283052865273376, 'W_D': 4.3196959039839555, 'J_D': 61.46515012335775, 'W_D_1KI': 8.815705926497868, 'J_D_1KI': 17.991236584689528}
+[20.0, 20.04, 20.24, 20.28, 20.28, 20.28, 20.44, 20.2, 20.12, 20.16]
+[20.16, 20.4, 20.28, 22.2, 24.0, 27.88, 28.52, 28.36, 27.2, 25.84, 24.16, 24.32, 24.36, 24.36]
+14.247219562530518
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 466, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.031994819641113, 'TIME_S_1KI': 21.527885879058182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.8022685432434, 'W': 22.86777901563946}
+[20.0, 20.04, 20.24, 20.28, 20.28, 20.28, 20.44, 20.2, 20.12, 20.16, 20.4, 20.44, 20.32, 20.44, 20.56, 20.32, 20.4, 20.36, 20.08, 20.04]
+365.1
+18.255000000000003
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 466, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.031994819641113, 'TIME_S_1KI': 21.527885879058182, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.8022685432434, 'W': 22.86777901563946, 'J_1KI': 699.1464990198356, 'W_1KI': 49.07248715802459, 'W_D': 4.6127790156394575, 'J_D': 65.71927542924874, 'W_D_1KI': 9.898667415535316, 'J_D_1KI': 21.241775569818273}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.json
index d53721b..e3b0887 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.575754880905151, "TIME_S_1KI": 105.75754880905151, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 357.48722974777223, "W": 23.357767084816274, "J_1KI": 3574.8722974777224, "W_1KI": 233.57767084816274, "W_D": 4.891767084816273, "J_D": 74.86778412389756, "W_D_1KI": 48.91767084816273, "J_D_1KI": 489.1767084816273}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.582204103469849, "TIME_S_1KI": 105.82204103469849, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 394.3855400466919, "W": 24.027936546671576, "J_1KI": 3943.8554004669195, "W_1KI": 240.27936546671577, "W_D": 5.732936546671578, "J_D": 94.09827063679698, "W_D_1KI": 57.32936546671578, "J_D_1KI": 573.2936546671577}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.output
index 5bc40f5..9eadab5 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.575754880905151}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.582204103469849}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     505,    1011,  ..., 4998999,
-                            4999505, 5000000]),
-       col_indices=tensor([  17,   34,   93,  ..., 9927, 9945, 9977]),
-       values=tensor([0.3942, 0.9668, 0.2842,  ..., 0.3748, 0.5474, 0.6270]),
+tensor(crow_indices=tensor([      0,     506,    1036,  ..., 4999012,
+                            4999522, 5000000]),
+       col_indices=tensor([   2,    6,   14,  ..., 9953, 9962, 9983]),
+       values=tensor([0.7970, 0.3700, 0.8324,  ..., 0.2223, 0.8075, 0.1339]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.3102, 0.7326, 0.1847,  ..., 0.2267, 0.2009, 0.0941])
+tensor([0.3881, 0.8882, 0.8978,  ..., 0.9188, 0.6267, 0.4542])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.575754880905151 seconds
+Time: 10.582204103469849 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     505,    1011,  ..., 4998999,
-                            4999505, 5000000]),
-       col_indices=tensor([  17,   34,   93,  ..., 9927, 9945, 9977]),
-       values=tensor([0.3942, 0.9668, 0.2842,  ..., 0.3748, 0.5474, 0.6270]),
+tensor(crow_indices=tensor([      0,     506,    1036,  ..., 4999012,
+                            4999522, 5000000]),
+       col_indices=tensor([   2,    6,   14,  ..., 9953, 9962, 9983]),
+       values=tensor([0.7970, 0.3700, 0.8324,  ..., 0.2223, 0.8075, 0.1339]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.3102, 0.7326, 0.1847,  ..., 0.2267, 0.2009, 0.0941])
+tensor([0.3881, 0.8882, 0.8978,  ..., 0.9188, 0.6267, 0.4542])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.575754880905151 seconds
+Time: 10.582204103469849 seconds
 
-[20.44, 20.44, 20.44, 20.52, 20.44, 20.4, 20.32, 20.2, 20.08, 20.32]
-[20.32, 20.16, 21.08, 22.56, 24.44, 27.6, 27.6, 29.28, 28.88, 28.12, 25.16, 24.16, 24.12, 24.4, 24.56]
-15.30485463142395
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.575754880905151, 'TIME_S_1KI': 105.75754880905151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.48722974777223, 'W': 23.357767084816274}
-[20.44, 20.44, 20.44, 20.52, 20.44, 20.4, 20.32, 20.2, 20.08, 20.32, 20.52, 20.56, 20.76, 20.76, 20.84, 20.84, 20.72, 20.56, 20.56, 20.48]
-369.32
-18.466
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.575754880905151, 'TIME_S_1KI': 105.75754880905151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.48722974777223, 'W': 23.357767084816274, 'J_1KI': 3574.8722974777224, 'W_1KI': 233.57767084816274, 'W_D': 4.891767084816273, 'J_D': 74.86778412389756, 'W_D_1KI': 48.91767084816273, 'J_D_1KI': 489.1767084816273}
+[20.0, 20.2, 20.08, 20.2, 20.2, 20.12, 20.4, 20.28, 20.44, 20.72]
+[20.6, 20.72, 21.08, 24.84, 27.04, 29.48, 31.2, 28.84, 27.88, 27.88, 26.0, 23.84, 24.0, 24.32, 24.6, 24.48]
+16.41362500190735
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.582204103469849, 'TIME_S_1KI': 105.82204103469849, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 394.3855400466919, 'W': 24.027936546671576}
+[20.0, 20.2, 20.08, 20.2, 20.2, 20.12, 20.4, 20.28, 20.44, 20.72, 20.56, 20.44, 20.2, 20.24, 20.08, 20.28, 20.68, 20.68, 20.44, 20.6]
+365.9
+18.294999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.582204103469849, 'TIME_S_1KI': 105.82204103469849, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 394.3855400466919, 'W': 24.027936546671576, 'J_1KI': 3943.8554004669195, 'W_1KI': 240.27936546671577, 'W_D': 5.732936546671578, 'J_D': 94.09827063679698, 'W_D_1KI': 57.32936546671578, 'J_D_1KI': 573.2936546671577}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.json
index d4908f3..33e4608 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.58586049079895, "TIME_S_1KI": 215.8586049079895, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 695.2596657943726, "W": 24.42843676698333, "J_1KI": 6952.596657943726, "W_1KI": 244.2843676698333, "W_D": 6.1854367669833294, "J_D": 176.04420374608034, "W_D_1KI": 61.854367669833294, "J_D_1KI": 618.543676698333}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.590675115585327, "TIME_S_1KI": 215.90675115585327, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 698.2714468479157, "W": 24.533351073497297, "J_1KI": 6982.714468479157, "W_1KI": 245.33351073497298, "W_D": 6.067351073497296, "J_D": 172.68973977231983, "W_D_1KI": 60.67351073497296, "J_D_1KI": 606.7351073497296}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.output
index b9e17f1..3058f8c 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.1 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.58586049079895}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.590675115585327}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      983,     1945,  ...,  9997995,
-                             9998975, 10000000]),
-       col_indices=tensor([   7,   29,   32,  ..., 9986, 9994, 9999]),
-       values=tensor([0.5805, 0.0545, 0.7779,  ..., 0.8799, 0.6314, 0.5149]),
+tensor(crow_indices=tensor([       0,      953,     1990,  ...,  9998028,
+                             9999002, 10000000]),
+       col_indices=tensor([   3,    5,   31,  ..., 9963, 9981, 9996]),
+       values=tensor([0.0177, 0.1736, 0.9045,  ..., 0.8807, 0.7948, 0.3225]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1246, 0.2739, 0.0084,  ..., 0.7975, 0.3318, 0.0977])
+tensor([0.5695, 0.4055, 0.5858,  ..., 0.6808, 0.9483, 0.6795])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 21.58586049079895 seconds
+Time: 21.590675115585327 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      983,     1945,  ...,  9997995,
-                             9998975, 10000000]),
-       col_indices=tensor([   7,   29,   32,  ..., 9986, 9994, 9999]),
-       values=tensor([0.5805, 0.0545, 0.7779,  ..., 0.8799, 0.6314, 0.5149]),
+tensor(crow_indices=tensor([       0,      953,     1990,  ...,  9998028,
+                             9999002, 10000000]),
+       col_indices=tensor([   3,    5,   31,  ..., 9963, 9981, 9996]),
+       values=tensor([0.0177, 0.1736, 0.9045,  ..., 0.8807, 0.7948, 0.3225]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1246, 0.2739, 0.0084,  ..., 0.7975, 0.3318, 0.0977])
+tensor([0.5695, 0.4055, 0.5858,  ..., 0.6808, 0.9483, 0.6795])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 21.58586049079895 seconds
+Time: 21.590675115585327 seconds
 
-[20.44, 20.24, 20.32, 20.24, 20.16, 20.52, 20.56, 20.68, 20.88, 21.04]
-[20.8, 20.6, 23.72, 23.72, 26.04, 27.56, 30.88, 32.68, 29.8, 29.08, 27.52, 26.28, 24.44, 24.48, 24.48, 24.2, 24.2, 24.2, 24.12, 24.12, 24.28, 24.28, 24.4, 24.32, 24.16, 24.0, 24.08, 24.16]
-28.461078882217407
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.58586049079895, 'TIME_S_1KI': 215.8586049079895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 695.2596657943726, 'W': 24.42843676698333}
-[20.44, 20.24, 20.32, 20.24, 20.16, 20.52, 20.56, 20.68, 20.88, 21.04, 19.8, 19.8, 19.8, 19.96, 20.0, 20.08, 20.12, 20.36, 20.32, 20.36]
-364.86
-18.243000000000002
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.58586049079895, 'TIME_S_1KI': 215.8586049079895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 695.2596657943726, 'W': 24.42843676698333, 'J_1KI': 6952.596657943726, 'W_1KI': 244.2843676698333, 'W_D': 6.1854367669833294, 'J_D': 176.04420374608034, 'W_D_1KI': 61.854367669833294, 'J_D_1KI': 618.543676698333}
+[20.76, 20.76, 20.76, 20.76, 20.48, 20.8, 20.84, 20.8, 20.76, 20.6]
+[20.24, 20.28, 23.72, 25.04, 26.96, 30.6, 31.88, 31.88, 29.52, 29.44, 27.04, 25.04, 23.8, 23.76, 24.04, 24.2, 24.36, 24.36, 24.28, 24.28, 24.28, 24.2, 24.12, 24.32, 24.28, 24.52, 24.36, 24.52]
+28.462130784988403
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.590675115585327, 'TIME_S_1KI': 215.90675115585327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 698.2714468479157, 'W': 24.533351073497297}
+[20.76, 20.76, 20.76, 20.76, 20.48, 20.8, 20.84, 20.8, 20.76, 20.6, 20.52, 20.48, 20.64, 20.56, 20.36, 20.36, 20.0, 20.12, 19.88, 20.04]
+369.32000000000005
+18.466
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.590675115585327, 'TIME_S_1KI': 215.90675115585327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 698.2714468479157, 'W': 24.533351073497297, 'J_1KI': 6982.714468479157, 'W_1KI': 245.33351073497298, 'W_D': 6.067351073497296, 'J_D': 172.68973977231983, 'W_D_1KI': 60.67351073497296, 'J_D_1KI': 606.7351073497296}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.json
index 00303c3..4bc8860 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.006776571273804, "TIME_S_1KI": 420.06776571273804, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1323.3857918548583, "W": 24.65839535462019, "J_1KI": 13233.857918548583, "W_1KI": 246.58395354620188, "W_D": 6.256395354620192, "J_D": 335.77305422592167, "W_D_1KI": 62.56395354620192, "J_D_1KI": 625.6395354620192}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.53456234931946, "TIME_S_1KI": 425.3456234931946, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1258.7017677497863, "W": 24.331627673949086, "J_1KI": 12587.017677497863, "W_1KI": 243.31627673949086, "W_D": 5.892627673949086, "J_D": 304.83208806586254, "W_D_1KI": 58.92627673949086, "J_D_1KI": 589.2627673949086}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.output
index 7c8bead..d93254b 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.2 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.006776571273804}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.53456234931946}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1950,     3929,  ..., 19995954,
-                            19997973, 20000000]),
-       col_indices=tensor([   0,   10,   17,  ..., 9977, 9980, 9990]),
-       values=tensor([0.1470, 0.8510, 0.9446,  ..., 0.3735, 0.6466, 0.3885]),
+tensor(crow_indices=tensor([       0,     1981,     3961,  ..., 19996043,
+                            19998038, 20000000]),
+       col_indices=tensor([   1,    3,    6,  ..., 9979, 9991, 9993]),
+       values=tensor([0.0058, 0.7086, 0.6623,  ..., 0.9502, 0.1257, 0.5097]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.1499, 0.7404, 0.4886,  ..., 0.1182, 0.4158, 0.3615])
+tensor([0.3594, 0.8224, 0.5071,  ..., 0.7554, 0.0445, 0.2812])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 42.006776571273804 seconds
+Time: 42.53456234931946 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1950,     3929,  ..., 19995954,
-                            19997973, 20000000]),
-       col_indices=tensor([   0,   10,   17,  ..., 9977, 9980, 9990]),
-       values=tensor([0.1470, 0.8510, 0.9446,  ..., 0.3735, 0.6466, 0.3885]),
+tensor(crow_indices=tensor([       0,     1981,     3961,  ..., 19996043,
+                            19998038, 20000000]),
+       col_indices=tensor([   1,    3,    6,  ..., 9979, 9991, 9993]),
+       values=tensor([0.0058, 0.7086, 0.6623,  ..., 0.9502, 0.1257, 0.5097]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.1499, 0.7404, 0.4886,  ..., 0.1182, 0.4158, 0.3615])
+tensor([0.3594, 0.8224, 0.5071,  ..., 0.7554, 0.0445, 0.2812])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 42.006776571273804 seconds
+Time: 42.53456234931946 seconds
 
-[20.44, 20.28, 20.36, 20.28, 20.32, 20.16, 20.32, 20.32, 20.52, 20.56]
-[20.6, 20.56, 20.76, 24.64, 26.0, 26.92, 30.76, 29.04, 28.68, 29.52, 30.24, 30.24, 27.76, 27.48, 26.4, 24.64, 24.48, 24.32, 24.28, 24.28, 24.28, 24.08, 24.28, 24.28, 24.36, 24.2, 24.24, 24.6, 24.64, 24.52, 24.72, 24.48, 24.64, 24.4, 24.52, 24.52, 24.36, 24.4, 24.4, 24.4, 24.48, 24.68, 24.76, 24.56, 24.36, 24.16, 24.24, 24.4, 24.4, 24.76, 24.88, 24.96, 24.92]
-53.668771743774414
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.006776571273804, 'TIME_S_1KI': 420.06776571273804, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1323.3857918548583, 'W': 24.65839535462019}
-[20.44, 20.28, 20.36, 20.28, 20.32, 20.16, 20.32, 20.32, 20.52, 20.56, 20.12, 19.92, 20.0, 20.28, 20.44, 20.72, 20.96, 21.04, 21.04, 21.04]
-368.03999999999996
-18.401999999999997
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.006776571273804, 'TIME_S_1KI': 420.06776571273804, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1323.3857918548583, 'W': 24.65839535462019, 'J_1KI': 13233.857918548583, 'W_1KI': 246.58395354620188, 'W_D': 6.256395354620192, 'J_D': 335.77305422592167, 'W_D_1KI': 62.56395354620192, 'J_D_1KI': 625.6395354620192}
+[20.48, 20.48, 20.28, 20.32, 20.32, 20.64, 20.72, 20.8, 20.8, 20.6]
+[20.68, 20.52, 20.64, 22.6, 23.52, 25.2, 28.36, 29.8, 29.56, 30.0, 31.0, 27.24, 27.24, 27.16, 25.84, 24.08, 24.08, 24.32, 24.32, 24.2, 24.04, 24.08, 24.12, 24.16, 24.16, 24.28, 24.44, 24.36, 24.12, 23.96, 23.8, 24.08, 24.2, 24.32, 24.56, 24.56, 24.32, 24.32, 24.4, 24.28, 24.48, 24.4, 24.4, 24.36, 24.6, 24.52, 24.4, 24.52, 24.36, 24.24, 24.24]
+51.731096029281616
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.53456234931946, 'TIME_S_1KI': 425.3456234931946, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1258.7017677497863, 'W': 24.331627673949086}
+[20.48, 20.48, 20.28, 20.32, 20.32, 20.64, 20.72, 20.8, 20.8, 20.6, 19.76, 19.96, 20.32, 20.4, 20.48, 20.2, 20.28, 20.84, 20.88, 21.28]
+368.78
+18.439
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.53456234931946, 'TIME_S_1KI': 425.3456234931946, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1258.7017677497863, 'W': 24.331627673949086, 'J_1KI': 12587.017677497863, 'W_1KI': 243.31627673949086, 'W_D': 5.892627673949086, 'J_D': 304.83208806586254, 'W_D_1KI': 58.92627673949086, 'J_D_1KI': 589.2627673949086}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json
index efd7bc8..a851bf2 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 67.20304369926453, "TIME_S_1KI": 672.0304369926453, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1846.4787199783327, "W": 24.29422238106021, "J_1KI": 18464.787199783328, "W_1KI": 242.9422238106021, "W_D": 5.818222381060206, "J_D": 442.2131174325942, "W_D_1KI": 58.18222381060206, "J_D_1KI": 581.8222381060207}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 63.5993595123291, "TIME_S_1KI": 635.993595123291, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1830.1276798915862, "W": 24.37217906347355, "J_1KI": 18301.27679891586, "W_1KI": 243.72179063473547, "W_D": 6.110179063473549, "J_D": 458.81854897069934, "W_D_1KI": 61.10179063473549, "J_D_1KI": 611.0179063473548}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output
index f1c9a57..8e00d2d 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.3 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 67.20304369926453}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 63.5993595123291}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2926,     5920,  ..., 29993999,
-                            29997022, 30000000]),
-       col_indices=tensor([   1,    4,    6,  ..., 9978, 9982, 9992]),
-       values=tensor([0.3929, 0.6592, 0.7367,  ..., 0.3321, 0.3012, 0.1502]),
+tensor(crow_indices=tensor([       0,     3020,     6019,  ..., 29993935,
+                            29996918, 30000000]),
+       col_indices=tensor([   3,   10,   12,  ..., 9996, 9998, 9999]),
+       values=tensor([0.4272, 0.9443, 0.2889,  ..., 0.0892, 0.8844, 0.9121]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.6782, 0.5388, 0.0901,  ..., 0.7339, 0.4235, 0.1483])
+tensor([0.7267, 0.4804, 0.8266,  ..., 0.7945, 0.0876, 0.7736])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 67.20304369926453 seconds
+Time: 63.5993595123291 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2926,     5920,  ..., 29993999,
-                            29997022, 30000000]),
-       col_indices=tensor([   1,    4,    6,  ..., 9978, 9982, 9992]),
-       values=tensor([0.3929, 0.6592, 0.7367,  ..., 0.3321, 0.3012, 0.1502]),
+tensor(crow_indices=tensor([       0,     3020,     6019,  ..., 29993935,
+                            29996918, 30000000]),
+       col_indices=tensor([   3,   10,   12,  ..., 9996, 9998, 9999]),
+       values=tensor([0.4272, 0.9443, 0.2889,  ..., 0.0892, 0.8844, 0.9121]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.6782, 0.5388, 0.0901,  ..., 0.7339, 0.4235, 0.1483])
+tensor([0.7267, 0.4804, 0.8266,  ..., 0.7945, 0.0876, 0.7736])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 67.20304369926453 seconds
+Time: 63.5993595123291 seconds
 
-[20.6, 20.8, 20.76, 20.64, 20.32, 20.48, 20.48, 20.52, 20.8, 20.6]
-[20.64, 20.6, 20.6, 21.32, 22.6, 24.24, 25.68, 28.84, 30.4, 30.64, 31.4, 31.32, 28.4, 28.16, 27.72, 26.8, 26.8, 25.96, 24.6, 24.4, 24.12, 24.12, 24.12, 24.08, 24.36, 24.56, 24.8, 24.88, 24.88, 24.92, 24.92, 24.72, 24.64, 24.44, 24.28, 24.52, 24.68, 24.64, 24.68, 24.64, 24.64, 24.52, 24.76, 24.84, 24.68, 24.72, 24.68, 24.68, 24.76, 24.68, 24.52, 24.2, 24.12, 24.12, 24.24, 24.48, 24.64, 24.76, 24.76, 24.52, 24.28, 24.32, 24.12, 24.04, 24.32, 24.32, 24.6, 24.52, 24.76, 24.72, 24.36, 24.12, 24.16, 24.36, 24.48]
-76.0048496723175
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 67.20304369926453, 'TIME_S_1KI': 672.0304369926453, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1846.4787199783327, 'W': 24.29422238106021}
-[20.6, 20.8, 20.76, 20.64, 20.32, 20.48, 20.48, 20.52, 20.8, 20.6, 20.04, 20.16, 20.28, 20.16, 20.16, 20.64, 20.72, 20.72, 20.92, 20.68]
-369.52000000000004
-18.476000000000003
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 67.20304369926453, 'TIME_S_1KI': 672.0304369926453, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1846.4787199783327, 'W': 24.29422238106021, 'J_1KI': 18464.787199783328, 'W_1KI': 242.9422238106021, 'W_D': 5.818222381060206, 'J_D': 442.2131174325942, 'W_D_1KI': 58.18222381060206, 'J_D_1KI': 581.8222381060207}
+[20.12, 20.36, 20.28, 20.24, 20.4, 20.44, 20.32, 20.4, 20.48, 20.56]
+[20.56, 20.48, 20.76, 22.32, 24.68, 26.0, 27.64, 30.32, 30.48, 30.52, 31.08, 31.48, 29.28, 29.36, 29.36, 28.84, 27.6, 26.24, 24.72, 24.8, 24.56, 24.56, 24.52, 24.24, 24.12, 24.36, 24.52, 24.52, 24.36, 24.48, 24.12, 23.72, 23.84, 24.24, 24.16, 24.24, 24.16, 24.2, 24.0, 24.0, 24.16, 24.32, 24.36, 24.48, 24.4, 24.32, 24.28, 24.16, 24.2, 24.08, 24.28, 24.36, 24.36, 24.4, 24.2, 24.28, 24.16, 24.28, 24.4, 24.48, 24.68, 24.72, 24.72, 24.56, 24.56, 24.48, 24.64, 24.44, 24.4, 24.44, 24.28, 24.24, 24.36, 24.52]
+75.09085154533386
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 63.5993595123291, 'TIME_S_1KI': 635.993595123291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1830.1276798915862, 'W': 24.37217906347355}
+[20.12, 20.36, 20.28, 20.24, 20.4, 20.44, 20.32, 20.4, 20.48, 20.56, 20.2, 20.2, 20.36, 20.16, 20.08, 20.08, 20.12, 20.24, 20.4, 20.48]
+365.24
+18.262
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 63.5993595123291, 'TIME_S_1KI': 635.993595123291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1830.1276798915862, 'W': 24.37217906347355, 'J_1KI': 18301.27679891586, 'W_1KI': 243.72179063473547, 'W_D': 6.110179063473549, 'J_D': 458.81854897069934, 'W_D_1KI': 61.10179063473549, 'J_D_1KI': 611.0179063473548}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..8143691
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 83.99711680412292, "TIME_S_1KI": 839.9711680412292, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2478.5038306999204, "W": 24.954187412594408, "J_1KI": 24785.038306999206, "W_1KI": 249.54187412594408, "W_D": 6.705187412594405, "J_D": 665.9737066528793, "W_D_1KI": 67.05187412594405, "J_D_1KI": 670.5187412594405}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..0e62c4b
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.4.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.4 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 83.99711680412292}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3989,     7950,  ..., 39991913,
+                            39995922, 40000000]),
+       col_indices=tensor([   0,    1,    6,  ..., 9996, 9997, 9999]),
+       values=tensor([0.4556, 0.9367, 0.5980,  ..., 0.0177, 0.8725, 0.2828]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.5334, 0.8652, 0.9709,  ..., 0.8156, 0.3004, 0.2949])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 83.99711680412292 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3989,     7950,  ..., 39991913,
+                            39995922, 40000000]),
+       col_indices=tensor([   0,    1,    6,  ..., 9996, 9997, 9999]),
+       values=tensor([0.4556, 0.9367, 0.5980,  ..., 0.0177, 0.8725, 0.2828]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.5334, 0.8652, 0.9709,  ..., 0.8156, 0.3004, 0.2949])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 83.99711680412292 seconds
+
+[20.2, 20.04, 20.04, 20.04, 20.28, 20.2, 20.28, 20.64, 20.48, 20.64]
+[20.92, 20.76, 21.2, 24.8, 26.64, 27.52, 29.04, 30.4, 30.4, 31.44, 31.2, 31.8, 30.56, 27.28, 27.84, 27.44, 28.2, 27.6, 26.92, 25.92, 25.92, 24.76, 24.68, 24.72, 24.84, 24.84, 24.92, 25.08, 25.16, 25.12, 25.12, 24.8, 24.68, 24.68, 24.68, 24.76, 24.92, 24.84, 24.8, 25.0, 25.08, 25.08, 25.48, 25.56, 25.56, 25.56, 25.32, 25.2, 24.96, 24.6, 24.72, 24.72, 24.76, 25.04, 25.04, 24.96, 24.8, 24.72, 24.72, 24.6, 24.64, 24.8, 25.04, 24.96, 25.04, 25.08, 24.88, 24.68, 24.76, 24.8, 24.8, 24.88, 24.92, 25.12, 25.08, 25.16, 25.36, 25.28, 25.32, 25.4, 25.2, 24.96, 24.96, 24.96, 24.8, 25.0, 25.04, 25.0, 24.92, 24.88, 24.84, 24.8, 24.6, 24.44, 24.48, 24.48, 24.6, 24.72]
+99.32216143608093
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 83.99711680412292, 'TIME_S_1KI': 839.9711680412292, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2478.5038306999204, 'W': 24.954187412594408}
+[20.2, 20.04, 20.04, 20.04, 20.28, 20.2, 20.28, 20.64, 20.48, 20.64, 20.4, 20.44, 20.36, 20.32, 20.24, 20.24, 20.24, 20.16, 20.16, 20.4]
+364.98
+18.249000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 83.99711680412292, 'TIME_S_1KI': 839.9711680412292, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2478.5038306999204, 'W': 24.954187412594408, 'J_1KI': 24785.038306999206, 'W_1KI': 249.54187412594408, 'W_D': 6.705187412594405, 'J_D': 665.9737066528793, 'W_D_1KI': 67.05187412594405, 'J_D_1KI': 670.5187412594405}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..d4aa07e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 122.3178551197052, "TIME_S_1KI": 1223.178551197052, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3064.272414226532, "W": 24.76047197717133, "J_1KI": 30642.72414226532, "W_1KI": 247.6047197717133, "W_D": 6.469471977171327, "J_D": 800.6400093073842, "W_D_1KI": 64.69471977171328, "J_D_1KI": 646.9471977171328}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..e82eda2
--- /dev/null
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_0.5.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.5 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 122.3178551197052}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4994,    10005,  ..., 49990059,
+                            49994955, 50000000]),
+       col_indices=tensor([   0,    1,    3,  ..., 9993, 9994, 9997]),
+       values=tensor([0.7680, 0.8457, 0.0556,  ..., 0.3907, 0.5360, 0.5578]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.8026, 0.6854, 0.4524,  ..., 0.3540, 0.7807, 0.8385])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 122.3178551197052 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4994,    10005,  ..., 49990059,
+                            49994955, 50000000]),
+       col_indices=tensor([   0,    1,    3,  ..., 9993, 9994, 9997]),
+       values=tensor([0.7680, 0.8457, 0.0556,  ..., 0.3907, 0.5360, 0.5578]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.8026, 0.6854, 0.4524,  ..., 0.3540, 0.7807, 0.8385])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 122.3178551197052 seconds
+
+[20.72, 20.68, 20.44, 20.36, 20.16, 20.16, 19.92, 20.0, 20.04, 20.16]
+[20.12, 20.36, 20.6, 24.52, 26.2, 27.84, 28.8, 27.28, 29.52, 31.56, 31.56, 31.64, 32.56, 31.84, 27.64, 27.84, 28.52, 29.04, 29.32, 29.24, 28.2, 27.12, 26.12, 26.12, 25.08, 24.96, 24.96, 24.84, 24.88, 24.8, 24.56, 24.44, 24.2, 24.56, 24.68, 24.68, 24.68, 25.0, 24.96, 24.84, 24.76, 24.72, 24.44, 24.56, 24.52, 24.56, 24.96, 24.84, 24.84, 24.68, 24.76, 24.72, 24.6, 24.56, 24.4, 24.48, 24.36, 24.48, 24.44, 24.4, 24.4, 24.48, 24.24, 24.24, 24.28, 24.28, 24.16, 24.48, 24.52, 24.64, 25.0, 24.8, 24.88, 24.88, 24.84, 24.6, 24.48, 24.56, 24.56, 24.44, 24.36, 24.08, 24.2, 24.16, 24.32, 24.44, 24.44, 24.56, 24.64, 24.6, 24.6, 24.2, 24.04, 24.32, 24.36, 24.32, 24.64, 24.36, 24.36, 24.52, 24.44, 24.24, 24.24, 24.44, 24.4, 24.32, 24.68, 24.6, 24.6, 24.64, 24.72, 24.72, 24.36, 24.4, 24.16, 24.28, 24.44, 24.32, 24.4, 24.56, 24.52, 24.52]
+123.7566237449646
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 122.3178551197052, 'TIME_S_1KI': 1223.178551197052, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3064.272414226532, 'W': 24.76047197717133}
+[20.72, 20.68, 20.44, 20.36, 20.16, 20.16, 19.92, 20.0, 20.04, 20.16, 20.48, 20.52, 20.6, 20.6, 20.44, 20.44, 20.48, 20.28, 20.0, 20.04]
+365.82000000000005
+18.291000000000004
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 122.3178551197052, 'TIME_S_1KI': 1223.178551197052, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3064.272414226532, 'W': 24.76047197717133, 'J_1KI': 30642.72414226532, 'W_1KI': 247.6047197717133, 'W_D': 6.469471977171327, 'J_D': 800.6400093073842, 'W_D_1KI': 64.69471977171328, 'J_D_1KI': 646.9471977171328}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.json
index e0e6919..3b59597 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 145476, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.560847282409668, "TIME_S_1KI": 0.07259511728676668, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 314.8679765701293, "W": 22.125839021878733, "J_1KI": 2.1643980902013342, "W_1KI": 0.15209270960075016, "W_D": 3.6298390218787304, "J_D": 51.65544533538807, "W_D_1KI": 0.024951462934633413, "J_D_1KI": 0.00017151600906426773}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 138585, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.35036301612854, "TIME_S_1KI": 0.07468602674263838, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 330.615868434906, "W": 23.22132044200209, "J_1KI": 2.385654063822968, "W_1KI": 0.16756012874410714, "W_D": 4.727320442002089, "J_D": 67.30569681453706, "W_D_1KI": 0.034111342800462456, "J_D_1KI": 0.0002461402229711906}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.output
index 29cdb63..8b339c8 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_1e-05.output
@@ -1,373 +1,373 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.015525579452514648}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.01529836654663086}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
-       col_indices=tensor([3898, 1482, 5519, 8868, 7883, 9892, 3663, 8094, 3265,
-                           5214, 2679, 8390, 8300, 1254, 5771, 2330, 2277, 5567,
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-                           1903]),
-       values=tensor([8.8707e-01, 8.2479e-01, 8.1424e-01, 6.0134e-01,
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+                      4.4886e-01, 4.6777e-01, 7.9362e-01, 2.4707e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.3198, 0.6605, 0.6944,  ..., 0.6134, 0.5235, 0.6507])
+tensor([0.5683, 0.7872, 0.4946,  ..., 0.0674, 0.9451, 0.0950])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -375,650 +375,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.015525579452514648 seconds
+Time: 0.01529836654663086 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 67630 -ss 10000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.8812994956970215}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
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-       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.0493, 0.7033, 0.8450,  ..., 0.3708, 0.9702, 0.0106])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 4.8812994956970215 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 145476 -ss 10000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.560847282409668}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 68634 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.2000732421875}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4976, 0.0481, 0.1913,  ..., 0.0301, 0.3766, 0.0826])
+tensor([0.2470, 0.1789, 0.3752,  ..., 0.5099, 0.7520, 0.5628])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1026,268 +754,271 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.560847282409668 seconds
+Time: 5.2000732421875 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 138585 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.35036301612854}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.1973, 0.3656, 0.0240, 0.0025, 0.3395, 0.3695, 0.8744,
+                      0.9097, 0.7335, 0.2404, 0.0369, 0.6290, 0.1712, 0.3940,
+                      0.3991, 0.5322, 0.9830, 0.7075, 0.4980, 0.3804, 0.4277,
+                      0.4732, 0.3721, 0.2580, 0.7335, 0.2379, 0.0923, 0.4525,
+                      0.9507, 0.1057, 0.7727, 0.1758, 0.7368, 0.0289, 0.1710,
+                      0.2037, 0.6810, 0.9099, 0.7890, 0.0406, 0.0261, 0.9874,
+                      0.9915, 0.3904, 0.8019, 0.2435, 0.3811, 0.1826, 0.1831,
+                      0.8831, 0.1668, 0.0829, 0.3309, 0.0780, 0.1112, 0.8197,
+                      0.0271, 0.4877, 0.3209, 0.2643, 0.2855, 0.3654, 0.4651,
+                      0.9876, 0.5843, 0.6127, 0.0052, 0.3528, 0.5656]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4976, 0.0481, 0.1913,  ..., 0.0301, 0.3766, 0.0826])
+tensor([0.8355, 0.8843, 0.0720,  ..., 0.0094, 0.5931, 0.4853])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1295,13 +1026,282 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.560847282409668 seconds
+Time: 10.35036301612854 seconds
 
-[20.4, 20.16, 20.52, 20.6, 20.92, 20.92, 20.96, 20.76, 20.68, 20.44]
-[20.4, 20.52, 20.92, 23.12, 24.52, 26.12, 26.6, 26.16, 24.72, 24.72, 23.36, 23.32, 23.4, 23.52]
-14.230781316757202
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 145476, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.560847282409668, 'TIME_S_1KI': 0.07259511728676668, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.8679765701293, 'W': 22.125839021878733}
-[20.4, 20.16, 20.52, 20.6, 20.92, 20.92, 20.96, 20.76, 20.68, 20.44, 20.2, 20.44, 20.48, 20.56, 20.52, 20.48, 20.4, 20.4, 20.4, 20.4]
-369.9200000000001
-18.496000000000002
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 145476, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.560847282409668, 'TIME_S_1KI': 0.07259511728676668, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.8679765701293, 'W': 22.125839021878733, 'J_1KI': 2.1643980902013342, 'W_1KI': 0.15209270960075016, 'W_D': 3.6298390218787304, 'J_D': 51.65544533538807, 'W_D_1KI': 0.024951462934633413, 'J_D_1KI': 0.00017151600906426773}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([4697, 7166, 5061, 5588,   16, 2135, 4356, 6963, 1471,
+                           6448, 7291, 4803, 9505, 5043,  277, 2464, 2086, 1290,
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+                           2478]),
+       values=tensor([0.3702, 0.7345, 0.6826, 0.7317, 0.9145, 0.4627, 0.9182,
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+                      0.9915, 0.3904, 0.8019, 0.2435, 0.3811, 0.1826, 0.1831,
+                      0.8831, 0.1668, 0.0829, 0.3309, 0.0780, 0.1112, 0.8197,
+                      0.0271, 0.4877, 0.3209, 0.2643, 0.2855, 0.3654, 0.4651,
+                      0.9876, 0.5843, 0.6127, 0.0052, 0.3528, 0.5656]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8355, 0.8843, 0.0720,  ..., 0.0094, 0.5931, 0.4853])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.35036301612854 seconds
+
+[20.52, 20.56, 20.76, 20.76, 20.48, 20.44, 20.32, 20.56, 20.68, 20.96]
+[20.96, 20.6, 21.04, 25.8, 27.56, 28.4, 28.4, 29.0, 26.08, 24.84, 23.8, 23.52, 23.6, 23.72]
+14.237599849700928
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 138585, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.35036301612854, 'TIME_S_1KI': 0.07468602674263838, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 330.615868434906, 'W': 23.22132044200209}
+[20.52, 20.56, 20.76, 20.76, 20.48, 20.44, 20.32, 20.56, 20.68, 20.96, 20.52, 20.56, 20.76, 20.52, 20.68, 20.6, 20.48, 20.48, 20.2, 20.08]
+369.88
+18.494
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 138585, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.35036301612854, 'TIME_S_1KI': 0.07468602674263838, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 330.615868434906, 'W': 23.22132044200209, 'J_1KI': 2.385654063822968, 'W_1KI': 0.16756012874410714, 'W_D': 4.727320442002089, 'J_D': 67.30569681453706, 'W_D_1KI': 0.034111342800462456, 'J_D_1KI': 0.0002461402229711906}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.json
index 2bcb239..e6d8cff 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 52342, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.44128155708313, "TIME_S_1KI": 0.19948189899283808, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 334.7413140869141, "W": 23.567352524316387, "J_1KI": 6.395271752835469, "W_1KI": 0.4502570120422679, "W_D": 3.4093525243163825, "J_D": 48.42508902931206, "W_D_1KI": 0.06513607665577133, "J_D_1KI": 0.0012444323231013588}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 51464, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.445167779922485, "TIME_S_1KI": 0.20296066726104628, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 314.6867702293396, "W": 22.03824534762116, "J_1KI": 6.114697074252673, "W_1KI": 0.4282264368805604, "W_D": 3.5672453476211636, "J_D": 50.937127677440664, "W_D_1KI": 0.06931535340473269, "J_D_1KI": 0.0013468706941693746}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.output
index 0a68cee..abbe8e8 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_10000_5e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.027875900268554688}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.02733898162841797}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([4151, 7566,   61,  ..., 1923, 6890, 8738]),
-       values=tensor([0.6199, 0.1524, 0.8589,  ..., 0.4429, 0.5764, 0.1533]),
+       col_indices=tensor([9723, 8611, 8438,  ..., 5624,  673, 6841]),
+       values=tensor([0.4875, 0.3962, 0.0358,  ..., 0.4364, 0.5339, 0.1666]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.5335, 0.6247, 0.4039,  ..., 0.6064, 0.4993, 0.4017])
+tensor([0.8431, 0.8974, 0.5076,  ..., 0.9535, 0.3028, 0.2501])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 0.027875900268554688 seconds
+Time: 0.02733898162841797 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 37666 -ss 10000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.555848598480225}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 38406 -ss 10000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.835801124572754}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    2,  ..., 4999, 5000, 5000]),
-       col_indices=tensor([4832, 7617, 3198,  ..., 2337, 8239, 2535]),
-       values=tensor([0.0012, 0.2497, 0.5477,  ..., 0.0331, 0.3343, 0.4565]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4998, 5000, 5000]),
+       col_indices=tensor([5082, 2494, 7957,  ..., 5477, 1429, 2123]),
+       values=tensor([0.1927, 0.5682, 0.3872,  ..., 0.1095, 0.5904, 0.1329]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.1414, 0.6293, 0.2915,  ..., 0.6179, 0.0556, 0.9688])
+tensor([0.1556, 0.9461, 0.0409,  ..., 0.3172, 0.5821, 0.0029])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 7.555848598480225 seconds
+Time: 7.835801124572754 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 52342 -ss 10000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.44128155708313}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 51464 -ss 10000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.445167779922485}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 4999, 5000, 5000]),
-       col_indices=tensor([6929, 6481, 6208,  ..., 5185, 5914, 4436]),
-       values=tensor([0.2292, 0.3731, 0.2148,  ..., 0.4978, 0.6385, 0.1071]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([1626, 9635, 3828,  ..., 5045, 7904, 9593]),
+       values=tensor([0.2200, 0.6966, 0.2889,  ..., 0.9700, 0.2141, 0.3368]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.7171, 0.2412, 0.9457,  ..., 0.4356, 0.0163, 0.8101])
+tensor([0.8545, 0.5671, 0.0230,  ..., 0.8560, 0.7069, 0.3058])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.44128155708313 seconds
+Time: 10.445167779922485 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 4999, 5000, 5000]),
-       col_indices=tensor([6929, 6481, 6208,  ..., 5185, 5914, 4436]),
-       values=tensor([0.2292, 0.3731, 0.2148,  ..., 0.4978, 0.6385, 0.1071]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([1626, 9635, 3828,  ..., 5045, 7904, 9593]),
+       values=tensor([0.2200, 0.6966, 0.2889,  ..., 0.9700, 0.2141, 0.3368]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.7171, 0.2412, 0.9457,  ..., 0.4356, 0.0163, 0.8101])
+tensor([0.8545, 0.5671, 0.0230,  ..., 0.8560, 0.7069, 0.3058])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.44128155708313 seconds
+Time: 10.445167779922485 seconds
 
-[25.48, 25.4, 25.4, 24.8, 24.68, 24.36, 24.4, 24.16, 24.24, 23.68]
-[23.36, 22.52, 25.48, 26.0, 27.84, 27.84, 27.8, 28.32, 24.88, 24.76, 23.72, 23.52, 23.72, 23.68]
-14.20360279083252
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52342, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.44128155708313, 'TIME_S_1KI': 0.19948189899283808, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.7413140869141, 'W': 23.567352524316387}
-[25.48, 25.4, 25.4, 24.8, 24.68, 24.36, 24.4, 24.16, 24.24, 23.68, 20.0, 19.92, 19.76, 20.2, 20.12, 20.12, 20.36, 20.24, 20.24, 20.36]
-403.1600000000001
-20.158000000000005
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52342, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.44128155708313, 'TIME_S_1KI': 0.19948189899283808, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.7413140869141, 'W': 23.567352524316387, 'J_1KI': 6.395271752835469, 'W_1KI': 0.4502570120422679, 'W_D': 3.4093525243163825, 'J_D': 48.42508902931206, 'W_D_1KI': 0.06513607665577133, 'J_D_1KI': 0.0012444323231013588}
+[20.64, 20.72, 20.64, 20.8, 20.8, 20.76, 20.6, 20.64, 20.4, 20.16]
+[20.28, 20.2, 20.6, 21.76, 24.52, 25.28, 26.2, 25.88, 25.88, 25.72, 23.64, 23.4, 23.36, 23.24]
+14.279120922088623
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 51464, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.445167779922485, 'TIME_S_1KI': 0.20296066726104628, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.6867702293396, 'W': 22.03824534762116}
+[20.64, 20.72, 20.64, 20.8, 20.8, 20.76, 20.6, 20.64, 20.4, 20.16, 20.56, 20.44, 20.32, 20.6, 20.48, 20.36, 20.44, 20.36, 20.36, 20.04]
+369.41999999999996
+18.470999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 51464, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.445167779922485, 'TIME_S_1KI': 0.20296066726104628, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.6867702293396, 'W': 22.03824534762116, 'J_1KI': 6.114697074252673, 'W_1KI': 0.4282264368805604, 'W_D': 3.5672453476211636, 'J_D': 50.937127677440664, 'W_D_1KI': 0.06931535340473269, 'J_D_1KI': 0.0013468706941693746}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.json
index ece3918..05807f6 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 137, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.396055936813354, "TIME_S_1KI": 75.88361997673981, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 753.5857475948335, "W": 33.1694657622459, "J_1KI": 5500.625894852799, "W_1KI": 242.11288877551752, "W_D": 14.2684657622459, "J_D": 324.16899673771866, "W_D_1KI": 104.14938512588249, "J_D_1KI": 760.2144899699452}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 134, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.467389583587646, "TIME_S_1KI": 78.11484763871378, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 706.7122743606567, "W": 32.619576005812384, "J_1KI": 5273.972196721319, "W_1KI": 243.42967168516705, "W_D": 13.915576005812383, "J_D": 301.4848619232177, "W_D_1KI": 103.84758213292824, "J_D_1KI": 774.9819562158824}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.output
index 5419742..71de004 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_0.0001.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.629654169082642}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.793238162994385}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       44,       92,  ..., 24999905,
-                            24999955, 25000000]),
-       col_indices=tensor([  2191,   6192,  41052,  ..., 471066, 488040,
-                           493296]),
-       values=tensor([0.3986, 0.5227, 0.3241,  ..., 0.9261, 0.7192, 0.3287]),
+tensor(crow_indices=tensor([       0,       60,      106,  ..., 24999903,
+                            24999944, 25000000]),
+       col_indices=tensor([  5013,   8672,  15970,  ..., 458181, 460431,
+                           489743]),
+       values=tensor([0.5903, 0.4134, 0.2669,  ..., 0.7684, 0.5815, 0.3065]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.0957, 0.3468, 0.1431,  ..., 0.5849, 0.2942, 0.3782])
+tensor([0.8384, 0.7499, 0.3876,  ..., 0.2128, 0.0590, 0.5085])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 7.629654169082642 seconds
+Time: 7.793238162994385 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 137 -ss 500000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.396055936813354}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 134 -ss 500000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.467389583587646}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       53,       94,  ..., 24999919,
-                            24999951, 25000000]),
-       col_indices=tensor([  2485,  12624,  22152,  ..., 462150, 467889,
-                           476331]),
-       values=tensor([0.9572, 0.0985, 0.5455,  ..., 0.5648, 0.8530, 0.8208]),
+tensor(crow_indices=tensor([       0,       60,      123,  ..., 24999898,
+                            24999943, 25000000]),
+       col_indices=tensor([  6210,  15955,  17043,  ..., 486318, 493056,
+                           494837]),
+       values=tensor([0.0668, 0.1833, 0.6551,  ..., 0.5227, 0.0230, 0.1644]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.3242, 0.9080, 0.9457,  ..., 0.2147, 0.3332, 0.4113])
+tensor([0.4055, 0.4142, 0.8884,  ..., 0.0877, 0.6540, 0.4930])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 10.396055936813354 seconds
+Time: 10.467389583587646 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       53,       94,  ..., 24999919,
-                            24999951, 25000000]),
-       col_indices=tensor([  2485,  12624,  22152,  ..., 462150, 467889,
-                           476331]),
-       values=tensor([0.9572, 0.0985, 0.5455,  ..., 0.5648, 0.8530, 0.8208]),
+tensor(crow_indices=tensor([       0,       60,      123,  ..., 24999898,
+                            24999943, 25000000]),
+       col_indices=tensor([  6210,  15955,  17043,  ..., 486318, 493056,
+                           494837]),
+       values=tensor([0.0668, 0.1833, 0.6551,  ..., 0.5227, 0.0230, 0.1644]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.3242, 0.9080, 0.9457,  ..., 0.2147, 0.3332, 0.4113])
+tensor([0.4055, 0.4142, 0.8884,  ..., 0.0877, 0.6540, 0.4930])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 10.396055936813354 seconds
+Time: 10.467389583587646 seconds
 
-[20.64, 20.72, 20.84, 20.96, 21.08, 21.36, 21.56, 21.56, 21.36, 21.36]
-[21.36, 21.04, 20.8, 24.16, 25.16, 26.72, 28.8, 27.16, 30.52, 29.84, 29.76, 29.92, 29.92, 30.28, 32.88, 38.36, 44.52, 49.04, 53.12, 53.28, 53.2, 52.84]
-22.719260931015015
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 137, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.396055936813354, 'TIME_S_1KI': 75.88361997673981, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 753.5857475948335, 'W': 33.1694657622459}
-[20.64, 20.72, 20.84, 20.96, 21.08, 21.36, 21.56, 21.56, 21.36, 21.36, 20.8, 20.84, 20.88, 21.0, 21.04, 20.84, 20.96, 20.68, 20.68, 20.52]
-378.02
-18.901
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 137, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.396055936813354, 'TIME_S_1KI': 75.88361997673981, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 753.5857475948335, 'W': 33.1694657622459, 'J_1KI': 5500.625894852799, 'W_1KI': 242.11288877551752, 'W_D': 14.2684657622459, 'J_D': 324.16899673771866, 'W_D_1KI': 104.14938512588249, 'J_D_1KI': 760.2144899699452}
+[21.04, 21.08, 21.12, 20.72, 20.84, 20.76, 20.48, 20.6, 20.8, 21.12]
+[21.12, 21.12, 21.36, 22.64, 24.76, 25.8, 28.0, 29.0, 30.12, 31.08, 31.04, 30.64, 30.88, 31.48, 37.76, 37.76, 43.28, 49.24, 53.92, 53.72, 53.68]
+21.66528081893921
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 134, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.467389583587646, 'TIME_S_1KI': 78.11484763871378, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 706.7122743606567, 'W': 32.619576005812384}
+[21.04, 21.08, 21.12, 20.72, 20.84, 20.76, 20.48, 20.6, 20.8, 21.12, 20.52, 20.72, 20.56, 20.52, 20.64, 20.88, 20.72, 20.84, 21.0, 20.92]
+374.08
+18.704
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 134, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.467389583587646, 'TIME_S_1KI': 78.11484763871378, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 706.7122743606567, 'W': 32.619576005812384, 'J_1KI': 5273.972196721319, 'W_1KI': 243.42967168516705, 'W_D': 13.915576005812383, 'J_D': 301.4848619232177, 'W_D_1KI': 103.84758213292824, 'J_D_1KI': 774.9819562158824}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.json
index 28ed47d..2ff6880 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1548, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.061279058456421, "TIME_S_1KI": 7.145529107529987, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 576.3272948646545, "W": 36.84956710355697, "J_1KI": 372.3044540469344, "W_1KI": 23.80462991185851, "W_D": 18.03556710355697, "J_D": 282.0763014917373, "W_D_1KI": 11.65088314183267, "J_D_1KI": 7.526410298341518}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1566, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.265558242797852, "TIME_S_1KI": 7.1938430669207225, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 590.2575314998627, "W": 37.710029711893654, "J_1KI": 376.920518199146, "W_1KI": 24.08047874322711, "W_D": 19.015029711893654, "J_D": 297.63340376257895, "W_D_1KI": 12.142419994823534, "J_D_1KI": 7.75378032875066}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.output
index ddb3af3..9ff0fff 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.6782102584838867}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.6704628467559814}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       8,      13,  ..., 2499990,
-                            2499997, 2500000]),
-       col_indices=tensor([ 40175, 122073, 147940,  ..., 245767, 297950,
-                           495791]),
-       values=tensor([0.1248, 0.8645, 0.7112,  ..., 0.2227, 0.8085, 0.2637]),
+tensor(crow_indices=tensor([      0,       6,      13,  ..., 2499992,
+                            2499996, 2500000]),
+       col_indices=tensor([ 18465, 224310, 266853,  ..., 261692, 275322,
+                           390795]),
+       values=tensor([0.7820, 0.2662, 0.3463,  ..., 0.8983, 0.8249, 0.4987]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3055, 0.1588, 0.3916,  ..., 0.3608, 0.8122, 0.4114])
+tensor([0.5044, 0.5686, 0.5423,  ..., 0.2915, 0.5450, 0.7110])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 0.6782102584838867 seconds
+Time: 0.6704628467559814 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1548 -ss 500000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.061279058456421}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1566 -ss 500000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.265558242797852}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       0,       4,  ..., 2499989,
+tensor(crow_indices=tensor([      0,       4,      12,  ..., 2499989,
                             2499994, 2500000]),
-       col_indices=tensor([ 33871,  87157, 252512,  ..., 380315, 410804,
-                           497208]),
-       values=tensor([0.0607, 0.8545, 0.0688,  ..., 0.9965, 0.6178, 0.6113]),
+       col_indices=tensor([ 32811, 195297, 326453,  ..., 287008, 298525,
+                           381158]),
+       values=tensor([0.7187, 0.3561, 0.0889,  ..., 0.2717, 0.8525, 0.2350]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3094, 0.9384, 0.5289,  ..., 0.5205, 0.7717, 0.7334])
+tensor([0.4888, 0.5473, 0.1593,  ..., 0.6089, 0.0990, 0.9360])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 11.061279058456421 seconds
+Time: 11.265558242797852 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       0,       4,  ..., 2499989,
+tensor(crow_indices=tensor([      0,       4,      12,  ..., 2499989,
                             2499994, 2500000]),
-       col_indices=tensor([ 33871,  87157, 252512,  ..., 380315, 410804,
-                           497208]),
-       values=tensor([0.0607, 0.8545, 0.0688,  ..., 0.9965, 0.6178, 0.6113]),
+       col_indices=tensor([ 32811, 195297, 326453,  ..., 287008, 298525,
+                           381158]),
+       values=tensor([0.7187, 0.3561, 0.0889,  ..., 0.2717, 0.8525, 0.2350]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3094, 0.9384, 0.5289,  ..., 0.5205, 0.7717, 0.7334])
+tensor([0.4888, 0.5473, 0.1593,  ..., 0.6089, 0.0990, 0.9360])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 11.061279058456421 seconds
+Time: 11.265558242797852 seconds
 
-[20.72, 20.76, 21.12, 20.92, 20.92, 20.92, 20.88, 20.44, 20.48, 20.48]
-[20.56, 20.56, 21.24, 22.4, 24.48, 28.6, 36.72, 41.88, 48.52, 48.52, 52.36, 52.92, 53.48, 53.4, 53.48]
-15.640001773834229
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1548, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.061279058456421, 'TIME_S_1KI': 7.145529107529987, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 576.3272948646545, 'W': 36.84956710355697}
-[20.72, 20.76, 21.12, 20.92, 20.92, 20.92, 20.88, 20.44, 20.48, 20.48, 20.6, 20.68, 20.88, 21.24, 21.48, 21.32, 21.48, 20.96, 20.64, 20.52]
-376.28
-18.814
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1548, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.061279058456421, 'TIME_S_1KI': 7.145529107529987, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 576.3272948646545, 'W': 36.84956710355697, 'J_1KI': 372.3044540469344, 'W_1KI': 23.80462991185851, 'W_D': 18.03556710355697, 'J_D': 282.0763014917373, 'W_D_1KI': 11.65088314183267, 'J_D_1KI': 7.526410298341518}
+[20.76, 20.8, 20.76, 20.88, 20.84, 20.68, 20.68, 21.0, 20.88, 20.92]
+[21.0, 21.08, 21.12, 24.92, 26.6, 30.76, 37.4, 42.12, 47.32, 52.72, 53.68, 53.68, 53.36, 53.36, 53.48]
+15.652534246444702
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.265558242797852, 'TIME_S_1KI': 7.1938430669207225, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 590.2575314998627, 'W': 37.710029711893654}
+[20.76, 20.8, 20.76, 20.88, 20.84, 20.68, 20.68, 21.0, 20.88, 20.92, 20.52, 20.76, 21.0, 20.92, 20.88, 20.72, 20.56, 20.52, 20.6, 20.64]
+373.9
+18.695
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.265558242797852, 'TIME_S_1KI': 7.1938430669207225, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 590.2575314998627, 'W': 37.710029711893654, 'J_1KI': 376.920518199146, 'W_1KI': 24.08047874322711, 'W_D': 19.015029711893654, 'J_D': 297.63340376257895, 'W_D_1KI': 12.142419994823534, 'J_D_1KI': 7.75378032875066}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.json
index 9620135..6f5ae4b 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 285, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.060697317123413, "TIME_S_1KI": 38.809464270608466, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 641.8192787170411, "W": 34.323302144261945, "J_1KI": 2251.9974691826005, "W_1KI": 120.43263910267349, "W_D": 15.818302144261942, "J_D": 295.7900504469872, "W_D_1KI": 55.50281454126997, "J_D_1KI": 194.74671768866656}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 282, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.728825569152832, "TIME_S_1KI": 38.04548074167671, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 652.2455996704102, "W": 34.861341364200754, "J_1KI": 2312.92765840571, "W_1KI": 123.62177788723672, "W_D": 15.989341364200758, "J_D": 299.15594576454185, "W_D_1KI": 56.699792071633894, "J_D_1KI": 201.06309245260246}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.output
index 0f40f5a..6fcb344 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_500000_5e-05.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.6834404468536377}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.716564655303955}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       36,       64,  ..., 12499962,
-                            12499977, 12500000]),
-       col_indices=tensor([  6540,  37225,  45963,  ..., 476281, 491551,
-                           491729]),
-       values=tensor([0.4995, 0.3434, 0.2289,  ..., 0.9980, 0.3953, 0.2839]),
+tensor(crow_indices=tensor([       0,       34,       56,  ..., 12499948,
+                            12499973, 12500000]),
+       col_indices=tensor([  6554,  12774,  29441,  ..., 452817, 453786,
+                           473967]),
+       values=tensor([0.5936, 0.4042, 0.6702,  ..., 0.6965, 0.8881, 0.9167]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.6494, 0.0196, 0.1697,  ..., 0.1655, 0.3294, 0.8926])
+tensor([0.1409, 0.2128, 0.4133,  ..., 0.3735, 0.0685, 0.1386])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 3.6834404468536377 seconds
+Time: 3.716564655303955 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 285 -ss 500000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.060697317123413}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 282 -ss 500000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.728825569152832}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       30,       51,  ..., 12499947,
-                            12499979, 12500000]),
-       col_indices=tensor([ 32854,  40713,  51141,  ..., 464012, 471829,
-                           496055]),
-       values=tensor([0.7704, 0.8573, 0.2864,  ..., 0.9432, 0.9508, 0.7094]),
+tensor(crow_indices=tensor([       0,       29,       62,  ..., 12499958,
+                            12499978, 12500000]),
+       col_indices=tensor([ 57103,  90320,  96727,  ..., 418254, 450816,
+                           472842]),
+       values=tensor([0.1970, 0.6853, 0.2290,  ..., 0.5665, 0.4164, 0.8779]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.9627, 0.2273, 0.6691,  ..., 0.1238, 0.9472, 0.0057])
+tensor([0.7571, 0.9474, 0.9034,  ..., 0.2940, 0.2447, 0.3708])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 11.060697317123413 seconds
+Time: 10.728825569152832 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       30,       51,  ..., 12499947,
-                            12499979, 12500000]),
-       col_indices=tensor([ 32854,  40713,  51141,  ..., 464012, 471829,
-                           496055]),
-       values=tensor([0.7704, 0.8573, 0.2864,  ..., 0.9432, 0.9508, 0.7094]),
+tensor(crow_indices=tensor([       0,       29,       62,  ..., 12499958,
+                            12499978, 12500000]),
+       col_indices=tensor([ 57103,  90320,  96727,  ..., 418254, 450816,
+                           472842]),
+       values=tensor([0.1970, 0.6853, 0.2290,  ..., 0.5665, 0.4164, 0.8779]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.9627, 0.2273, 0.6691,  ..., 0.1238, 0.9472, 0.0057])
+tensor([0.7571, 0.9474, 0.9034,  ..., 0.2940, 0.2447, 0.3708])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 11.060697317123413 seconds
+Time: 10.728825569152832 seconds
 
-[20.52, 20.84, 20.92, 20.88, 20.88, 20.72, 20.52, 20.32, 20.36, 20.2]
-[20.44, 20.6, 21.48, 21.48, 23.84, 25.24, 27.32, 30.28, 29.76, 32.76, 37.84, 41.44, 46.92, 51.96, 52.12, 52.64, 52.6, 52.8]
-18.699228763580322
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 285, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.060697317123413, 'TIME_S_1KI': 38.809464270608466, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 641.8192787170411, 'W': 34.323302144261945}
-[20.52, 20.84, 20.92, 20.88, 20.88, 20.72, 20.52, 20.32, 20.36, 20.2, 20.52, 20.44, 20.6, 20.44, 20.44, 20.48, 20.48, 20.36, 20.44, 20.72]
-370.1
-18.505000000000003
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 285, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.060697317123413, 'TIME_S_1KI': 38.809464270608466, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 641.8192787170411, 'W': 34.323302144261945, 'J_1KI': 2251.9974691826005, 'W_1KI': 120.43263910267349, 'W_D': 15.818302144261942, 'J_D': 295.7900504469872, 'W_D_1KI': 55.50281454126997, 'J_D_1KI': 194.74671768866656}
+[21.0, 20.76, 20.92, 21.0, 21.12, 21.12, 21.16, 21.08, 20.88, 20.52]
+[20.56, 20.52, 20.52, 21.4, 22.64, 24.72, 27.0, 30.12, 30.6, 35.4, 40.24, 43.88, 49.0, 53.2, 53.36, 53.16, 52.72, 52.48]
+18.709710359573364
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 282, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.728825569152832, 'TIME_S_1KI': 38.04548074167671, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 652.2455996704102, 'W': 34.861341364200754}
+[21.0, 20.76, 20.92, 21.0, 21.12, 21.12, 21.16, 21.08, 20.88, 20.52, 20.6, 20.68, 21.08, 21.08, 20.96, 21.2, 21.32, 20.88, 20.8, 20.68]
+377.43999999999994
+18.871999999999996
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 282, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.728825569152832, 'TIME_S_1KI': 38.04548074167671, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 652.2455996704102, 'W': 34.861341364200754, 'J_1KI': 2312.92765840571, 'W_1KI': 123.62177788723672, 'W_D': 15.989341364200758, 'J_D': 299.15594576454185, 'W_D_1KI': 56.699792071633894, 'J_D_1KI': 201.06309245260246}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.json
index 29a7f3d..bba8bd2 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3424, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.731184720993042, "TIME_S_1KI": 3.1341076872059115, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 463.79336466789243, "W": 31.765422510706816, "J_1KI": 135.4536695875854, "W_1KI": 9.27728461177185, "W_D": 13.349422510706813, "J_D": 194.90921553230277, "W_D_1KI": 3.8987799388746534, "J_D_1KI": 1.1386623653255412}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3338, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.433396339416504, "TIME_S_1KI": 3.1256430016226795, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 439.5648366165161, "W": 32.3031062563951, "J_1KI": 131.68509185635594, "W_1KI": 9.67738353996258, "W_D": 13.753106256395096, "J_D": 187.14552887201313, "W_D_1KI": 4.120163647811593, "J_D_1KI": 1.2343210448806448}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.output
index 192c47b..58f50a5 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.4611988067626953}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3145158290863037}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,      9,  ..., 249990, 249996,
+tensor(crow_indices=tensor([     0,      2,      8,  ..., 249990, 249997,
                             250000]),
-       col_indices=tensor([ 1266,  4071, 18947,  ..., 33754, 36171, 46993]),
-       values=tensor([0.2894, 0.3028, 0.5808,  ..., 0.9499, 0.5530, 0.4490]),
+       col_indices=tensor([16316, 21389,   513,  ...,  3438,  9548, 13900]),
+       values=tensor([0.7129, 0.1949, 0.4120,  ..., 0.9486, 0.3922, 0.7881]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.9097, 0.0887, 0.0049,  ..., 0.6179, 0.8641, 0.1772])
+tensor([0.6555, 0.2877, 0.8843,  ..., 0.9355, 0.9146, 0.1438])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 0.4611988067626953 seconds
+Time: 0.3145158290863037 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 2276 -ss 50000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.978086233139038}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3338 -ss 50000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.433396339416504}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,      9,  ..., 249990, 249995,
+tensor(crow_indices=tensor([     0,      2,      5,  ..., 249989, 249995,
                             250000]),
-       col_indices=tensor([ 2233,  6887, 19755,  ..., 38632, 41476, 48223]),
-       values=tensor([0.3109, 0.9167, 0.4160,  ..., 0.6671, 0.8506, 0.5777]),
+       col_indices=tensor([16110, 27292, 28034,  ..., 24392, 29295, 38009]),
+       values=tensor([0.3064, 0.1653, 0.6261,  ..., 0.3241, 0.1335, 0.8482]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7812, 0.4224, 0.5960,  ..., 0.2514, 0.6292, 0.3012])
+tensor([0.1035, 0.4360, 0.8597,  ..., 0.2911, 0.6198, 0.0473])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 6.978086233139038 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3424 -ss 50000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.731184720993042}
+Time: 10.433396339416504 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,      7,  ..., 249990, 249996,
+tensor(crow_indices=tensor([     0,      2,      5,  ..., 249989, 249995,
                             250000]),
-       col_indices=tensor([12104, 14436, 24112,  ..., 12878, 32819, 38734]),
-       values=tensor([0.5759, 0.9600, 0.3696,  ..., 0.0040, 0.7766, 0.9665]),
+       col_indices=tensor([16110, 27292, 28034,  ..., 24392, 29295, 38009]),
+       values=tensor([0.3064, 0.1653, 0.6261,  ..., 0.3241, 0.1335, 0.8482]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4011, 0.3434, 0.3941,  ..., 0.7256, 0.6030, 0.5117])
+tensor([0.1035, 0.4360, 0.8597,  ..., 0.2911, 0.6198, 0.0473])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,30 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.731184720993042 seconds
+Time: 10.433396339416504 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,      7,  ..., 249990, 249996,
-                            250000]),
-       col_indices=tensor([12104, 14436, 24112,  ..., 12878, 32819, 38734]),
-       values=tensor([0.5759, 0.9600, 0.3696,  ..., 0.0040, 0.7766, 0.9665]),
-       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4011, 0.3434, 0.3941,  ..., 0.7256, 0.6030, 0.5117])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 250000
-Density: 0.0001
-Time: 10.731184720993042 seconds
-
-[20.72, 20.68, 20.48, 20.6, 20.64, 20.32, 20.32, 20.36, 20.32, 20.4]
-[20.52, 20.76, 22.24, 22.24, 24.16, 27.64, 32.84, 38.08, 39.84, 44.4, 44.52, 43.96, 44.12, 44.28]
-14.60057282447815
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3424, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.731184720993042, 'TIME_S_1KI': 3.1341076872059115, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 463.79336466789243, 'W': 31.765422510706816}
-[20.72, 20.68, 20.48, 20.6, 20.64, 20.32, 20.32, 20.36, 20.32, 20.4, 20.68, 20.44, 20.48, 20.48, 20.2, 20.32, 20.44, 20.44, 20.6, 20.6]
-368.32000000000005
-18.416000000000004
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3424, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.731184720993042, 'TIME_S_1KI': 3.1341076872059115, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 463.79336466789243, 'W': 31.765422510706816, 'J_1KI': 135.4536695875854, 'W_1KI': 9.27728461177185, 'W_D': 13.349422510706813, 'J_D': 194.90921553230277, 'W_D_1KI': 3.8987799388746534, 'J_D_1KI': 1.1386623653255412}
+[20.96, 20.44, 20.52, 20.6, 20.6, 20.6, 20.72, 20.88, 20.68, 20.84]
+[20.88, 20.88, 21.28, 25.44, 27.56, 32.8, 38.08, 39.28, 42.28, 44.04, 44.08, 44.32, 44.32]
+13.607509851455688
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3338, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.433396339416504, 'TIME_S_1KI': 3.1256430016226795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 439.5648366165161, 'W': 32.3031062563951}
+[20.96, 20.44, 20.52, 20.6, 20.6, 20.6, 20.72, 20.88, 20.68, 20.84, 20.56, 20.56, 20.44, 20.36, 20.72, 20.68, 20.72, 20.6, 20.48, 20.44]
+371.0
+18.55
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3338, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.433396339416504, 'TIME_S_1KI': 3.1256430016226795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 439.5648366165161, 'W': 32.3031062563951, 'J_1KI': 131.68509185635594, 'W_1KI': 9.67738353996258, 'W_D': 13.753106256395096, 'J_D': 187.14552887201313, 'W_D_1KI': 4.120163647811593, 'J_D_1KI': 1.2343210448806448}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.json
index e699ae2..f0bd04f 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 385, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.064192295074463, "TIME_S_1KI": 28.738161805388216, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 468.0283072185517, "W": 32.037671646494225, "J_1KI": 1215.657940827407, "W_1KI": 83.21473154933565, "W_D": 13.341671646494227, "J_D": 194.90430094528205, "W_D_1KI": 34.65369258829669, "J_D_1KI": 90.00959113843297}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 381, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.208153009414673, "TIME_S_1KI": 29.417724434159247, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 471.2987487792969, "W": 32.20972724950075, "J_1KI": 1237.0045899719078, "W_1KI": 84.53996653412271, "W_D": 13.801727249500754, "J_D": 201.94945251464853, "W_D_1KI": 36.22500590420145, "J_D_1KI": 95.07875565407205}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.output
index c964dd6..46637b5 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.9453940391540527}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.7509913444519043}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      37,      86,  ..., 2499902,
-                            2499952, 2500000]),
-       col_indices=tensor([  541,  1139,  1813,  ..., 42919, 43072, 44933]),
-       values=tensor([0.0452, 0.1724, 0.8861,  ..., 0.4157, 0.9772, 0.2120]),
+tensor(crow_indices=tensor([      0,      53,      99,  ..., 2499896,
+                            2499945, 2500000]),
+       col_indices=tensor([  151,  1532,  2515,  ..., 45409, 45788, 48199]),
+       values=tensor([0.6730, 0.0619, 0.7951,  ..., 0.3963, 0.0592, 0.9045]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1045, 0.1557, 0.1178,  ..., 0.5894, 0.9079, 0.5773])
+tensor([0.5006, 0.5607, 0.2495,  ..., 0.5708, 0.0051, 0.0518])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 2.9453940391540527 seconds
+Time: 2.7509913444519043 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 356 -ss 50000 -sd 0.001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.708060026168823}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 381 -ss 50000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.208153009414673}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      46,      99,  ..., 2499902,
-                            2499948, 2500000]),
-       col_indices=tensor([ 4031,  7226,  7309,  ..., 44877, 48582, 49711]),
-       values=tensor([0.9329, 0.4420, 0.5313,  ..., 0.9423, 0.2849, 0.2389]),
+tensor(crow_indices=tensor([      0,      55,     110,  ..., 2499902,
+                            2499954, 2500000]),
+       col_indices=tensor([ 1027,  1222,  4121,  ..., 48262, 49731, 49819]),
+       values=tensor([0.4693, 0.9048, 0.3075,  ..., 0.6723, 0.2017, 0.4340]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0088, 0.8123, 0.3302,  ..., 0.9483, 0.6171, 0.9552])
+tensor([0.7107, 0.1882, 0.3199,  ..., 0.6310, 0.1253, 0.0320])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 9.708060026168823 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 385 -ss 50000 -sd 0.001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.064192295074463}
+Time: 11.208153009414673 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      67,     125,  ..., 2499902,
-                            2499956, 2500000]),
-       col_indices=tensor([ 1129,  2884,  2891,  ..., 49010, 49022, 49816]),
-       values=tensor([0.8127, 0.7656, 0.2912,  ..., 0.8978, 0.1718, 0.1428]),
+tensor(crow_indices=tensor([      0,      55,     110,  ..., 2499902,
+                            2499954, 2500000]),
+       col_indices=tensor([ 1027,  1222,  4121,  ..., 48262, 49731, 49819]),
+       values=tensor([0.4693, 0.9048, 0.3075,  ..., 0.6723, 0.2017, 0.4340]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7569, 0.5985, 0.1427,  ..., 0.6714, 0.1732, 0.3064])
+tensor([0.7107, 0.1882, 0.3199,  ..., 0.6310, 0.1253, 0.0320])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,30 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 11.064192295074463 seconds
+Time: 11.208153009414673 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      67,     125,  ..., 2499902,
-                            2499956, 2500000]),
-       col_indices=tensor([ 1129,  2884,  2891,  ..., 49010, 49022, 49816]),
-       values=tensor([0.8127, 0.7656, 0.2912,  ..., 0.8978, 0.1718, 0.1428]),
-       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7569, 0.5985, 0.1427,  ..., 0.6714, 0.1732, 0.3064])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 2500000
-Density: 0.001
-Time: 11.064192295074463 seconds
-
-[20.72, 20.52, 20.56, 20.48, 20.48, 20.52, 20.56, 20.6, 20.36, 20.6]
-[20.48, 20.52, 21.48, 23.4, 25.44, 29.64, 35.04, 38.48, 42.0, 43.12, 43.12, 43.84, 43.68, 43.32]
-14.608686685562134
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 385, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.064192295074463, 'TIME_S_1KI': 28.738161805388216, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 468.0283072185517, 'W': 32.037671646494225}
-[20.72, 20.52, 20.56, 20.48, 20.48, 20.52, 20.56, 20.6, 20.36, 20.6, 20.84, 20.8, 21.0, 21.24, 21.16, 21.08, 21.28, 20.92, 20.84, 20.88]
-373.91999999999996
-18.695999999999998
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 385, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.064192295074463, 'TIME_S_1KI': 28.738161805388216, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 468.0283072185517, 'W': 32.037671646494225, 'J_1KI': 1215.657940827407, 'W_1KI': 83.21473154933565, 'W_D': 13.341671646494227, 'J_D': 194.90430094528205, 'W_D_1KI': 34.65369258829669, 'J_D_1KI': 90.00959113843297}
+[20.52, 20.72, 20.76, 20.76, 20.84, 20.76, 20.44, 20.24, 20.6, 20.6]
+[20.52, 20.56, 23.48, 24.32, 26.96, 31.16, 36.28, 37.6, 41.44, 41.44, 43.16, 42.84, 43.16, 42.96]
+14.632186889648438
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 381, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.208153009414673, 'TIME_S_1KI': 29.417724434159247, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 471.2987487792969, 'W': 32.20972724950075}
+[20.52, 20.72, 20.76, 20.76, 20.84, 20.76, 20.44, 20.24, 20.6, 20.6, 20.28, 20.2, 20.4, 20.32, 20.0, 20.04, 20.16, 20.32, 20.6, 20.6]
+368.15999999999997
+18.407999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 381, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.208153009414673, 'TIME_S_1KI': 29.417724434159247, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 471.2987487792969, 'W': 32.20972724950075, 'J_1KI': 1237.0045899719078, 'W_1KI': 84.53996653412271, 'W_D': 13.801727249500754, 'J_D': 201.94945251464853, 'W_D_1KI': 36.22500590420145, 'J_D_1KI': 95.07875565407205}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.json
index 3c1f90f..7eccccc 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 32.80737018585205, "TIME_S_1KI": 328.0737018585205, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1398.267660713196, "W": 32.405416577679354, "J_1KI": 13982.676607131958, "W_1KI": 324.0541657767935, "W_D": 13.697416577679352, "J_D": 591.0325080986024, "W_D_1KI": 136.97416577679354, "J_D_1KI": 1369.7416577679353}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 31.461788177490234, "TIME_S_1KI": 314.61788177490234, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1422.216083526611, "W": 32.08903074827424, "J_1KI": 14222.160835266111, "W_1KI": 320.8903074827424, "W_D": 13.453030748274237, "J_D": 596.2510009250636, "W_D_1KI": 134.53030748274236, "J_D_1KI": 1345.3030748274234}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.output
index 0d92e6b..3ea4230 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.01 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 32.80737018585205}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 31.461788177490234}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      489,      963,  ..., 24999055,
-                            24999529, 25000000]),
-       col_indices=tensor([   18,   157,   241,  ..., 49747, 49771, 49960]),
-       values=tensor([0.7706, 0.7949, 0.9210,  ..., 0.0962, 0.6322, 0.0053]),
+tensor(crow_indices=tensor([       0,      495,     1024,  ..., 24998992,
+                            24999507, 25000000]),
+       col_indices=tensor([   89,   121,   308,  ..., 49151, 49365, 49592]),
+       values=tensor([0.2016, 0.7170, 0.9969,  ..., 0.6090, 0.1562, 0.9700]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.2729, 0.2896, 0.6966,  ..., 0.6831, 0.4086, 0.6520])
+tensor([0.5426, 0.2677, 0.5298,  ..., 0.8739, 0.7597, 0.2370])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,16 +16,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 32.80737018585205 seconds
+Time: 31.461788177490234 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      489,      963,  ..., 24999055,
-                            24999529, 25000000]),
-       col_indices=tensor([   18,   157,   241,  ..., 49747, 49771, 49960]),
-       values=tensor([0.7706, 0.7949, 0.9210,  ..., 0.0962, 0.6322, 0.0053]),
+tensor(crow_indices=tensor([       0,      495,     1024,  ..., 24998992,
+                            24999507, 25000000]),
+       col_indices=tensor([   89,   121,   308,  ..., 49151, 49365, 49592]),
+       values=tensor([0.2016, 0.7170, 0.9969,  ..., 0.6090, 0.1562, 0.9700]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.2729, 0.2896, 0.6966,  ..., 0.6831, 0.4086, 0.6520])
+tensor([0.5426, 0.2677, 0.5298,  ..., 0.8739, 0.7597, 0.2370])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -33,13 +33,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 32.80737018585205 seconds
+Time: 31.461788177490234 seconds
 
-[20.96, 20.68, 20.68, 20.68, 20.72, 20.64, 20.64, 20.76, 20.84, 20.92]
-[20.92, 20.84, 21.32, 22.6, 25.12, 26.44, 26.44, 28.96, 28.92, 32.68, 31.76, 31.16, 31.4, 31.52, 32.12, 34.6, 36.28, 37.8, 37.28, 37.56, 37.96, 37.96, 38.28, 38.36, 37.96, 37.32, 36.48, 36.68, 36.84, 37.44, 37.44, 37.76, 38.0, 39.0, 37.68, 36.84, 37.4, 37.4, 36.8, 37.32, 37.6, 37.72]
-43.14919567108154
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 32.80737018585205, 'TIME_S_1KI': 328.0737018585205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1398.267660713196, 'W': 32.405416577679354}
-[20.96, 20.68, 20.68, 20.68, 20.72, 20.64, 20.64, 20.76, 20.84, 20.92, 21.04, 20.84, 20.72, 20.68, 20.72, 20.6, 20.92, 21.2, 20.96, 20.84]
-374.16
-18.708000000000002
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 32.80737018585205, 'TIME_S_1KI': 328.0737018585205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1398.267660713196, 'W': 32.405416577679354, 'J_1KI': 13982.676607131958, 'W_1KI': 324.0541657767935, 'W_D': 13.697416577679352, 'J_D': 591.0325080986024, 'W_D_1KI': 136.97416577679354, 'J_D_1KI': 1369.7416577679353}
+[20.44, 20.56, 20.8, 20.92, 20.84, 20.88, 20.84, 20.76, 20.8, 20.8]
+[20.72, 20.8, 20.96, 21.88, 23.96, 25.36, 27.04, 27.8, 28.0, 30.52, 30.92, 30.4, 31.04, 31.04, 29.84, 32.08, 33.8, 35.52, 36.96, 37.0, 37.28, 37.72, 38.12, 37.84, 37.64, 36.96, 36.08, 37.0, 37.08, 37.76, 37.16, 37.16, 37.32, 37.92, 38.36, 37.44, 38.12, 38.32, 37.68, 37.96, 37.68, 37.6, 37.44]
+44.320942401885986
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 31.461788177490234, 'TIME_S_1KI': 314.61788177490234, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1422.216083526611, 'W': 32.08903074827424}
+[20.44, 20.56, 20.8, 20.92, 20.84, 20.88, 20.84, 20.76, 20.8, 20.8, 20.72, 20.6, 20.4, 20.56, 20.56, 20.72, 20.76, 20.8, 20.6, 20.68]
+372.72
+18.636000000000003
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 31.461788177490234, 'TIME_S_1KI': 314.61788177490234, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1422.216083526611, 'W': 32.08903074827424, 'J_1KI': 14222.160835266111, 'W_1KI': 320.8903074827424, 'W_D': 13.453030748274237, 'J_D': 596.2510009250636, 'W_D_1KI': 134.53030748274236, 'J_D_1KI': 1345.3030748274234}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.json
index a4de095..0d86cc8 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 19951, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.462696075439453, "TIME_S_1KI": 0.5244196318700542, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 488.71538955688476, "W": 33.3802333465878, "J_1KI": 24.49578414900931, "W_1KI": 1.6731107887618566, "W_D": 15.011233346587801, "J_D": 219.77739569807053, "W_D_1KI": 0.752405059725718, "J_D_1KI": 0.03771264897627778}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 18570, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.544741868972778, "TIME_S_1KI": 0.5678374727502842, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 417.4055347442627, "W": 30.6890173810843, "J_1KI": 22.47741167174274, "W_1KI": 1.652612675341104, "W_D": 12.3270173810843, "J_D": 167.6614541893005, "W_D_1KI": 0.6638135369458428, "J_D_1KI": 0.03574655557058927}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.output
index 06a37e6..1958e0c 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.05949115753173828}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.12336349487304688}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 24999, 25000]),
-       col_indices=tensor([35821, 49411,  3789,  ..., 32092, 27347, 39445]),
-       values=tensor([0.1439, 0.1701, 0.0383,  ..., 0.6521, 0.3755, 0.5678]),
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([10786, 46751, 16470,  ..., 29231, 39472, 27659]),
+       values=tensor([0.1604, 0.9127, 0.3132,  ..., 0.0234, 0.0202, 0.1700]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8709, 0.6173, 0.3475,  ..., 0.7020, 0.1451, 0.7453])
+tensor([0.4453, 0.6856, 0.2424,  ..., 0.5766, 0.9662, 0.5674])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,18 +15,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.05949115753173828 seconds
+Time: 0.12336349487304688 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17649 -ss 50000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.288093090057373}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 8511 -ss 50000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.812279939651489}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     2,  ..., 24999, 25000, 25000]),
-       col_indices=tensor([10903, 22613,  1325,  ...,  4616, 25772, 38217]),
-       values=tensor([0.1548, 0.5404, 0.0562,  ..., 0.6796, 0.5534, 0.6437]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([16974, 41354, 19010,  ..., 27208,  3324, 14724]),
+       values=tensor([0.8188, 0.7622, 0.0432,  ..., 0.2727, 0.7144, 0.5814]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8066, 0.3465, 0.3699,  ..., 0.5654, 0.2544, 0.1290])
+tensor([0.3360, 0.6018, 0.6109,  ..., 0.5049, 0.3747, 0.6381])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,18 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 9.288093090057373 seconds
+Time: 4.812279939651489 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 19951 -ss 50000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.462696075439453}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 18570 -ss 50000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.544741868972778}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 24997, 24998, 25000]),
-       col_indices=tensor([36526, 27522,  9271,  ..., 28337, 20494, 41611]),
-       values=tensor([0.2838, 0.5711, 0.3512,  ..., 0.1758, 0.7475, 0.3339]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([15172, 40157, 23776,  ..., 14049, 32572, 38298]),
+       values=tensor([0.0933, 0.9209, 0.0305,  ..., 0.9069, 0.4875, 0.3887]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9803, 0.0496, 0.4924,  ..., 0.5397, 0.0486, 0.3592])
+tensor([0.5338, 0.8875, 0.4383,  ..., 0.6019, 0.9284, 0.7233])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,15 +53,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.462696075439453 seconds
+Time: 10.544741868972778 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 24997, 24998, 25000]),
-       col_indices=tensor([36526, 27522,  9271,  ..., 28337, 20494, 41611]),
-       values=tensor([0.2838, 0.5711, 0.3512,  ..., 0.1758, 0.7475, 0.3339]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([15172, 40157, 23776,  ..., 14049, 32572, 38298]),
+       values=tensor([0.0933, 0.9209, 0.0305,  ..., 0.9069, 0.4875, 0.3887]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9803, 0.0496, 0.4924,  ..., 0.5397, 0.0486, 0.3592])
+tensor([0.5338, 0.8875, 0.4383,  ..., 0.6019, 0.9284, 0.7233])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -69,13 +69,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.462696075439453 seconds
+Time: 10.544741868972778 seconds
 
-[20.44, 20.56, 20.6, 20.64, 20.64, 20.44, 20.44, 20.44, 20.4, 20.56]
-[20.44, 20.64, 23.28, 24.2, 26.6, 32.52, 37.24, 40.44, 44.28, 44.96, 44.96, 44.92, 44.44, 43.84]
-14.640861988067627
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 19951, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.462696075439453, 'TIME_S_1KI': 0.5244196318700542, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 488.71538955688476, 'W': 33.3802333465878}
-[20.44, 20.56, 20.6, 20.64, 20.64, 20.44, 20.44, 20.44, 20.4, 20.56, 20.32, 20.32, 20.12, 20.24, 20.32, 20.56, 20.52, 20.28, 20.2, 20.0]
-367.38
-18.369
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 19951, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.462696075439453, 'TIME_S_1KI': 0.5244196318700542, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 488.71538955688476, 'W': 33.3802333465878, 'J_1KI': 24.49578414900931, 'W_1KI': 1.6731107887618566, 'W_D': 15.011233346587801, 'J_D': 219.77739569807053, 'W_D_1KI': 0.752405059725718, 'J_D_1KI': 0.03771264897627778}
+[20.52, 20.32, 20.16, 20.16, 20.24, 20.32, 20.44, 20.52, 20.52, 20.28]
+[20.32, 20.36, 21.0, 23.24, 24.96, 30.08, 34.16, 37.96, 40.96, 40.96, 42.56, 43.04, 43.56]
+13.601137161254883
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 18570, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.544741868972778, 'TIME_S_1KI': 0.5678374727502842, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 417.4055347442627, 'W': 30.6890173810843}
+[20.52, 20.32, 20.16, 20.16, 20.24, 20.32, 20.44, 20.52, 20.52, 20.28, 20.4, 20.4, 20.36, 20.28, 20.56, 20.56, 20.64, 20.52, 20.4, 20.48]
+367.24
+18.362000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 18570, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.544741868972778, 'TIME_S_1KI': 0.5678374727502842, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 417.4055347442627, 'W': 30.6890173810843, 'J_1KI': 22.47741167174274, 'W_1KI': 1.652612675341104, 'W_D': 12.3270173810843, 'J_D': 167.6614541893005, 'W_D_1KI': 0.6638135369458428, 'J_D_1KI': 0.03574655557058927}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.json
index cff7542..d86e43a 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 6322, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.657772779464722, "TIME_S_1KI": 1.68582296416715, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 425.8721401214599, "W": 31.344652680689325, "J_1KI": 67.3635147297469, "W_1KI": 4.958027946961298, "W_D": 12.573652680689325, "J_D": 170.83514789009087, "W_D_1KI": 1.9888726163697126, "J_D_1KI": 0.31459547870447846}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 6116, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.511497974395752, "TIME_S_1KI": 1.7186883542177487, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 426.78555660247804, "W": 31.380359239851575, "J_1KI": 69.78181108608209, "W_1KI": 5.130863185064025, "W_D": 12.985359239851572, "J_D": 176.60612896442407, "W_D_1KI": 2.1231784237821407, "J_D_1KI": 0.3471514754385449}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.output
index 57f8461..0c1ead7 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_50000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.3423471450805664}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.17166757583618164}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      7,  ..., 124998, 124999,
+tensor(crow_indices=tensor([     0,      1,      2,  ..., 124999, 124999,
                             125000]),
-       col_indices=tensor([  303, 26221, 28347,  ...,  8622, 14261,  4291]),
-       values=tensor([0.9240, 0.5223, 0.0365,  ..., 0.6044, 0.0072, 0.5479]),
+       col_indices=tensor([45961, 10128,  6506,  ..., 18491, 35128, 14957]),
+       values=tensor([0.8466, 0.2605, 0.1046,  ..., 0.4924, 0.9420, 0.7185]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.1523, 0.9417, 0.1754,  ..., 0.6908, 0.2427, 0.5501])
+tensor([0.3816, 0.8434, 0.3348,  ..., 0.6276, 0.4301, 0.2374])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 0.3423471450805664 seconds
+Time: 0.17166757583618164 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3067 -ss 50000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 5.0935986042022705}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 6116 -ss 50000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.511497974395752}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      3,  ..., 124997, 125000,
+tensor(crow_indices=tensor([     0,      4,      8,  ..., 124988, 124994,
                             125000]),
-       col_indices=tensor([ 1194,  5034,  6320,  ..., 11179, 21504, 33093]),
-       values=tensor([0.7209, 0.3055, 0.4482,  ..., 0.3076, 0.8643, 0.0918]),
+       col_indices=tensor([ 1782, 22893, 30856,  ..., 16042, 33293, 42752]),
+       values=tensor([0.7242, 0.8386, 0.5964,  ..., 0.7254, 0.7751, 0.2503]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.9680, 0.6265, 0.9723,  ..., 0.1304, 0.1284, 0.7215])
+tensor([0.0331, 0.9093, 0.3340,  ..., 0.9159, 0.0637, 0.5700])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 5.0935986042022705 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 6322 -ss 50000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.657772779464722}
+Time: 10.511497974395752 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      2,  ..., 124992, 124996,
+tensor(crow_indices=tensor([     0,      4,      8,  ..., 124988, 124994,
                             125000]),
-       col_indices=tensor([41720,  5446, 23409,  ..., 23991, 37197, 42632]),
-       values=tensor([0.7857, 0.2010, 0.0929,  ..., 0.8446, 0.3352, 0.3559]),
+       col_indices=tensor([ 1782, 22893, 30856,  ..., 16042, 33293, 42752]),
+       values=tensor([0.7242, 0.8386, 0.5964,  ..., 0.7254, 0.7751, 0.2503]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.5851, 0.1828, 0.1733,  ..., 0.7326, 0.4663, 0.8685])
+tensor([0.0331, 0.9093, 0.3340,  ..., 0.9159, 0.0637, 0.5700])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,30 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.657772779464722 seconds
+Time: 10.511497974395752 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      2,  ..., 124992, 124996,
-                            125000]),
-       col_indices=tensor([41720,  5446, 23409,  ..., 23991, 37197, 42632]),
-       values=tensor([0.7857, 0.2010, 0.0929,  ..., 0.8446, 0.3352, 0.3559]),
-       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.5851, 0.1828, 0.1733,  ..., 0.7326, 0.4663, 0.8685])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 125000
-Density: 5e-05
-Time: 10.657772779464722 seconds
-
-[20.44, 20.32, 20.44, 20.56, 20.76, 20.84, 20.88, 21.04, 21.08, 21.08]
-[21.08, 20.88, 20.92, 24.6, 25.64, 31.32, 35.8, 37.92, 41.28, 43.2, 43.12, 43.4, 43.48]
-13.586755752563477
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 6322, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.657772779464722, 'TIME_S_1KI': 1.68582296416715, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 425.8721401214599, 'W': 31.344652680689325}
-[20.44, 20.32, 20.44, 20.56, 20.76, 20.84, 20.88, 21.04, 21.08, 21.08, 20.84, 20.88, 20.84, 20.72, 20.96, 20.96, 21.08, 21.16, 21.2, 21.04]
-375.42
-18.771
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 6322, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.657772779464722, 'TIME_S_1KI': 1.68582296416715, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 425.8721401214599, 'W': 31.344652680689325, 'J_1KI': 67.3635147297469, 'W_1KI': 4.958027946961298, 'W_D': 12.573652680689325, 'J_D': 170.83514789009087, 'W_D_1KI': 1.9888726163697126, 'J_D_1KI': 0.31459547870447846}
+[20.36, 20.36, 20.4, 20.12, 20.08, 20.2, 20.32, 20.4, 20.72, 20.6]
+[20.6, 20.8, 20.68, 23.76, 24.96, 29.8, 35.08, 39.36, 41.64, 44.0, 44.2, 44.36, 43.48]
+13.600403785705566
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 6116, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.511497974395752, 'TIME_S_1KI': 1.7186883542177487, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 426.78555660247804, 'W': 31.380359239851575}
+[20.36, 20.36, 20.4, 20.12, 20.08, 20.2, 20.32, 20.4, 20.72, 20.6, 20.68, 20.68, 20.56, 20.64, 20.6, 20.52, 20.52, 20.4, 20.44, 20.24]
+367.90000000000003
+18.395000000000003
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 6116, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.511497974395752, 'TIME_S_1KI': 1.7186883542177487, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 426.78555660247804, 'W': 31.380359239851575, 'J_1KI': 69.78181108608209, 'W_1KI': 5.130863185064025, 'W_D': 12.985359239851572, 'J_D': 176.60612896442407, 'W_D_1KI': 2.1231784237821407, 'J_D_1KI': 0.3471514754385449}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.json
index 3609346..9b01442 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 98325, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.519834756851196, "TIME_S_1KI": 0.10699043739487614, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 316.21768825531, "W": 22.200046075478067, "J_1KI": 3.216045647142741, "W_1KI": 0.22578231452304162, "W_D": 3.72304607547807, "J_D": 53.03110719919203, "W_D_1KI": 0.03786469438574187, "J_D_1KI": 0.0003850973240350051}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 94617, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.157525777816772, "TIME_S_1KI": 0.10735413062997952, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 323.48465412139893, "W": 22.672925840081955, "J_1KI": 3.4188851276345575, "W_1KI": 0.23962845831174057, "W_D": 4.139925840081958, "J_D": 59.066151757955616, "W_D_1KI": 0.043754566727775744, "J_D_1KI": 0.000462438744916619}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.output
index 555507d..924f8be 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.018892765045166016}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.01822948455810547}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([3456, 1605,  749,  ..., 2516,  837, 4620]),
-       values=tensor([0.8429, 0.4221, 0.2092,  ..., 0.3256, 0.3578, 0.9398]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([3941,  541, 1824,  ...,  314, 1337, 2093]),
+       values=tensor([0.8128, 0.3162, 0.2437,  ..., 0.1898, 0.9919, 0.9815]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.1595, 0.2560, 0.8545,  ..., 0.4673, 0.4412, 0.6412])
+tensor([0.0259, 0.2275, 0.6989,  ..., 0.5897, 0.4789, 0.5857])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,38 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.018892765045166016 seconds
+Time: 0.01822948455810547 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 55576 -ss 5000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.934850692749023}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 57598 -ss 5000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.391798257827759}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    2,    2,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([2304, 3497, 2599,  ..., 3517, 2336, 3180]),
-       values=tensor([7.9793e-01, 1.3489e-04, 7.1193e-01,  ...,
-                      7.4115e-01, 8.0632e-01, 9.8789e-03]), size=(5000, 5000),
-       nnz=2500, layout=torch.sparse_csr)
-tensor([0.4232, 0.5545, 0.0889,  ..., 0.2237, 0.6245, 0.5041])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 2500
-Density: 0.0001
-Time: 5.934850692749023 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 98325 -ss 5000 -sd 0.0001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.519834756851196}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([ 417, 1523, 4116,  ..., 1599, 2107, 3220]),
-       values=tensor([0.7284, 0.4903, 0.1270,  ..., 0.3684, 0.2323, 0.2388]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([2397, 3853, 4072,  ..., 1513, 2142, 2593]),
+       values=tensor([0.3690, 0.1022, 0.2006,  ..., 0.2544, 0.7570, 0.9378]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.8570, 0.2399, 0.2271,  ..., 0.1785, 0.2270, 0.3588])
+tensor([0.3198, 0.5308, 0.2405,  ..., 0.6744, 0.2635, 0.8401])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -54,15 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.519834756851196 seconds
+Time: 6.391798257827759 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 94617 -ss 5000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.157525777816772}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([ 417, 1523, 4116,  ..., 1599, 2107, 3220]),
-       values=tensor([0.7284, 0.4903, 0.1270,  ..., 0.3684, 0.2323, 0.2388]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([ 413, 1888, 3337,  ..., 2077, 3725,  374]),
+       values=tensor([0.2969, 0.4624, 0.7154,  ..., 0.0630, 0.2523, 0.5001]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.8570, 0.2399, 0.2271,  ..., 0.1785, 0.2270, 0.3588])
+tensor([0.4946, 0.1451, 0.8446,  ..., 0.8105, 0.4300, 0.6554])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -70,13 +53,29 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.519834756851196 seconds
+Time: 10.157525777816772 seconds
 
-[20.24, 20.24, 20.16, 20.08, 20.32, 20.6, 20.6, 20.56, 20.52, 20.6]
-[20.68, 20.8, 21.12, 22.88, 24.6, 25.72, 26.32, 26.12, 25.12, 25.12, 23.36, 23.52, 23.52, 23.76]
-14.24401044845581
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 98325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.519834756851196, 'TIME_S_1KI': 0.10699043739487614, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.21768825531, 'W': 22.200046075478067}
-[20.24, 20.24, 20.16, 20.08, 20.32, 20.6, 20.6, 20.56, 20.52, 20.6, 20.68, 20.68, 20.72, 20.52, 20.44, 20.56, 20.8, 20.84, 20.76, 20.76]
-369.53999999999996
-18.476999999999997
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 98325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.519834756851196, 'TIME_S_1KI': 0.10699043739487614, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.21768825531, 'W': 22.200046075478067, 'J_1KI': 3.216045647142741, 'W_1KI': 0.22578231452304162, 'W_D': 3.72304607547807, 'J_D': 53.03110719919203, 'W_D_1KI': 0.03786469438574187, 'J_D_1KI': 0.0003850973240350051}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([ 413, 1888, 3337,  ..., 2077, 3725,  374]),
+       values=tensor([0.2969, 0.4624, 0.7154,  ..., 0.0630, 0.2523, 0.5001]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.4946, 0.1451, 0.8446,  ..., 0.8105, 0.4300, 0.6554])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.157525777816772 seconds
+
+[20.24, 20.04, 20.12, 20.56, 20.56, 20.32, 20.44, 20.84, 20.8, 20.48]
+[20.56, 20.44, 20.48, 24.24, 26.4, 28.08, 28.6, 25.84, 25.84, 25.64, 23.24, 23.28, 23.32, 23.2]
+14.267441987991333
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 94617, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.157525777816772, 'TIME_S_1KI': 0.10735413062997952, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 323.48465412139893, 'W': 22.672925840081955}
+[20.24, 20.04, 20.12, 20.56, 20.56, 20.32, 20.44, 20.84, 20.8, 20.48, 20.96, 20.96, 20.92, 20.56, 20.64, 20.64, 20.76, 20.68, 20.68, 20.6]
+370.65999999999997
+18.532999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 94617, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.157525777816772, 'TIME_S_1KI': 0.10735413062997952, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 323.48465412139893, 'W': 22.672925840081955, 'J_1KI': 3.4188851276345575, 'W_1KI': 0.23962845831174057, 'W_D': 4.139925840081958, 'J_D': 59.066151757955616, 'W_D_1KI': 0.043754566727775744, 'J_D_1KI': 0.000462438744916619}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.json
index 2fa1f21..3aa279b 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 17780, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.605318784713745, "TIME_S_1KI": 0.5964746223123591, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 313.57909806251524, "W": 22.019197605482734, "J_1KI": 17.636619688555413, "W_1KI": 1.2384250621756319, "W_D": 3.4661976054827335, "J_D": 49.36270332407948, "W_D_1KI": 0.19494924665257218, "J_D_1KI": 0.010964524558637355}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 17204, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.812280178070068, "TIME_S_1KI": 0.6284747836590368, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 298.97844259262087, "W": 22.60552041406754, "J_1KI": 17.378426098152804, "W_1KI": 1.313968868522875, "W_D": 4.147520414067543, "J_D": 54.85470678424838, "W_D_1KI": 0.24107884294742754, "J_D_1KI": 0.014012952972996253}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.output
index 8d3b43b..66b8fcd 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06766819953918457}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.09629178047180176}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     7,    15,  ..., 24992, 24996, 25000]),
-       col_indices=tensor([ 734,  800, 1880,  ..., 3125, 3280, 3794]),
-       values=tensor([0.0540, 0.4911, 0.3592,  ..., 0.2590, 0.5736, 0.3057]),
+tensor(crow_indices=tensor([    0,     4,    13,  ..., 24984, 24991, 25000]),
+       col_indices=tensor([ 906, 2302, 2949,  ..., 4312, 4401, 4762]),
+       values=tensor([0.3111, 0.1904, 0.6965,  ..., 0.8141, 0.8187, 0.4745]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9823, 0.9343, 0.9377,  ..., 0.0786, 0.0908, 0.1511])
+tensor([0.5510, 0.4831, 0.5270,  ..., 0.8910, 0.7950, 0.2815])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.06766819953918457 seconds
+Time: 0.09629178047180176 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 15516 -ss 5000 -sd 0.001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.162637948989868}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10904 -ss 5000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 6.654679298400879}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     8,    11,  ..., 24988, 24995, 25000]),
-       col_indices=tensor([  62,  227,  575,  ..., 2337, 2631, 3700]),
-       values=tensor([0.5265, 0.4146, 0.5026,  ..., 0.0706, 0.1241, 0.5991]),
+tensor(crow_indices=tensor([    0,    10,    18,  ..., 24990, 24995, 25000]),
+       col_indices=tensor([ 952, 1438, 2146,  ..., 2644, 3063, 3989]),
+       values=tensor([0.1223, 0.6009, 0.2905,  ..., 0.8222, 0.7018, 0.0633]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.6610, 0.4053, 0.0257,  ..., 0.7779, 0.2973, 0.6422])
+tensor([0.1062, 0.0133, 0.7952,  ..., 0.7261, 0.0237, 0.2753])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 9.162637948989868 seconds
+Time: 6.654679298400879 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17780 -ss 5000 -sd 0.001 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.605318784713745}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17204 -ss 5000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.812280178070068}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     6,     8,  ..., 24994, 24997, 25000]),
-       col_indices=tensor([ 423, 1662, 2124,  ...,  288, 1379, 2658]),
-       values=tensor([0.1096, 0.1453, 0.3978,  ..., 0.4089, 0.5724, 0.6122]),
+tensor(crow_indices=tensor([    0,     5,     9,  ..., 24988, 24995, 25000]),
+       col_indices=tensor([ 489,  693, 1351,  ..., 1645, 2689, 3021]),
+       values=tensor([0.1026, 0.4246, 0.1164,  ..., 0.2742, 0.6330, 0.5157]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.2174, 0.6127, 0.5782,  ..., 0.6057, 0.7055, 0.7233])
+tensor([0.3786, 0.4087, 0.7676,  ..., 0.4045, 0.0617, 0.3244])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.605318784713745 seconds
+Time: 10.812280178070068 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     6,     8,  ..., 24994, 24997, 25000]),
-       col_indices=tensor([ 423, 1662, 2124,  ...,  288, 1379, 2658]),
-       values=tensor([0.1096, 0.1453, 0.3978,  ..., 0.4089, 0.5724, 0.6122]),
+tensor(crow_indices=tensor([    0,     5,     9,  ..., 24988, 24995, 25000]),
+       col_indices=tensor([ 489,  693, 1351,  ..., 1645, 2689, 3021]),
+       values=tensor([0.1026, 0.4246, 0.1164,  ..., 0.2742, 0.6330, 0.5157]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.2174, 0.6127, 0.5782,  ..., 0.6057, 0.7055, 0.7233])
+tensor([0.3786, 0.4087, 0.7676,  ..., 0.4045, 0.0617, 0.3244])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.605318784713745 seconds
+Time: 10.812280178070068 seconds
 
-[20.64, 20.56, 20.28, 20.4, 20.28, 20.28, 20.36, 20.44, 20.4, 20.4]
-[20.64, 20.56, 20.76, 22.16, 23.52, 25.36, 26.08, 26.2, 25.48, 23.84, 23.92, 23.84, 23.84, 23.88]
-14.24116826057434
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 17780, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.605318784713745, 'TIME_S_1KI': 0.5964746223123591, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 313.57909806251524, 'W': 22.019197605482734}
-[20.64, 20.56, 20.28, 20.4, 20.28, 20.28, 20.36, 20.44, 20.4, 20.4, 20.84, 20.68, 20.92, 20.96, 20.88, 20.88, 21.0, 20.8, 20.68, 20.64]
-371.06
-18.553
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 17780, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.605318784713745, 'TIME_S_1KI': 0.5964746223123591, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 313.57909806251524, 'W': 22.019197605482734, 'J_1KI': 17.636619688555413, 'W_1KI': 1.2384250621756319, 'W_D': 3.4661976054827335, 'J_D': 49.36270332407948, 'W_D_1KI': 0.19494924665257218, 'J_D_1KI': 0.010964524558637355}
+[20.44, 20.32, 20.44, 20.32, 20.32, 20.32, 20.44, 20.52, 20.44, 20.56]
+[20.88, 20.92, 21.12, 24.88, 27.0, 27.52, 27.92, 25.04, 25.04, 24.88, 23.4, 23.6, 23.72]
+13.225903987884521
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 17204, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.812280178070068, 'TIME_S_1KI': 0.6284747836590368, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 298.97844259262087, 'W': 22.60552041406754}
+[20.44, 20.32, 20.44, 20.32, 20.32, 20.32, 20.44, 20.52, 20.44, 20.56, 20.24, 20.48, 20.56, 20.44, 20.64, 20.76, 20.72, 20.56, 20.84, 20.84]
+369.15999999999997
+18.458
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 17204, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.812280178070068, 'TIME_S_1KI': 0.6284747836590368, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 298.97844259262087, 'W': 22.60552041406754, 'J_1KI': 17.378426098152804, 'W_1KI': 1.313968868522875, 'W_D': 4.147520414067543, 'J_D': 54.85470678424838, 'W_D_1KI': 0.24107884294742754, 'J_D_1KI': 0.014012952972996253}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.json
index 84660f7..e540484 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1921, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.388477563858032, "TIME_S_1KI": 5.407848809920892, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 312.91259566307076, "W": 21.966357313024826, "J_1KI": 162.89047145396708, "W_1KI": 11.434855446655297, "W_D": 3.429357313024827, "J_D": 48.85148151707659, "W_D_1KI": 1.785193812089967, "J_D_1KI": 0.9293044310723411}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1947, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.529423713684082, "TIME_S_1KI": 5.408024506257875, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 315.91934822082516, "W": 22.150260863076344, "J_1KI": 162.2595522449025, "W_1KI": 11.376610612776755, "W_D": 3.727260863076342, "J_D": 53.1602688469886, "W_D_1KI": 1.9143609979847676, "J_D_1KI": 0.9832362598791822}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.output
index 1a4afe9..7d819b0 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.01 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.593717098236084}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.5951905250549316}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     39,     88,  ..., 249897, 249951,
+tensor(crow_indices=tensor([     0,     65,    114,  ..., 249905, 249943,
                             250000]),
-       col_indices=tensor([   1,   41,  120,  ..., 4868, 4902, 4963]),
-       values=tensor([0.6487, 0.6379, 0.3189,  ..., 0.3941, 0.1960, 0.9453]),
+       col_indices=tensor([ 243,  360,  569,  ..., 4673, 4859, 4971]),
+       values=tensor([0.9327, 0.0572, 0.1694,  ..., 0.7766, 0.8178, 0.1277]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.9493, 0.7713, 0.4212,  ..., 0.5345, 0.1694, 0.1229])
+tensor([0.3631, 0.7053, 0.5115,  ..., 0.9329, 0.3771, 0.9303])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 0.593717098236084 seconds
+Time: 0.5951905250549316 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1768 -ss 5000 -sd 0.01 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.65909719467163}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1764 -ss 5000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.50925087928772}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     53,    105,  ..., 249907, 249948,
+tensor(crow_indices=tensor([     0,     39,     87,  ..., 249897, 249952,
                             250000]),
-       col_indices=tensor([ 103,  261,  471,  ..., 4857, 4933, 4959]),
-       values=tensor([0.8889, 0.3073, 0.1638,  ..., 0.6109, 0.3049, 0.0052]),
+       col_indices=tensor([  15,  176,  253,  ..., 4823, 4923, 4997]),
+       values=tensor([0.4394, 0.7426, 0.7011,  ..., 0.9433, 0.0282, 0.2442]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5335, 0.0728, 0.9615,  ..., 0.8926, 0.1348, 0.8188])
+tensor([0.3323, 0.0904, 0.5554,  ..., 0.0808, 0.6293, 0.6246])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 9.65909719467163 seconds
+Time: 9.50925087928772 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1921 -ss 5000 -sd 0.01 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.388477563858032}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1947 -ss 5000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.529423713684082}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     65,     98,  ..., 249897, 249948,
+tensor(crow_indices=tensor([     0,     42,     84,  ..., 249908, 249963,
                             250000]),
-       col_indices=tensor([ 141,  179,  219,  ..., 4719, 4923, 4985]),
-       values=tensor([0.6589, 0.9882, 0.9555,  ..., 0.3007, 0.0365, 0.3378]),
+       col_indices=tensor([  42,  271,  489,  ..., 4615, 4714, 4994]),
+       values=tensor([0.3823, 0.0445, 0.0088,  ..., 0.5571, 0.6706, 0.4440]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.9859, 0.3282, 0.7924,  ..., 0.6550, 0.5905, 0.4141])
+tensor([0.5611, 0.2970, 0.3578,  ..., 0.8362, 0.6526, 0.5800])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.388477563858032 seconds
+Time: 10.529423713684082 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     65,     98,  ..., 249897, 249948,
+tensor(crow_indices=tensor([     0,     42,     84,  ..., 249908, 249963,
                             250000]),
-       col_indices=tensor([ 141,  179,  219,  ..., 4719, 4923, 4985]),
-       values=tensor([0.6589, 0.9882, 0.9555,  ..., 0.3007, 0.0365, 0.3378]),
+       col_indices=tensor([  42,  271,  489,  ..., 4615, 4714, 4994]),
+       values=tensor([0.3823, 0.0445, 0.0088,  ..., 0.5571, 0.6706, 0.4440]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.9859, 0.3282, 0.7924,  ..., 0.6550, 0.5905, 0.4141])
+tensor([0.5611, 0.2970, 0.3578,  ..., 0.8362, 0.6526, 0.5800])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.388477563858032 seconds
+Time: 10.529423713684082 seconds
 
-[21.12, 20.84, 20.6, 20.44, 20.44, 20.44, 20.52, 20.52, 20.6, 20.96]
-[20.8, 20.8, 20.76, 21.68, 22.28, 24.84, 25.84, 26.2, 25.88, 24.96, 23.84, 23.92, 23.72, 23.88]
-14.245083570480347
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1921, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.388477563858032, 'TIME_S_1KI': 5.407848809920892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 312.91259566307076, 'W': 21.966357313024826}
-[21.12, 20.84, 20.6, 20.44, 20.44, 20.44, 20.52, 20.52, 20.6, 20.96, 20.44, 20.44, 20.44, 20.48, 20.56, 20.68, 20.68, 20.56, 20.84, 20.8]
-370.74
-18.537
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1921, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.388477563858032, 'TIME_S_1KI': 5.407848809920892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 312.91259566307076, 'W': 21.966357313024826, 'J_1KI': 162.89047145396708, 'W_1KI': 11.434855446655297, 'W_D': 3.429357313024827, 'J_D': 48.85148151707659, 'W_D_1KI': 1.785193812089967, 'J_D_1KI': 0.9293044310723411}
+[20.36, 20.36, 20.48, 20.6, 20.24, 20.4, 20.28, 20.28, 20.44, 20.48]
+[20.68, 20.84, 20.84, 22.12, 23.2, 25.6, 26.2, 26.4, 25.76, 24.84, 23.88, 23.88, 23.84, 23.84]
+14.262556552886963
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1947, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.529423713684082, 'TIME_S_1KI': 5.408024506257875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.91934822082516, 'W': 22.150260863076344}
+[20.36, 20.36, 20.48, 20.6, 20.24, 20.4, 20.28, 20.28, 20.44, 20.48, 20.52, 20.48, 20.48, 20.68, 20.72, 20.68, 20.72, 20.36, 20.44, 20.28]
+368.46000000000004
+18.423000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1947, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.529423713684082, 'TIME_S_1KI': 5.408024506257875, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.91934822082516, 'W': 22.150260863076344, 'J_1KI': 162.2595522449025, 'W_1KI': 11.376610612776755, 'W_D': 3.727260863076342, 'J_D': 53.1602688469886, 'W_D_1KI': 1.9143609979847676, 'J_D_1KI': 0.9832362598791822}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.json
index 50afdc0..21e4ae9 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 396, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.481398582458496, "TIME_S_1KI": 26.468178238531554, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 323.34643746376037, "W": 22.685982650996596, "J_1KI": 816.5314077367685, "W_1KI": 57.28783497726413, "W_D": 4.214982650996596, "J_D": 60.07672866272925, "W_D_1KI": 10.643895583324738, "J_D_1KI": 26.878524200314995}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 389, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.3775634765625, "TIME_S_1KI": 26.677541070854755, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 318.5190047359467, "W": 22.32677308088455, "J_1KI": 818.814922200377, "W_1KI": 57.39530354983175, "W_D": 3.7537730808845495, "J_D": 53.55221112322808, "W_D_1KI": 9.649802264484704, "J_D_1KI": 24.806689625924687}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.output
index 07e41de..4a32e24 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.6507530212402344}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.6972105503082275}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     267,     531,  ..., 1249531,
-                            1249748, 1250000]),
-       col_indices=tensor([  12,   24,   45,  ..., 4958, 4983, 4986]),
-       values=tensor([0.7384, 0.2434, 0.0755,  ..., 0.4736, 0.1384, 0.4678]),
+tensor(crow_indices=tensor([      0,     225,     478,  ..., 1249494,
+                            1249751, 1250000]),
+       col_indices=tensor([  16,   28,   30,  ..., 4980, 4993, 4996]),
+       values=tensor([0.5189, 0.5626, 0.5403,  ..., 0.3379, 0.7660, 0.2903]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.2921, 0.5624, 0.4015,  ..., 0.8005, 0.9400, 0.6114])
+tensor([0.5904, 0.1736, 0.4962,  ..., 0.6657, 0.7668, 0.4097])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 2.6507530212402344 seconds
+Time: 2.6972105503082275 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 396 -ss 5000 -sd 0.05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.481398582458496}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 389 -ss 5000 -sd 0.05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.3775634765625}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     247,     505,  ..., 1249488,
-                            1249757, 1250000]),
-       col_indices=tensor([  27,   35,   41,  ..., 4930, 4938, 4952]),
-       values=tensor([0.8294, 0.9821, 0.6691,  ..., 0.3905, 0.4873, 0.1672]),
+tensor(crow_indices=tensor([      0,     232,     472,  ..., 1249492,
+                            1249758, 1250000]),
+       col_indices=tensor([  16,   29,   37,  ..., 4970, 4975, 4986]),
+       values=tensor([0.2042, 0.0894, 0.5998,  ..., 0.8932, 0.3489, 0.1528]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.8352, 0.4457, 0.1150,  ..., 0.9988, 0.2164, 0.9018])
+tensor([0.6726, 0.9286, 0.4083,  ..., 0.5149, 0.9750, 0.2214])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.481398582458496 seconds
+Time: 10.3775634765625 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     247,     505,  ..., 1249488,
-                            1249757, 1250000]),
-       col_indices=tensor([  27,   35,   41,  ..., 4930, 4938, 4952]),
-       values=tensor([0.8294, 0.9821, 0.6691,  ..., 0.3905, 0.4873, 0.1672]),
+tensor(crow_indices=tensor([      0,     232,     472,  ..., 1249492,
+                            1249758, 1250000]),
+       col_indices=tensor([  16,   29,   37,  ..., 4970, 4975, 4986]),
+       values=tensor([0.2042, 0.0894, 0.5998,  ..., 0.8932, 0.3489, 0.1528]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.8352, 0.4457, 0.1150,  ..., 0.9988, 0.2164, 0.9018])
+tensor([0.6726, 0.9286, 0.4083,  ..., 0.5149, 0.9750, 0.2214])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.481398582458496 seconds
+Time: 10.3775634765625 seconds
 
-[20.72, 20.84, 20.92, 20.68, 20.6, 20.44, 20.48, 20.24, 20.44, 20.24]
-[20.24, 20.52, 20.72, 22.36, 24.28, 26.72, 27.0, 27.56, 26.28, 25.88, 24.68, 24.52, 24.36, 24.36]
-14.253137826919556
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 396, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.481398582458496, 'TIME_S_1KI': 26.468178238531554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 323.34643746376037, 'W': 22.685982650996596}
-[20.72, 20.84, 20.92, 20.68, 20.6, 20.44, 20.48, 20.24, 20.44, 20.24, 20.24, 20.24, 20.36, 20.48, 20.48, 20.6, 20.68, 20.64, 20.4, 20.6]
-369.42
-18.471
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 396, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.481398582458496, 'TIME_S_1KI': 26.468178238531554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 323.34643746376037, 'W': 22.685982650996596, 'J_1KI': 816.5314077367685, 'W_1KI': 57.28783497726413, 'W_D': 4.214982650996596, 'J_D': 60.07672866272925, 'W_D_1KI': 10.643895583324738, 'J_D_1KI': 26.878524200314995}
+[20.68, 20.56, 20.56, 20.68, 20.88, 20.88, 21.08, 21.08, 20.76, 20.64]
+[20.64, 20.48, 20.48, 21.84, 22.92, 26.08, 26.72, 27.12, 26.56, 25.64, 24.0, 23.96, 24.04, 23.96]
+14.266235589981079
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 389, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.3775634765625, 'TIME_S_1KI': 26.677541070854755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.5190047359467, 'W': 22.32677308088455}
+[20.68, 20.56, 20.56, 20.68, 20.88, 20.88, 21.08, 21.08, 20.76, 20.64, 20.12, 20.08, 20.2, 20.2, 20.28, 20.64, 20.8, 20.8, 20.88, 20.76]
+371.46000000000004
+18.573
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 389, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.3775634765625, 'TIME_S_1KI': 26.677541070854755, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.5190047359467, 'W': 22.32677308088455, 'J_1KI': 818.814922200377, 'W_1KI': 57.39530354983175, 'W_D': 3.7537730808845495, 'J_D': 53.55221112322808, 'W_D_1KI': 9.649802264484704, 'J_D_1KI': 24.806689625924687}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.json
index 721e6aa..6d6710b 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 199, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.477930784225464, "TIME_S_1KI": 52.6529185136958, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 366.7794834327698, "W": 23.985504753820422, "J_1KI": 1843.11298207422, "W_1KI": 120.53017464231368, "W_D": 5.225504753820424, "J_D": 79.90692520141607, "W_D_1KI": 26.25881785839409, "J_D_1KI": 131.95385858489493}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 197, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.158027410507202, "TIME_S_1KI": 56.639733048259906, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 389.69751098632815, "W": 25.31289781132327, "J_1KI": 1978.1599542453205, "W_1KI": 128.49186706255466, "W_D": 4.1918978113232725, "J_D": 64.53516920733456, "W_D_1KI": 21.278669093011537, "J_D_1KI": 108.01354869549004}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.output
index a6a4a54..943df46 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.1 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.267488241195679}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.311717987060547}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     489,     972,  ..., 2499002,
-                            2499515, 2500000]),
-       col_indices=tensor([   0,    4,   21,  ..., 4965, 4988, 4998]),
-       values=tensor([0.4985, 0.2439, 0.0801,  ..., 0.3726, 0.6532, 0.2308]),
+tensor(crow_indices=tensor([      0,     531,     990,  ..., 2498988,
+                            2499517, 2500000]),
+       col_indices=tensor([   7,    8,   15,  ..., 4991, 4995, 4997]),
+       values=tensor([0.8258, 0.3761, 0.6668,  ..., 0.9013, 0.8515, 0.3086]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7620, 0.1310, 0.6898,  ..., 0.4324, 0.6267, 0.4614])
+tensor([0.4193, 0.7332, 0.2050,  ..., 0.9758, 0.2089, 0.8589])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 5.267488241195679 seconds
+Time: 5.311717987060547 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 199 -ss 5000 -sd 0.1 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.477930784225464}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 197 -ss 5000 -sd 0.1 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.158027410507202}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     479,     980,  ..., 2498983,
-                            2499479, 2500000]),
-       col_indices=tensor([   7,   13,   23,  ..., 4987, 4988, 4998]),
-       values=tensor([0.4519, 0.3203, 0.6830,  ..., 0.2361, 0.6866, 0.7928]),
+tensor(crow_indices=tensor([      0,     464,     986,  ..., 2499032,
+                            2499496, 2500000]),
+       col_indices=tensor([   8,   39,   48,  ..., 4964, 4987, 4997]),
+       values=tensor([0.1348, 0.8243, 0.3544,  ..., 0.8528, 0.5821, 0.2231]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4502, 0.7188, 0.8112,  ..., 0.2797, 0.2285, 0.9848])
+tensor([0.5491, 0.2706, 0.0646,  ..., 0.7967, 0.8767, 0.7030])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.477930784225464 seconds
+Time: 11.158027410507202 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     479,     980,  ..., 2498983,
-                            2499479, 2500000]),
-       col_indices=tensor([   7,   13,   23,  ..., 4987, 4988, 4998]),
-       values=tensor([0.4519, 0.3203, 0.6830,  ..., 0.2361, 0.6866, 0.7928]),
+tensor(crow_indices=tensor([      0,     464,     986,  ..., 2499032,
+                            2499496, 2500000]),
+       col_indices=tensor([   8,   39,   48,  ..., 4964, 4987, 4997]),
+       values=tensor([0.1348, 0.8243, 0.3544,  ..., 0.8528, 0.5821, 0.2231]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4502, 0.7188, 0.8112,  ..., 0.2797, 0.2285, 0.9848])
+tensor([0.5491, 0.2706, 0.0646,  ..., 0.7967, 0.8767, 0.7030])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.477930784225464 seconds
+Time: 11.158027410507202 seconds
 
-[20.56, 20.84, 21.36, 21.88, 21.64, 21.6, 21.4, 20.76, 20.76, 20.72]
-[21.16, 21.2, 21.4, 25.96, 27.32, 30.6, 31.24, 28.68, 27.4, 26.08, 24.2, 24.2, 24.4, 24.36, 24.2]
-15.291714191436768
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 199, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.477930784225464, 'TIME_S_1KI': 52.6529185136958, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 366.7794834327698, 'W': 23.985504753820422}
-[20.56, 20.84, 21.36, 21.88, 21.64, 21.6, 21.4, 20.76, 20.76, 20.72, 20.44, 20.32, 20.44, 20.64, 20.64, 20.44, 20.4, 20.36, 20.44, 20.84]
-375.2
-18.759999999999998
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 199, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.477930784225464, 'TIME_S_1KI': 52.6529185136958, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 366.7794834327698, 'W': 23.985504753820422, 'J_1KI': 1843.11298207422, 'W_1KI': 120.53017464231368, 'W_D': 5.225504753820424, 'J_D': 79.90692520141607, 'W_D_1KI': 26.25881785839409, 'J_D_1KI': 131.95385858489493}
+[20.6, 20.28, 20.4, 20.48, 20.32, 21.48, 22.08, 23.12, 23.12, 23.84]
+[23.96, 23.84, 24.08, 24.8, 25.88, 28.92, 30.16, 31.32, 32.12, 30.04, 27.68, 26.28, 24.84, 24.84, 25.6]
+15.395215272903442
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.158027410507202, 'TIME_S_1KI': 56.639733048259906, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 389.69751098632815, 'W': 25.31289781132327}
+[20.6, 20.28, 20.4, 20.48, 20.32, 21.48, 22.08, 23.12, 23.12, 23.84, 25.76, 25.88, 25.56, 26.0, 25.76, 25.48, 25.44, 25.04, 24.68, 24.4]
+422.41999999999996
+21.121
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.158027410507202, 'TIME_S_1KI': 56.639733048259906, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 389.69751098632815, 'W': 25.31289781132327, 'J_1KI': 1978.1599542453205, 'W_1KI': 128.49186706255466, 'W_D': 4.1918978113232725, 'J_D': 64.53516920733456, 'W_D_1KI': 21.278669093011537, 'J_D_1KI': 108.01354869549004}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.json
index 328683d..fe8e28f 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.774195671081543, "TIME_S_1KI": 107.74195671081543, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 357.5530287361145, "W": 23.422029760012947, "J_1KI": 3575.5302873611454, "W_1KI": 234.22029760012947, "W_D": 5.2480297600129475, "J_D": 80.11470204830175, "W_D_1KI": 52.480297600129475, "J_D_1KI": 524.8029760012947}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.820615768432617, "TIME_S_1KI": 108.20615768432617, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 383.52341383934026, "W": 23.480020540098028, "J_1KI": 3835.2341383934026, "W_1KI": 234.80020540098027, "W_D": 4.937020540098029, "J_D": 80.64145295357712, "W_D_1KI": 49.37020540098029, "J_D_1KI": 493.7020540098029}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.output
index 4419f1e..77a0a49 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.2 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.774195671081543}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.820615768432617}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1044,    1986,  ..., 4998023,
-                            4998990, 5000000]),
-       col_indices=tensor([   2,   11,   17,  ..., 4984, 4985, 4991]),
-       values=tensor([0.4872, 0.8747, 0.2341,  ..., 0.7866, 0.4499, 0.5164]),
+tensor(crow_indices=tensor([      0,     975,    1962,  ..., 4998021,
+                            4999006, 5000000]),
+       col_indices=tensor([   7,    9,   14,  ..., 4985, 4989, 4996]),
+       values=tensor([0.5148, 0.0164, 0.4922,  ..., 0.3749, 0.4144, 0.9350]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.5529, 0.0016, 0.5040,  ..., 0.3915, 0.6771, 0.4202])
+tensor([0.3739, 0.8269, 0.8989,  ..., 0.5457, 0.9173, 0.8641])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.774195671081543 seconds
+Time: 10.820615768432617 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1044,    1986,  ..., 4998023,
-                            4998990, 5000000]),
-       col_indices=tensor([   2,   11,   17,  ..., 4984, 4985, 4991]),
-       values=tensor([0.4872, 0.8747, 0.2341,  ..., 0.7866, 0.4499, 0.5164]),
+tensor(crow_indices=tensor([      0,     975,    1962,  ..., 4998021,
+                            4999006, 5000000]),
+       col_indices=tensor([   7,    9,   14,  ..., 4985, 4989, 4996]),
+       values=tensor([0.5148, 0.0164, 0.4922,  ..., 0.3749, 0.4144, 0.9350]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.5529, 0.0016, 0.5040,  ..., 0.3915, 0.6771, 0.4202])
+tensor([0.3739, 0.8269, 0.8989,  ..., 0.5457, 0.9173, 0.8641])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +33,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.774195671081543 seconds
+Time: 10.820615768432617 seconds
 
-[20.36, 20.12, 20.12, 20.04, 20.28, 20.12, 20.08, 20.4, 20.4, 20.72]
-[20.8, 21.08, 22.4, 23.32, 25.48, 25.48, 27.96, 29.08, 28.16, 27.04, 25.64, 24.2, 24.2, 24.4, 24.44]
-15.265672206878662
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.774195671081543, 'TIME_S_1KI': 107.74195671081543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.5530287361145, 'W': 23.422029760012947}
-[20.36, 20.12, 20.12, 20.04, 20.28, 20.12, 20.08, 20.4, 20.4, 20.72, 20.32, 20.16, 20.04, 20.08, 20.08, 20.0, 20.16, 20.28, 20.28, 20.28]
-363.48
-18.174
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.774195671081543, 'TIME_S_1KI': 107.74195671081543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.5530287361145, 'W': 23.422029760012947, 'J_1KI': 3575.5302873611454, 'W_1KI': 234.22029760012947, 'W_D': 5.2480297600129475, 'J_D': 80.11470204830175, 'W_D_1KI': 52.480297600129475, 'J_D_1KI': 524.8029760012947}
+[20.4, 20.56, 20.56, 20.6, 20.44, 20.44, 20.44, 20.8, 21.0, 20.96]
+[21.0, 20.84, 20.88, 24.32, 26.08, 27.96, 29.8, 27.12, 27.16, 26.32, 26.32, 24.16, 24.08, 24.0, 24.0, 23.96]
+16.3340322971344
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.820615768432617, 'TIME_S_1KI': 108.20615768432617, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 383.52341383934026, 'W': 23.480020540098028}
+[20.4, 20.56, 20.56, 20.6, 20.44, 20.44, 20.44, 20.8, 21.0, 20.96, 20.72, 20.52, 20.48, 20.56, 20.52, 20.68, 20.88, 20.56, 20.56, 20.44]
+370.86
+18.543
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.820615768432617, 'TIME_S_1KI': 108.20615768432617, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 383.52341383934026, 'W': 23.480020540098028, 'J_1KI': 3835.2341383934026, 'W_1KI': 234.80020540098027, 'W_D': 4.937020540098029, 'J_D': 80.64145295357712, 'W_D_1KI': 49.37020540098029, 'J_D_1KI': 493.7020540098029}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.json
index 1787dbd..d8da17f 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 15.919427633285522, "TIME_S_1KI": 159.19427633285522, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 506.52752479553226, "W": 23.776826253573663, "J_1KI": 5065.275247955323, "W_1KI": 237.76826253573662, "W_D": 5.445826253573667, "J_D": 116.01468014574058, "W_D_1KI": 54.45826253573667, "J_D_1KI": 544.5826253573666}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 16.126448392868042, "TIME_S_1KI": 161.26448392868042, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 511.86830524444576, "W": 23.949620791408254, "J_1KI": 5118.683052444458, "W_1KI": 239.49620791408256, "W_D": 5.571620791408254, "J_D": 119.08063667488092, "W_D_1KI": 55.71620791408254, "J_D_1KI": 557.1620791408254}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.output
index 2f8bfb8..199b724 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.3 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 15.919427633285522}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 16.126448392868042}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1464,    2929,  ..., 7497018,
-                            7498512, 7500000]),
-       col_indices=tensor([   1,    9,   13,  ..., 4985, 4989, 4990]),
-       values=tensor([0.4014, 0.1905, 0.8906,  ..., 0.4332, 0.9731, 0.1283]),
+tensor(crow_indices=tensor([      0,    1515,    2990,  ..., 7496949,
+                            7498462, 7500000]),
+       col_indices=tensor([   3,    5,   10,  ..., 4991, 4994, 4996]),
+       values=tensor([0.2561, 0.8875, 0.1225,  ..., 0.4305, 0.2716, 0.4832]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.5776, 0.8031, 0.5959,  ..., 0.3626, 0.0858, 0.0842])
+tensor([0.7648, 0.0458, 0.2576,  ..., 0.3703, 0.6649, 0.5067])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 15.919427633285522 seconds
+Time: 16.126448392868042 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1464,    2929,  ..., 7497018,
-                            7498512, 7500000]),
-       col_indices=tensor([   1,    9,   13,  ..., 4985, 4989, 4990]),
-       values=tensor([0.4014, 0.1905, 0.8906,  ..., 0.4332, 0.9731, 0.1283]),
+tensor(crow_indices=tensor([      0,    1515,    2990,  ..., 7496949,
+                            7498462, 7500000]),
+       col_indices=tensor([   3,    5,   10,  ..., 4991, 4994, 4996]),
+       values=tensor([0.2561, 0.8875, 0.1225,  ..., 0.4305, 0.2716, 0.4832]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.5776, 0.8031, 0.5959,  ..., 0.3626, 0.0858, 0.0842])
+tensor([0.7648, 0.0458, 0.2576,  ..., 0.3703, 0.6649, 0.5067])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +33,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 15.919427633285522 seconds
+Time: 16.126448392868042 seconds
 
-[20.2, 20.32, 20.32, 20.32, 20.32, 20.24, 20.52, 20.56, 20.64, 20.76]
-[20.76, 20.64, 21.8, 21.8, 22.88, 24.6, 28.28, 30.28, 29.56, 29.28, 26.08, 25.32, 24.68, 24.72, 24.64, 24.44, 24.44, 24.48, 24.24, 24.32, 24.48]
-21.303411960601807
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 15.919427633285522, 'TIME_S_1KI': 159.19427633285522, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 506.52752479553226, 'W': 23.776826253573663}
-[20.2, 20.32, 20.32, 20.32, 20.32, 20.24, 20.52, 20.56, 20.64, 20.76, 20.12, 20.12, 19.96, 20.2, 20.04, 20.4, 20.6, 20.56, 20.64, 20.64]
-366.61999999999995
-18.330999999999996
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 15.919427633285522, 'TIME_S_1KI': 159.19427633285522, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 506.52752479553226, 'W': 23.776826253573663, 'J_1KI': 5065.275247955323, 'W_1KI': 237.76826253573662, 'W_D': 5.445826253573667, 'J_D': 116.01468014574058, 'W_D_1KI': 54.45826253573667, 'J_D_1KI': 544.5826253573666}
+[20.12, 19.96, 20.16, 20.16, 20.28, 20.4, 20.56, 20.6, 20.4, 20.32]
+[20.28, 20.24, 21.04, 22.32, 24.36, 26.76, 29.12, 29.12, 28.76, 28.44, 26.92, 25.68, 24.8, 24.64, 24.72, 24.76, 24.64, 24.56, 24.48, 24.76, 24.76]
+21.37271022796631
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 16.126448392868042, 'TIME_S_1KI': 161.26448392868042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 511.86830524444576, 'W': 23.949620791408254}
+[20.12, 19.96, 20.16, 20.16, 20.28, 20.4, 20.56, 20.6, 20.4, 20.32, 20.44, 20.4, 20.36, 20.4, 20.44, 20.6, 20.72, 20.6, 20.72, 20.72]
+367.56
+18.378
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 16.126448392868042, 'TIME_S_1KI': 161.26448392868042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 511.86830524444576, 'W': 23.949620791408254, 'J_1KI': 5118.683052444458, 'W_1KI': 239.49620791408256, 'W_D': 5.571620791408254, 'J_D': 119.08063667488092, 'W_D_1KI': 55.71620791408254, 'J_D_1KI': 557.1620791408254}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.json
index d18f4ce..75c1c11 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.475390195846558, "TIME_S_1KI": 214.75390195846558, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 659.3497357940673, "W": 24.06026504427972, "J_1KI": 6593.497357940673, "W_1KI": 240.60265044279723, "W_D": 5.657265044279722, "J_D": 155.03221620368953, "W_D_1KI": 56.57265044279722, "J_D_1KI": 565.7265044279723}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.22704839706421, "TIME_S_1KI": 212.2704839706421, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 664.4800143432619, "W": 24.218492520245295, "J_1KI": 6644.80014343262, "W_1KI": 242.18492520245294, "W_D": 5.684492520245296, "J_D": 155.96477230072048, "W_D_1KI": 56.84492520245296, "J_D_1KI": 568.4492520245296}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.output
index 6dfec86..5f2127e 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.4.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.4 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.475390195846558}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.22704839706421}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2112,     4108,  ...,  9995977,
-                             9998003, 10000000]),
-       col_indices=tensor([   0,    2,    5,  ..., 4993, 4997, 4998]),
-       values=tensor([0.6521, 0.2294, 0.7060,  ..., 0.9592, 0.5713, 0.6385]),
+tensor(crow_indices=tensor([       0,     2017,     3989,  ...,  9996063,
+                             9998055, 10000000]),
+       col_indices=tensor([   1,    2,    3,  ..., 4988, 4992, 4997]),
+       values=tensor([0.9794, 0.6063, 0.1282,  ..., 0.2870, 0.4423, 0.1444]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.2067, 0.4320, 0.3905,  ..., 0.7782, 0.8244, 0.2696])
+tensor([0.3967, 0.7127, 0.1145,  ..., 0.5862, 0.3390, 0.6491])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 21.475390195846558 seconds
+Time: 21.22704839706421 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2112,     4108,  ...,  9995977,
-                             9998003, 10000000]),
-       col_indices=tensor([   0,    2,    5,  ..., 4993, 4997, 4998]),
-       values=tensor([0.6521, 0.2294, 0.7060,  ..., 0.9592, 0.5713, 0.6385]),
+tensor(crow_indices=tensor([       0,     2017,     3989,  ...,  9996063,
+                             9998055, 10000000]),
+       col_indices=tensor([   1,    2,    3,  ..., 4988, 4992, 4997]),
+       values=tensor([0.9794, 0.6063, 0.1282,  ..., 0.2870, 0.4423, 0.1444]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.2067, 0.4320, 0.3905,  ..., 0.7782, 0.8244, 0.2696])
+tensor([0.3967, 0.7127, 0.1145,  ..., 0.5862, 0.3390, 0.6491])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +33,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 21.475390195846558 seconds
+Time: 21.22704839706421 seconds
 
-[20.36, 20.32, 20.12, 20.16, 20.12, 20.28, 20.68, 20.96, 21.08, 21.04]
-[20.56, 20.44, 20.44, 23.96, 25.32, 27.12, 30.0, 32.28, 29.04, 28.16, 26.48, 25.72, 24.44, 24.36, 24.24, 24.24, 24.2, 24.16, 24.28, 24.32, 24.36, 24.48, 24.04, 23.92, 23.8, 23.68, 23.88]
-27.40409278869629
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.475390195846558, 'TIME_S_1KI': 214.75390195846558, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 659.3497357940673, 'W': 24.06026504427972}
-[20.36, 20.32, 20.12, 20.16, 20.12, 20.28, 20.68, 20.96, 21.08, 21.04, 20.4, 20.4, 20.36, 20.56, 20.6, 20.4, 20.24, 20.36, 20.28, 20.48]
-368.06
-18.403
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.475390195846558, 'TIME_S_1KI': 214.75390195846558, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 659.3497357940673, 'W': 24.06026504427972, 'J_1KI': 6593.497357940673, 'W_1KI': 240.60265044279723, 'W_D': 5.657265044279722, 'J_D': 155.03221620368953, 'W_D_1KI': 56.57265044279722, 'J_D_1KI': 565.7265044279723}
+[20.24, 20.4, 20.32, 20.32, 20.4, 20.6, 20.72, 20.96, 20.92, 20.68]
+[20.52, 20.32, 20.8, 22.56, 24.2, 27.68, 29.48, 29.48, 30.08, 29.84, 28.44, 25.92, 25.08, 24.28, 24.32, 24.52, 24.48, 24.64, 24.44, 24.44, 24.48, 24.56, 24.28, 24.4, 24.28, 24.36, 24.4]
+27.436885833740234
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.22704839706421, 'TIME_S_1KI': 212.2704839706421, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 664.4800143432619, 'W': 24.218492520245295}
+[20.24, 20.4, 20.32, 20.32, 20.4, 20.6, 20.72, 20.96, 20.92, 20.68, 20.32, 20.52, 20.76, 20.6, 20.48, 20.52, 20.52, 20.64, 20.96, 20.84]
+370.68
+18.534
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.22704839706421, 'TIME_S_1KI': 212.2704839706421, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 664.4800143432619, 'W': 24.218492520245295, 'J_1KI': 6644.80014343262, 'W_1KI': 242.18492520245294, 'W_D': 5.684492520245296, 'J_D': 155.96477230072048, 'W_D_1KI': 56.84492520245296, 'J_D_1KI': 568.4492520245296}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.json
index dc66501..b69526b 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.60726523399353, "TIME_S_1KI": 266.0726523399353, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 821.7415859985354, "W": 24.511735271206813, "J_1KI": 8217.415859985353, "W_1KI": 245.11735271206814, "W_D": 6.057735271206813, "J_D": 203.08203129005446, "W_D_1KI": 60.57735271206813, "J_D_1KI": 605.7735271206813}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.64250874519348, "TIME_S_1KI": 266.4250874519348, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 813.7249093627931, "W": 24.241946717289224, "J_1KI": 8137.249093627931, "W_1KI": 242.41946717289224, "W_D": 5.653946717289223, "J_D": 189.78497617053995, "W_D_1KI": 56.53946717289223, "J_D_1KI": 565.3946717289223}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.output
index ad4e507..434a706 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_0.5.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.5 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.60726523399353}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.64250874519348}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2547,     5140,  ..., 12494977,
-                            12497506, 12500000]),
-       col_indices=tensor([   3,    4,    6,  ..., 4994, 4995, 4998]),
-       values=tensor([0.6176, 0.1216, 0.2065,  ..., 0.5783, 0.0575, 0.3833]),
+tensor(crow_indices=tensor([       0,     2517,     4999,  ..., 12495052,
+                            12497559, 12500000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4991, 4992, 4999]),
+       values=tensor([0.6703, 0.4092, 0.6328,  ..., 0.6598, 0.5299, 0.2341]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.3583, 0.1424, 0.2491,  ..., 0.0607, 0.2583, 0.4693])
+tensor([0.4179, 0.3399, 0.2076,  ..., 0.4619, 0.8936, 0.2466])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 26.60726523399353 seconds
+Time: 26.64250874519348 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2547,     5140,  ..., 12494977,
-                            12497506, 12500000]),
-       col_indices=tensor([   3,    4,    6,  ..., 4994, 4995, 4998]),
-       values=tensor([0.6176, 0.1216, 0.2065,  ..., 0.5783, 0.0575, 0.3833]),
+tensor(crow_indices=tensor([       0,     2517,     4999,  ..., 12495052,
+                            12497559, 12500000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4991, 4992, 4999]),
+       values=tensor([0.6703, 0.4092, 0.6328,  ..., 0.6598, 0.5299, 0.2341]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.3583, 0.1424, 0.2491,  ..., 0.0607, 0.2583, 0.4693])
+tensor([0.4179, 0.3399, 0.2076,  ..., 0.4619, 0.8936, 0.2466])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +33,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 26.60726523399353 seconds
+Time: 26.64250874519348 seconds
 
-[20.28, 20.52, 20.68, 20.56, 20.44, 20.52, 20.44, 20.44, 20.72, 21.0]
-[20.92, 20.68, 20.68, 23.84, 26.2, 27.84, 31.04, 29.96, 30.76, 29.48, 26.6, 26.6, 26.28, 24.68, 24.6, 24.48, 24.4, 24.24, 24.32, 24.32, 24.48, 24.52, 24.44, 24.52, 24.52, 24.56, 24.4, 24.56, 24.72, 24.68, 24.84, 24.8, 24.64]
-33.524415016174316
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.60726523399353, 'TIME_S_1KI': 266.0726523399353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 821.7415859985354, 'W': 24.511735271206813}
-[20.28, 20.52, 20.68, 20.56, 20.44, 20.52, 20.44, 20.44, 20.72, 21.0, 20.24, 20.2, 20.4, 20.6, 20.6, 20.6, 20.44, 20.56, 20.52, 20.16]
-369.08000000000004
-18.454
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.60726523399353, 'TIME_S_1KI': 266.0726523399353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 821.7415859985354, 'W': 24.511735271206813, 'J_1KI': 8217.415859985353, 'W_1KI': 245.11735271206814, 'W_D': 6.057735271206813, 'J_D': 203.08203129005446, 'W_D_1KI': 60.57735271206813, 'J_D_1KI': 605.7735271206813}
+[20.52, 20.52, 20.72, 20.76, 21.12, 21.04, 21.16, 21.04, 20.72, 20.32]
+[20.28, 20.24, 21.2, 23.24, 25.08, 25.08, 28.36, 29.84, 31.04, 30.0, 28.96, 26.28, 25.44, 24.36, 24.24, 24.2, 24.48, 24.6, 24.6, 24.48, 24.6, 24.48, 24.48, 24.52, 24.12, 24.16, 24.0, 24.04, 24.28, 24.36, 24.44, 24.44, 24.32]
+33.566813707351685
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.64250874519348, 'TIME_S_1KI': 266.4250874519348, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 813.7249093627931, 'W': 24.241946717289224}
+[20.52, 20.52, 20.72, 20.76, 21.12, 21.04, 21.16, 21.04, 20.72, 20.32, 20.28, 20.2, 20.28, 20.76, 20.88, 20.8, 20.6, 20.24, 20.2, 20.32]
+371.76
+18.588
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.64250874519348, 'TIME_S_1KI': 266.4250874519348, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 813.7249093627931, 'W': 24.241946717289224, 'J_1KI': 8137.249093627931, 'W_1KI': 242.41946717289224, 'W_D': 5.653946717289223, 'J_D': 189.78497617053995, 'W_D_1KI': 56.53946717289223, 'J_D_1KI': 565.3946717289223}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.json
index b24de3c..4989bfc 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 284909, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.16942024230957, "TIME_S_1KI": 0.035693573184102885, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 316.9542731094361, "W": 22.304497332617817, "J_1KI": 1.1124754679895548, "W_1KI": 0.07828639085679223, "W_D": 3.6764973326178207, "J_D": 52.24424125194558, "W_D_1KI": 0.012904110900736098, "J_D_1KI": 4.529204377796454e-05}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 287687, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.360889196395874, "TIME_S_1KI": 0.036014450414498654, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 315.19113276481625, "W": 22.12664535948525, "J_1KI": 1.095604364343249, "W_1KI": 0.07691221834662411, "W_D": 3.1666453594852477, "J_D": 45.108443765640196, "W_D_1KI": 0.011007259137483611, "J_D_1KI": 3.8261232302758245e-05}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.output
index ece41ca..44a657c 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,75 +1,75 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.012842655181884766}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.012710094451904297}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([1287, 2037,  612, 4005,  465, 4495, 4486, 1954, 4095,
-                           1514, 3786, 3287, 3358, 3432, 3673,  489, 2823,  505,
-                           4424, 1572, 4277,  474, 3301,   30, 2842, 4780, 2739,
-                            564, 2900, 4485, 4784, 2295,  755, 3717, 1261, 1856,
-                           2818, 3372, 3761, 1939, 4279, 1416, 4196, 1024,  159,
-                           3430, 1464,  630, 4128, 1057, 4758, 4930, 4819, 4211,
-                           3868, 1700, 2760, 4521, 1355, 4737, 4580, 1838, 4056,
-                           1953, 4561, 1726, 3125, 4174,  510, 4743,  502, 2822,
-                            338, 1706, 4412, 4712, 3417, 4607, 4478, 4287, 4365,
-                           4223, 3755,  467, 2870,  999, 1516, 3711, 3345, 4540,
-                           4303, 4477, 4047, 3188,  522,  451, 4048, 1301, 3760,
-                           3807,  142,  526, 3797, 3415,  942, 3041, 1022,  555,
-                           2433, 3440, 4291, 2481, 2516, 1226, 4664, 1242, 2239,
-                           3542, 3300, 3985, 1261,  628, 3797, 3571, 1648,  545,
-                           3417,  523,  297, 1814, 2339, 1387, 4149, 2499, 1698,
-                           4107, 3910,  907, 1601, 3072, 2976, 1955,   76, 3173,
-                             63,  633, 2089, 1360, 1226, 4574,  730, 2472, 4618,
-                            425, 3915, 1299, 1950, 4945, 1796,  628, 1797, 3210,
-                           2055, 2428,  876, 1161, 1529, 1660, 2886, 4614, 2062,
-                           2433, 3539, 1521,   33, 1294, 4198,  863, 2582, 1498,
-                             77,  507, 2697, 2034, 2514, 1935, 4132, 2925,  876,
-                           2808, 4770,  271, 3697, 1635, 2519, 4995, 3590, 3245,
-                            130,  480, 3111, 3121, 3132, 1937, 3910, 1943, 2562,
-                            426, 3962, 1910, 1189, 1897, 1056,  462, 1607, 1444,
-                            118,  191, 2005,  615, 1379,  633, 2360, 3526, 4732,
-                           2267, 3397, 1029, 3432, 2182, 2675, 4099, 3777, 2171,
-                           2640, 3913, 4300, 2946, 3758, 3305, 1103, 4800, 3668,
-                           4286, 3562,  281,  919, 4442, 2167, 2728]),
-       values=tensor([0.8347, 0.3655, 0.0811, 0.8356, 0.0205, 0.3330, 0.9286,
-                      0.0736, 0.7654, 0.8451, 0.0234, 0.4126, 0.2439, 0.1012,
-                      0.1525, 0.4404, 0.8423, 0.5434, 0.2968, 0.3607, 0.9939,
-                      0.0443, 0.6432, 0.5086, 0.6326, 0.2329, 0.7870, 0.7820,
-                      0.9646, 0.4656, 0.9109, 0.0130, 0.3562, 0.2378, 0.0761,
-                      0.1724, 0.0722, 0.8084, 0.1566, 0.8788, 0.9593, 0.2473,
-                      0.2746, 0.1767, 0.8469, 0.1106, 0.8653, 0.5297, 0.8543,
-                      0.5387, 0.4683, 0.0500, 0.6408, 0.2485, 0.5053, 0.9278,
-                      0.6730, 0.1223, 0.9361, 0.1415, 0.0908, 0.6368, 0.4532,
-                      0.7711, 0.1924, 0.7435, 0.0645, 0.3989, 0.7433, 0.7022,
-                      0.6974, 0.8264, 0.3293, 0.6363, 0.9947, 0.1723, 0.3099,
-                      0.5498, 0.6041, 0.9256, 0.6505, 0.2218, 0.5727, 0.8460,
-                      0.3386, 0.9152, 0.1985, 0.3213, 0.2437, 0.8619, 0.4265,
-                      0.8019, 0.3028, 0.4559, 0.9203, 0.9762, 0.2222, 0.3112,
-                      0.4047, 0.0709, 0.2379, 0.3209, 0.9982, 0.9963, 0.6946,
-                      0.0267, 0.7677, 0.2026, 0.6034, 0.5006, 0.8273, 0.2191,
-                      0.6497, 0.2706, 0.0892, 0.8677, 0.9857, 0.5541, 0.2974,
-                      0.1559, 0.1745, 0.4744, 0.1426, 0.1224, 0.3669, 0.1827,
-                      0.5044, 0.5810, 0.3220, 0.7231, 0.9240, 0.0412, 0.3152,
-                      0.9088, 0.3617, 0.9935, 0.3508, 0.0434, 0.0453, 0.5299,
-                      0.2529, 0.0232, 0.7419, 0.0564, 0.5519, 0.6136, 0.5013,
-                      0.9801, 0.4708, 0.5636, 0.5144, 0.1368, 0.7207, 0.1775,
-                      0.9552, 0.2262, 0.7144, 0.1124, 0.8514, 0.1783, 0.8401,
-                      0.1256, 0.7454, 0.1258, 0.2191, 0.5753, 0.9252, 0.8693,
-                      0.6514, 0.3440, 0.7780, 0.4771, 0.0787, 0.5042, 0.0634,
-                      0.8013, 0.8286, 0.4280, 0.3433, 0.9749, 0.0712, 0.9286,
-                      0.0320, 0.8979, 0.5094, 0.4000, 0.4693, 0.8308, 0.6000,
-                      0.3933, 0.7591, 0.2335, 0.5450, 0.3018, 0.3121, 0.4779,
-                      0.9302, 0.5324, 0.1295, 0.6438, 0.5030, 0.3371, 0.9613,
-                      0.8059, 0.9687, 0.2898, 0.7067, 0.8974, 0.1763, 0.0222,
-                      0.0300, 0.9494, 0.3209, 0.6515, 0.7028, 0.8063, 0.2794,
-                      0.7392, 0.1814, 0.3171, 0.4591, 0.7578, 0.6336, 0.8392,
-                      0.6142, 0.8521, 0.4206, 0.9799, 0.4517, 0.1512, 0.3696,
-                      0.0957, 0.3165, 0.3328, 0.9242, 0.5247, 0.8176, 0.9760,
-                      0.3689, 0.9384, 0.3805, 0.7826, 0.4113, 0.3311, 0.7250,
-                      0.9146, 0.3319, 0.6199, 0.8288, 0.1278]),
+       col_indices=tensor([4504, 4925, 3483, 2236, 3654,  879, 2367, 1033,  361,
+                           3171, 2429, 1911, 1608, 2947, 1720,  541,   16, 4242,
+                           4561, 4883, 4549,  893, 3920, 3256, 4585,  388, 1746,
+                           2051, 2283,  738, 1665, 2175,  502,  563, 4907, 1855,
+                             14, 4899, 3725, 1677, 3353, 3795, 2937, 2969, 4166,
+                           1958,  385, 3893, 1515,  395, 4430, 4695, 3039, 4753,
+                            944,  263, 3749, 1098, 4314,  948, 4146, 4287, 1019,
+                           3967, 1990, 4586, 4167, 3845, 3896, 2089, 3578, 1869,
+                            201, 4920,  252, 1590,  501, 3328, 1980, 2664, 1476,
+                           3301, 4106, 3571, 3576, 2787,  534,  211, 4210, 1393,
+                            899,  932,  158, 3456, 1353, 4964,  772, 2618, 2255,
+                           4378,   15, 4151, 1131, 4649,  978, 2370, 1002, 3024,
+                           3174,  132, 2699, 4574,  436,  287, 3781, 2840,  585,
+                            253, 3714,  890, 1237, 2106,  453, 3253, 2687, 4038,
+                           2573, 3153, 1786,  753, 2141, 4976,  479, 1169, 2361,
+                           4685, 2106, 1532, 1247, 2436, 1977, 1409,   53, 4427,
+                           4105,  922,  426, 2747, 3554, 4743, 4705, 1574,  252,
+                           1782,  415, 2753, 4857, 4050, 3415, 3199, 3863,  581,
+                           1831, 2495, 4469, 1436, 1047, 4329, 3112, 3247, 1156,
+                            344,   50, 2457, 1549, 4534, 4902, 4188, 1412, 2307,
+                           3467, 2816, 1347, 1780, 3660, 4058, 4763,  117, 2240,
+                            294, 2891, 1018, 4652,  770, 2573, 3893, 1274,  327,
+                           4428, 3093, 2257,  467, 3013, 2619, 2172, 4510, 3859,
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+                           2860, 1386, 2933, 3545, 4710, 4185, 3059, 3414, 2678,
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+                           3758, 4024, 3878, 1495, 1873, 2275, 4837,  668, 3929,
+                            633, 1606, 4036, 1972, 2352,  646, 1920]),
+       values=tensor([0.3281, 0.7186, 0.0885, 0.8824, 0.0428, 0.7484, 0.2776,
+                      0.8498, 0.3241, 0.6346, 0.3061, 0.1212, 0.9883, 0.2794,
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+                      0.7464, 0.3701, 0.1217, 0.5396, 0.6107, 0.7487, 0.7675,
+                      0.2977, 0.2849, 0.4368, 0.0073, 0.4129, 0.5166, 0.4763,
+                      0.0076, 0.8870, 0.0419, 0.8594, 0.6650, 0.1603, 0.5692,
+                      0.4273, 0.6343, 0.0207, 0.6781, 0.8372, 0.2087, 0.8662,
+                      0.5153, 0.5825, 0.1674, 0.5871, 0.7943]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.0363, 0.1704, 0.8959,  ..., 0.1381, 0.6314, 0.8045])
+tensor([0.5576, 0.8627, 0.7863,  ..., 0.7513, 0.9859, 0.4856])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -77,80 +77,107 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.012842655181884766 seconds
+Time: 0.012710094451904297 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 81758 -ss 5000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.01309871673584}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 82611 -ss 5000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.0151278972625732}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([3901, 3663,  229, 3286, 2751, 3483, 2128, 1446, 4503,
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-                           4165, 1821, 3059,  730, 1341, 2913, 4066, 2638, 3070,
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-                      0.9688, 0.5810, 0.6939, 0.8057, 0.2727, 0.5144, 0.0126,
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-                      0.3534, 0.8270, 0.9704, 0.5262, 0.1397]),
-       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.2610, 0.0051, 0.8611,  ..., 0.6706, 0.7457, 0.2823])
+tensor(crow_indices=tensor([  0,   0,   1,  ..., 250, 250, 250]),
+       col_indices=tensor([ 213, 4779, 3777, 4550, 4758,  725, 2100, 1582,  629,
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+                            934, 1556, 4488, 2745,   76,  977, 1941, 3187, 1442,
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+                           3290, 2440, 3917, 1363, 4386, 3961,  780, 3851, 3666,
+                           1416, 4031, 2287,  801, 4503, 3484, 4817]),
+       values=tensor([4.0354e-01, 1.2267e-01, 3.2350e-01, 7.9853e-01,
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+                      6.1740e-01, 8.8522e-01, 6.6458e-01, 5.7352e-02,
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+                      5.5219e-01, 1.2992e-01, 1.9966e-02, 7.1280e-01,
+                      5.1974e-01, 8.0110e-01, 9.8569e-01, 6.3972e-01,
+                      8.5091e-01, 2.5322e-03, 8.5596e-01, 7.8316e-01,
+                      7.2764e-01, 1.1004e-01, 4.4597e-01, 1.3273e-01,
+                      6.0224e-01, 1.0515e-01, 2.0270e-01, 8.8295e-01,
+                      2.2704e-01, 1.5559e-01, 3.1150e-01, 3.7542e-01,
+                      6.0848e-01, 7.3259e-01, 3.0180e-01, 8.5138e-01,
+                      1.2650e-02, 9.5390e-01, 6.7270e-01, 5.2163e-01,
+                      7.1039e-01, 6.3532e-02, 9.2610e-01, 6.1969e-01,
+                      3.4843e-01, 6.2312e-01, 7.2342e-01, 2.9651e-01,
+                      9.9131e-01, 9.5189e-03, 5.9330e-01, 8.7447e-01,
+                      9.5601e-01, 4.5420e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.5903, 0.2031, 0.9114,  ..., 0.3396, 0.0516, 0.4557])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -158,80 +185,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 3.01309871673584 seconds
+Time: 3.0151278972625732 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 284909 -ss 5000 -sd 1e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.16942024230957}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 287687 -ss 5000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.360889196395874}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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+       col_indices=tensor([3835, 4100, 3288, 1111, 2615, 1842,  721, 2110, 3471,
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+                      0.9499, 0.3531, 0.3033, 0.9542, 0.0598, 0.0487, 0.2094,
+                      0.8599, 0.3949, 0.2128, 0.4097, 0.2778, 0.3314, 0.2249,
+                      0.7619, 0.9244, 0.6695, 0.7027, 0.3445, 0.1185, 0.1951,
+                      0.1406, 0.2410, 0.4900, 0.3068, 0.2304, 0.0755, 0.6690,
+                      0.6082, 0.5362, 0.8224, 0.7336, 0.9890]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.9060, 0.0911, 0.6185,  ..., 0.7353, 0.0547, 0.2301])
+tensor([0.8124, 0.7369, 0.0249,  ..., 0.2346, 0.3675, 0.9867])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -239,77 +266,77 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.16942024230957 seconds
+Time: 10.360889196395874 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([1669, 2388, 3410,  214, 4888, 2047, 1859, 3824, 1130,
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-       values=tensor([0.8132, 0.1702, 0.5583, 0.1261, 0.6291, 0.5508, 0.1330,
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+       col_indices=tensor([3835, 4100, 3288, 1111, 2615, 1842,  721, 2110, 3471,
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+                           4339, 2096, 1695, 3122, 2088, 1784, 3655,  417, 4064,
+                            185, 3211, 1867,  853, 1958,  692, 3072, 1918, 1238,
+                            453, 2452, 3264, 4717,  598, 3296, 1291,   52, 4664,
+                           1619, 3701, 2531,  839, 1095, 1405, 1755, 4032, 1471,
+                           3742, 3286, 3684, 4379,  314, 1276, 2952, 1464, 3493,
+                           4632, 3867,  270, 1580, 3966, 1648, 4218, 4607, 1543,
+                           1273, 4431, 3554,  750, 2726, 3039, 4850,  204, 1982,
+                           1280, 1670,  435, 1029, 1265,  488,  313, 4283, 2463,
+                           3066, 3421, 3101, 1667, 3847,  502, 2450,  679, 4239,
+                           4387, 1269, 2481,  145, 1867,  117, 2524, 3731,  742,
+                           1231, 4496, 2560, 4909, 2316, 2336, 3858, 2912, 4745,
+                           4422,  458, 4406, 3773, 4481, 2073, 2667, 4275, 2595,
+                           2103, 4813, 1809, 3578, 3133, 3916, 1983, 2028, 1469,
+                           2266, 2381, 3024,  513, 3320,  179, 2026,  395, 3774,
+                           3246,  274,  738, 3581,  964, 3178, 1016, 1417, 4487,
+                           2137,  190, 1094,   41, 3224, 3890, 4493, 3175, 4452,
+                           2458, 2626, 1597, 2294, 1289, 4412, 4878, 2717,  611,
+                           3437,  165, 1044, 4973,  600, 1953,  234]),
+       values=tensor([0.9194, 0.9511, 0.1759, 0.0806, 0.5579, 0.5909, 0.5444,
+                      0.4929, 0.3283, 0.5813, 0.8577, 0.8573, 0.3370, 0.6634,
+                      0.3256, 0.0555, 0.4432, 0.9017, 0.3253, 0.5759, 0.1980,
+                      0.8117, 0.0490, 0.8318, 0.9103, 0.9173, 0.2933, 0.6711,
+                      0.2931, 0.9505, 0.4262, 0.7750, 0.5853, 0.8597, 0.3103,
+                      0.5115, 0.2132, 0.0255, 0.2347, 0.0616, 0.3497, 0.3151,
+                      0.3650, 0.2626, 0.4199, 0.9673, 0.5246, 0.1533, 0.1365,
+                      0.8928, 0.4291, 0.7577, 0.5244, 0.2399, 0.8984, 0.6947,
+                      0.4626, 0.8346, 0.5027, 0.3949, 0.3636, 0.5403, 0.5095,
+                      0.0164, 0.1264, 0.0974, 0.4505, 0.5292, 0.2234, 0.1960,
+                      0.5104, 0.0273, 0.8931, 0.1073, 0.8623, 0.2575, 0.7953,
+                      0.5701, 0.5194, 0.2709, 0.1261, 0.9933, 0.0287, 0.3824,
+                      0.4292, 0.1111, 0.0013, 0.8437, 0.4581, 0.4410, 0.3443,
+                      0.9120, 0.5789, 0.5934, 0.4413, 0.7093, 0.6011, 0.6718,
+                      0.1412, 0.9770, 0.5884, 0.7031, 0.5903, 0.9765, 0.1106,
+                      0.4818, 0.0405, 0.0041, 0.7017, 0.4360, 0.3522, 0.4112,
+                      0.9723, 0.0877, 0.0441, 0.1570, 0.8913, 0.1536, 0.1637,
+                      0.4981, 0.4859, 0.6376, 0.2129, 0.5287, 0.9896, 0.9621,
+                      0.1314, 0.7893, 0.3622, 0.7936, 0.0563, 0.8633, 0.0436,
+                      0.9765, 0.6100, 0.5316, 0.1536, 0.2153, 0.0996, 0.8797,
+                      0.6733, 0.6855, 0.6258, 0.3627, 0.0821, 0.4890, 0.6054,
+                      0.0612, 0.1414, 0.7279, 0.3036, 0.9598, 0.6434, 0.2948,
+                      0.2841, 0.4693, 0.6178, 0.6854, 0.2242, 0.5700, 0.9283,
+                      0.5926, 0.3085, 0.8456, 0.3258, 0.6163, 0.7395, 0.4116,
+                      0.7534, 0.4931, 0.6502, 0.7243, 0.0397, 0.2405, 0.0377,
+                      0.3907, 0.2557, 0.7719, 0.4558, 0.1846, 0.4629, 0.0481,
+                      0.8517, 0.3424, 0.8116, 0.9881, 0.9386, 0.9599, 0.5813,
+                      0.1414, 0.7283, 0.6897, 0.1106, 0.0774, 0.7348, 0.0658,
+                      0.0995, 0.0410, 0.8733, 0.4363, 0.9618, 0.9688, 0.2513,
+                      0.7746, 0.6495, 0.5569, 0.9848, 0.6514, 0.9270, 0.0876,
+                      0.9735, 0.2507, 0.3576, 0.5873, 0.9861, 0.2704, 0.2117,
+                      0.9499, 0.3531, 0.3033, 0.9542, 0.0598, 0.0487, 0.2094,
+                      0.8599, 0.3949, 0.2128, 0.4097, 0.2778, 0.3314, 0.2249,
+                      0.7619, 0.9244, 0.6695, 0.7027, 0.3445, 0.1185, 0.1951,
+                      0.1406, 0.2410, 0.4900, 0.3068, 0.2304, 0.0755, 0.6690,
+                      0.6082, 0.5362, 0.8224, 0.7336, 0.9890]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.9060, 0.0911, 0.6185,  ..., 0.7353, 0.0547, 0.2301])
+tensor([0.8124, 0.7369, 0.0249,  ..., 0.2346, 0.3675, 0.9867])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -317,13 +344,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.16942024230957 seconds
+Time: 10.360889196395874 seconds
 
-[20.28, 20.4, 20.4, 20.48, 20.76, 21.28, 21.16, 20.96, 20.76, 20.4]
-[20.12, 20.44, 21.32, 22.44, 25.12, 25.12, 25.76, 26.28, 25.96, 25.44, 23.88, 24.04, 24.16, 23.84]
-14.210330247879028
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 284909, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.16942024230957, 'TIME_S_1KI': 0.035693573184102885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.9542731094361, 'W': 22.304497332617817}
-[20.28, 20.4, 20.4, 20.48, 20.76, 21.28, 21.16, 20.96, 20.76, 20.4, 20.8, 20.88, 20.72, 20.76, 20.84, 20.84, 20.44, 20.52, 20.32, 20.6]
-372.55999999999995
-18.627999999999997
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 284909, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.16942024230957, 'TIME_S_1KI': 0.035693573184102885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.9542731094361, 'W': 22.304497332617817, 'J_1KI': 1.1124754679895548, 'W_1KI': 0.07828639085679223, 'W_D': 3.6764973326178207, 'J_D': 52.24424125194558, 'W_D_1KI': 0.012904110900736098, 'J_D_1KI': 4.529204377796454e-05}
+[20.32, 20.48, 20.36, 20.2, 20.52, 22.12, 22.84, 23.64, 23.64, 24.56]
+[23.48, 22.48, 21.68, 22.32, 23.76, 25.04, 25.96, 25.76, 25.2, 23.6, 23.52, 23.52, 23.36, 23.16]
+14.24486756324768
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 287687, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.360889196395874, 'TIME_S_1KI': 0.036014450414498654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.19113276481625, 'W': 22.12664535948525}
+[20.32, 20.48, 20.36, 20.2, 20.52, 22.12, 22.84, 23.64, 23.64, 24.56, 20.36, 20.36, 20.52, 20.44, 20.2, 20.16, 20.28, 20.28, 20.28, 20.52]
+379.2
+18.96
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 287687, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.360889196395874, 'TIME_S_1KI': 0.036014450414498654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.19113276481625, 'W': 22.12664535948525, 'J_1KI': 1.095604364343249, 'W_1KI': 0.07691221834662411, 'W_D': 3.1666453594852477, 'J_D': 45.108443765640196, 'W_D_1KI': 0.011007259137483611, 'J_D_1KI': 3.8261232302758245e-05}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.json
index 758834a..c7d5689 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.json
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 16, "ITERATIONS": 154432, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.621034145355225, "TIME_S_1KI": 0.06877482740206191, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 316.4244461250305, "W": 22.262246477486542, "J_1KI": 2.0489564735613763, "W_1KI": 0.144155657360434, "W_D": 3.7392464774865424, "J_D": 53.14778078484536, "W_D_1KI": 0.024212899382812774, "J_D_1KI": 0.00015678680184685024}
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 149526, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.39885950088501, "TIME_S_1KI": 0.07623329388123143, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 316.2356255340576, "W": 22.190935624507333, "J_1KI": 2.1149206528233058, "W_1KI": 0.14840854182220706, "W_D": 3.6929356245073315, "J_D": 52.62679442787168, "W_D_1KI": 0.024697615294379114, "J_D_1KI": 0.00016517271440671933}
diff --git a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.output
index 8f01bb5..8039b7d 100644
--- a/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.output
+++ b/pytorch/output_synthetic_16core/altra_16_csr_10_10_10_synthetic_5000_5e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.014879941940307617}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.014568090438842773}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1248, 1250, 1250]),
-       col_indices=tensor([1397, 3608,  621,  ..., 1983, 2722, 4972]),
-       values=tensor([0.7898, 0.8890, 0.9853,  ..., 0.2806, 0.4332, 0.7785]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4596, 2398, 1433,  ...,  472, 1393,    6]),
+       values=tensor([0.6977, 0.8727, 0.5300,  ..., 0.8044, 0.5079, 0.4951]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.8515, 0.1205, 0.1290,  ..., 0.0596, 0.1294, 0.2178])
+tensor([0.1657, 0.3721, 0.6924,  ..., 0.8050, 0.8578, 0.6368])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 0.014879941940307617 seconds
+Time: 0.014568090438842773 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 70564 -ss 5000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.797720670700073}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 72075 -ss 5000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 5.061230897903442}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    2,  ..., 1249, 1250, 1250]),
-       col_indices=tensor([4236, 1927,  389,  ..., 3900, 4084, 4178]),
-       values=tensor([0.5819, 0.5926, 0.4032,  ..., 0.1422, 0.8129, 0.9187]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([2260, 2424,  889,  ...,  317, 2175, 3750]),
+       values=tensor([0.1362, 0.3286, 0.9847,  ..., 0.0267, 0.4563, 0.8248]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.4782, 0.7587, 0.6755,  ..., 0.4641, 0.3230, 0.1517])
+tensor([0.2596, 0.1456, 0.1990,  ..., 0.1892, 0.0338, 0.8817])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 4.797720670700073 seconds
+Time: 5.061230897903442 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 154432 -ss 5000 -sd 5e-05 -c 16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.621034145355225}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 149526 -ss 5000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.39885950088501}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1250, 1250]),
-       col_indices=tensor([  91, 2944, 3974,  ..., 4430,   70, 3263]),
-       values=tensor([0.2553, 0.0855, 0.4739,  ..., 0.3797, 0.6721, 0.4378]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([2521,  379, 3911,  ..., 2509, 1527, 2788]),
+       values=tensor([0.8338, 0.7895, 0.3495,  ..., 0.2114, 0.5878, 0.1824]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.8009, 0.9874, 0.1682,  ..., 0.8612, 0.3697, 0.0752])
+tensor([0.2208, 0.1989, 0.1754,  ..., 0.7157, 0.0206, 0.1208])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.621034145355225 seconds
+Time: 11.39885950088501 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1250, 1250]),
-       col_indices=tensor([  91, 2944, 3974,  ..., 4430,   70, 3263]),
-       values=tensor([0.2553, 0.0855, 0.4739,  ..., 0.3797, 0.6721, 0.4378]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([2521,  379, 3911,  ..., 2509, 1527, 2788]),
+       values=tensor([0.8338, 0.7895, 0.3495,  ..., 0.2114, 0.5878, 0.1824]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.8009, 0.9874, 0.1682,  ..., 0.8612, 0.3697, 0.0752])
+tensor([0.2208, 0.1989, 0.1754,  ..., 0.7157, 0.0206, 0.1208])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.621034145355225 seconds
+Time: 11.39885950088501 seconds
 
-[20.4, 20.68, 20.44, 20.16, 20.16, 20.48, 20.44, 20.68, 20.64, 20.88]
-[20.92, 20.96, 21.52, 22.88, 25.2, 25.52, 26.2, 26.2, 25.6, 24.92, 23.32, 23.36, 23.56, 23.44]
-14.213500261306763
-{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 154432, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.621034145355225, 'TIME_S_1KI': 0.06877482740206191, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.4244461250305, 'W': 22.262246477486542}
-[20.4, 20.68, 20.44, 20.16, 20.16, 20.48, 20.44, 20.68, 20.64, 20.88, 20.72, 20.64, 20.68, 20.52, 20.64, 20.8, 20.76, 20.76, 20.68, 20.6]
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..926894b
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.0001.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..c651080
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.0001.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..847e4ec
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.001.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..94b706e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.001.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..134765b
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.01.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..5c17e1b
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_0.01.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..fbf1598
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_1e-05.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..cb8a612
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_1e-05.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..48ac41f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_5e-05.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..77f4e2a
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_100000_5e-05.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..7c1547f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.0001.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..410f729
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.0001.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..5d4c0a8
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.001.json
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+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3594, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.165302753448486, "TIME_S_1KI": 2.828409224665689, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 829.6233745193481, "W": 65.52, "J_1KI": 230.8356634722727, "W_1KI": 18.230383973288813, "W_D": 30.2055, "J_D": 382.4662521221638, "W_D_1KI": 8.404424040066779, "J_D_1KI": 2.3384596661287644}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..33572a0
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.001.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.001', '-c', '16']
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..a8267ac
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..7334943
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.01.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..5e082a6
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.05.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..b419fd7
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.05.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..2189ae8
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.1.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..8fa87f8
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.1.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..e0396a6
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..0887321
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.2.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..8d1db2d
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.3.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..a1ce70e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.3.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..1afd086
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.4.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..272753b
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.4.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..2118303
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.5.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..3004a6f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_0.5.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..8ba0450
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 314746, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.578866243362427, "TIME_S_1KI": 0.03361080440533772, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 850.5716023397445, "W": 65.38, "J_1KI": 2.7024063922646975, "W_1KI": 0.20772305287438125, "W_D": 29.65999999999999, "J_D": 385.8665299081801, "W_D_1KI": 0.09423471624738675, "J_D_1KI": 0.0002993992497041638}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..6cb35a6
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,962 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.011737346649169922}
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..001875f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 71859, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.571467638015747, "TIME_S_1KI": 0.14711403774079443, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 868.2272564220427, "W": 65.82, "J_1KI": 12.082373208951457, "W_1KI": 0.9159604224940507, "W_D": 30.39725, "J_D": 400.96810954535005, "W_D_1KI": 0.4230124271142098, "J_D_1KI": 0.005886700721053867}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..3633c50
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_10000_5e-05.output
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+tensor([0.4220, 0.1348, 0.6393,  ..., 0.8496, 0.6370, 0.8549])
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..ab3505a
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_0.0001.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..0a71393
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_0.0001.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..13025a2
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.5661301612854, "TIME_S_1KI": 115.661301612854, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 782.0749787807465, "W": 75.56, "J_1KI": 7820.749787807465, "W_1KI": 755.6, "W_D": 39.378, "J_D": 407.5774022555351, "W_D_1KI": 393.78000000000003, "J_D_1KI": 3937.8}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..8870149
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '500000', '-sd', '1e-05', '-c', '16']
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..3be8cad
--- /dev/null
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..cf1307f
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+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_500000_5e-05.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..987d012
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.0001.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..1da8c74
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.0001.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..707fa07
--- /dev/null
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..584288d
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.001.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..85cf2a4
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.01.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..fed1459
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.01.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.05.json
new file mode 100644
index 0000000..f0a419f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.05.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_0.05.output
new file mode 100644
index 0000000..8cd9724
--- /dev/null
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..24a8710
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..61fa314
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..8aa34f7
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..ef421cc
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+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_50000_5e-05.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..4c51eae
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.0001.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..4a010d8
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.0001.output
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new file mode 100644
index 0000000..8fffcbc
--- /dev/null
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new file mode 100644
index 0000000..a4fe4ad
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+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.001.output
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new file mode 100644
index 0000000..72313e9
--- /dev/null
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..d2c454f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.01.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..5c5b659
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.05.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..eaf08d6
--- /dev/null
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..5cdba8d
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.1.json
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..cfad9d8
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.1.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..9cad5a3
--- /dev/null
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..23a8845
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.2.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..df39c24
--- /dev/null
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..95caaf3
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.3.output
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..83c867e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 28.155537128448486, "TIME_S_1KI": 281.55537128448486, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2155.759934134483, "W": 65.82, "J_1KI": 21557.59934134483, "W_1KI": 658.1999999999999, "W_D": 30.531999999999996, "J_D": 999.9948694772719, "W_D_1KI": 305.32, "J_D_1KI": 3053.2}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..2ddc036
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '0.4', '-c', '16']
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+tensor(indices=tensor([[3645,  866, 4464,  ..., 3775, 4840, 1467],
+                       [ 325, 2018,  887,  ..., 1168, 4360, 1619]]),
+       values=tensor([0.5801, 0.1662, 0.1735,  ..., 0.9877, 0.5371, 0.4024]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.9744, 0.6419, 0.2598,  ..., 0.9655, 0.4765, 0.6331])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 28.155537128448486 seconds
+
+tensor(indices=tensor([[3645,  866, 4464,  ..., 3775, 4840, 1467],
+                       [ 325, 2018,  887,  ..., 1168, 4360, 1619]]),
+       values=tensor([0.5801, 0.1662, 0.1735,  ..., 0.9877, 0.5371, 0.4024]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.9744, 0.6419, 0.2598,  ..., 0.9655, 0.4765, 0.6331])
+Matrix Type: synthetic
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+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 28.155537128448486 seconds
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..2498f33
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 35.20994186401367, "TIME_S_1KI": 352.0994186401367, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2644.1709757876392, "W": 65.49, "J_1KI": 26441.709757876393, "W_1KI": 654.9, "W_D": 29.974999999999994, "J_D": 1210.2462207853791, "W_D_1KI": 299.74999999999994, "J_D_1KI": 2997.4999999999995}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..eb2720d
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 35.20994186401367}
+
+tensor(indices=tensor([[3943, 2004, 3051,  ..., 3465,  198, 2587],
+                       [4386, 4164,  493,  ..., 2470, 2586, 4314]]),
+       values=tensor([0.5814, 0.2631, 0.1674,  ..., 0.8278, 0.5822, 0.5172]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_coo)
+tensor([0.4259, 0.1334, 0.4015,  ..., 0.2681, 0.4743, 0.6607])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 35.20994186401367 seconds
+
+tensor(indices=tensor([[3943, 2004, 3051,  ..., 3465,  198, 2587],
+                       [4386, 4164,  493,  ..., 2470, 2586, 4314]]),
+       values=tensor([0.5814, 0.2631, 0.1674,  ..., 0.8278, 0.5822, 0.5172]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_coo)
+tensor([0.4259, 0.1334, 0.4015,  ..., 0.2681, 0.4743, 0.6607])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 35.20994186401367 seconds
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..c611346
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 902550, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.34853196144104, "TIME_S_1KI": 0.011465882179869304, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 847.5583872699738, "W": 66.12, "J_1KI": 0.9390708406957773, "W_1KI": 0.07325909921887984, "W_D": 30.845499999999994, "J_D": 395.3926532748937, "W_D_1KI": 0.034175945930973346, "J_D_1KI": 3.786598629546657e-05}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..4df0ceb
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,510 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.009576559066772461}
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diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..3d5f606
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 258935, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.24148416519165, "TIME_S_1KI": 0.03955233616618707, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 840.220828986168, "W": 66.18, "J_1KI": 3.244910224520316, "W_1KI": 0.25558537856991137, "W_D": 30.746000000000002, "J_D": 390.3510064673424, "W_D_1KI": 0.11874022438063608, "J_D_1KI": 0.00045857155031431084}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..b403cfa
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_coo_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,73 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.012699604034423828}
+
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+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.6672, 0.6134, 0.3158,  ..., 0.8387, 0.6834, 0.8093])
+Matrix Type: synthetic
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+Shape: torch.Size([5000, 5000])
+Rows: 5000
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+NNZ: 1250
+Density: 5e-05
+Time: 0.012699604034423828 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '82679', '-ss', '5000', '-sd', '5e-05', '-c', '16']
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+       values=tensor([0.1472, 0.5851, 0.7428,  ..., 0.4348, 0.9587, 0.6151]),
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+708.6800000000001
+35.434000000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 258935, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.24148416519165, 'TIME_S_1KI': 0.03955233616618707, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 840.220828986168, 'W': 66.18, 'J_1KI': 3.244910224520316, 'W_1KI': 0.25558537856991137, 'W_D': 30.746000000000002, 'J_D': 390.3510064673424, 'W_D_1KI': 0.11874022438063608, 'J_D_1KI': 0.00045857155031431084}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json
index a438e21..e67142b 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 65446, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.748796463012695, "TIME_S_1KI": 0.16423916607604278, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1866.1015530323982, "W": 143.13, "J_1KI": 28.51360744785622, "W_1KI": 2.1869938575314, "W_D": 106.99974999999999, "J_D": 1395.0422668139338, "W_D_1KI": 1.634931852214039, "J_D_1KI": 0.024981386978792274}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 66714, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.461231231689453, "TIME_S_1KI": 0.15680713540920127, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1791.6115726852415, "W": 132.88, "J_1KI": 26.855106464688696, "W_1KI": 1.9917858320592376, "W_D": 96.93974999999999, "J_D": 1307.0317425738572, "W_D_1KI": 1.4530645741523518, "J_D_1KI": 0.02178050445412285}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.output
index 7f0e014..3d6e8e6 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,54 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.060246944427490234}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.053905487060546875}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      16,      25,  ...,  999980,
-                             999989, 1000000]),
-       col_indices=tensor([ 4573,  4595,  4948,  ..., 71788, 92544, 99741]),
-       values=tensor([0.3512, 0.1040, 0.2729,  ..., 0.2513, 0.9554, 0.9408]),
-       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1257, 0.5794, 0.5612,  ..., 0.8235, 0.1474, 0.3975])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 1000000
-Density: 0.0001
-Time: 0.060246944427490234 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '17428', '-ss', '100000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.7960927486419678}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       8,      17,  ...,  999980,
-                             999989, 1000000]),
-       col_indices=tensor([11836, 34889, 39226,  ..., 79566, 86668, 94364]),
-       values=tensor([0.7886, 0.3777, 0.4340,  ..., 0.5250, 0.8836, 0.4934]),
-       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9435, 0.7532, 0.3829,  ..., 0.0561, 0.6547, 0.0145])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 1000000
-Density: 0.0001
-Time: 2.7960927486419678 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65446', '-ss', '100000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.748796463012695}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       8,      17,  ...,  999978,
+tensor(crow_indices=tensor([      0,       8,      13,  ...,  999979,
                              999990, 1000000]),
-       col_indices=tensor([ 6624,  6694, 37331,  ..., 71444, 97628, 99166]),
-       values=tensor([0.8094, 0.0427, 0.0622,  ..., 0.4502, 0.4633, 0.1157]),
+       col_indices=tensor([ 5399, 28611, 38632,  ..., 74258, 77167, 84233]),
+       values=tensor([0.8812, 0.0922, 0.5645,  ..., 0.4488, 0.4543, 0.9991]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2357, 0.1643, 0.3206,  ..., 0.7759, 0.8620, 0.1771])
+tensor([0.4452, 0.7873, 0.7879,  ..., 0.7551, 0.2377, 0.9069])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.748796463012695 seconds
+Time: 0.053905487060546875 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19478', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 3.0656049251556396}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       8,      17,  ...,  999978,
-                             999990, 1000000]),
-       col_indices=tensor([ 6624,  6694, 37331,  ..., 71444, 97628, 99166]),
-       values=tensor([0.8094, 0.0427, 0.0622,  ..., 0.4502, 0.4633, 0.1157]),
+tensor(crow_indices=tensor([      0,       9,      19,  ...,  999985,
+                             999991, 1000000]),
+       col_indices=tensor([ 7446, 17202, 36833,  ..., 70909, 76716, 77433]),
+       values=tensor([0.5834, 0.0757, 0.4016,  ..., 0.9038, 0.5261, 0.7581]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2357, 0.1643, 0.3206,  ..., 0.7759, 0.8620, 0.1771])
+tensor([0.0103, 0.1480, 0.1219,  ..., 0.8946, 0.0388, 0.8580])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +36,50 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.748796463012695 seconds
+Time: 3.0656049251556396 seconds
 
-[40.51, 40.31, 40.08, 39.71, 39.8, 39.59, 39.58, 41.48, 39.56, 40.06]
-[143.13]
-13.037808656692505
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 65446, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.748796463012695, 'TIME_S_1KI': 0.16423916607604278, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1866.1015530323982, 'W': 143.13}
-[40.51, 40.31, 40.08, 39.71, 39.8, 39.59, 39.58, 41.48, 39.56, 40.06, 41.48, 39.67, 40.17, 39.98, 41.44, 39.98, 40.5, 40.23, 39.59, 39.82]
-722.605
-36.130250000000004
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 65446, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.748796463012695, 'TIME_S_1KI': 0.16423916607604278, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1866.1015530323982, 'W': 143.13, 'J_1KI': 28.51360744785622, 'W_1KI': 2.1869938575314, 'W_D': 106.99974999999999, 'J_D': 1395.0422668139338, 'W_D_1KI': 1.634931852214039, 'J_D_1KI': 0.024981386978792274}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '66714', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.461231231689453}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      11,      30,  ...,  999988,
+                             999991, 1000000]),
+       col_indices=tensor([27709, 30156, 33688,  ..., 70455, 73760, 83220]),
+       values=tensor([0.5285, 0.4310, 0.0900,  ..., 0.7077, 0.0499, 0.7494]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8412, 0.9269, 0.8422,  ..., 0.3451, 0.3063, 0.1702])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.461231231689453 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      11,      30,  ...,  999988,
+                             999991, 1000000]),
+       col_indices=tensor([27709, 30156, 33688,  ..., 70455, 73760, 83220]),
+       values=tensor([0.5285, 0.4310, 0.0900,  ..., 0.7077, 0.0499, 0.7494]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8412, 0.9269, 0.8422,  ..., 0.3451, 0.3063, 0.1702])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.461231231689453 seconds
+
+[40.5, 39.66, 40.11, 39.57, 39.63, 39.63, 39.55, 39.48, 39.56, 40.05]
+[132.88]
+13.48292875289917
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 66714, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.461231231689453, 'TIME_S_1KI': 0.15680713540920127, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1791.6115726852415, 'W': 132.88}
+[40.5, 39.66, 40.11, 39.57, 39.63, 39.63, 39.55, 39.48, 39.56, 40.05, 40.81, 42.06, 40.15, 39.68, 39.89, 39.6, 39.82, 39.72, 39.56, 40.91]
+718.8050000000001
+35.940250000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 66714, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.461231231689453, 'TIME_S_1KI': 0.15680713540920127, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1791.6115726852415, 'W': 132.88, 'J_1KI': 26.855106464688696, 'W_1KI': 1.9917858320592376, 'W_D': 96.93974999999999, 'J_D': 1307.0317425738572, 'W_D_1KI': 1.4530645741523518, 'J_D_1KI': 0.02178050445412285}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.json
index d0be3f6..694db45 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4693, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 12.009309530258179, "TIME_S_1KI": 2.558983492490556, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2204.305100774765, "W": 128.95, "J_1KI": 469.7006394150362, "W_1KI": 27.477093543575535, "W_D": 92.7465, "J_D": 1585.4329820008277, "W_D_1KI": 19.76273172810569, "J_D_1KI": 4.211108401471487}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3901, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.46561336517334, "TIME_S_1KI": 2.682802708324363, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1865.2596230363845, "W": 124.74, "J_1KI": 478.14909588218006, "W_1KI": 31.976416303511918, "W_D": 88.61325, "J_D": 1325.0498419995904, "W_D_1KI": 22.715521661112536, "J_D_1KI": 5.8229996567835265}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.output
index 561971c..e401268 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.27472805976867676}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.28267550468444824}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      103,      224,  ...,  9999788,
-                             9999890, 10000000]),
-       col_indices=tensor([  311,  3365,  5161,  ..., 98602, 99530, 99576]),
-       values=tensor([0.9917, 0.0583, 0.3712,  ..., 0.9136, 0.4986, 0.7909]),
+tensor(crow_indices=tensor([       0,      103,      201,  ...,  9999817,
+                             9999916, 10000000]),
+       col_indices=tensor([ 1103,  1843,  2136,  ..., 95563, 97729, 98839]),
+       values=tensor([0.5689, 0.1057, 0.5632,  ..., 0.4681, 0.0483, 0.5356]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4323, 0.4083, 0.9080,  ..., 0.7530, 0.1922, 0.7136])
+tensor([0.5591, 0.4203, 0.6956,  ..., 0.1866, 0.2601, 0.3966])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 0.27472805976867676 seconds
+Time: 0.28267550468444824 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3821', '-ss', '100000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.548935651779175}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3714', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.995946168899536}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       86,      193,  ...,  9999790,
-                             9999889, 10000000]),
-       col_indices=tensor([  598,  3163,  6325,  ..., 93333, 94869, 95502]),
-       values=tensor([0.3479, 0.2007, 0.7107,  ..., 0.5121, 0.1193, 0.0296]),
+tensor(crow_indices=tensor([       0,       95,      175,  ...,  9999784,
+                             9999900, 10000000]),
+       col_indices=tensor([ 1431,  2503,  3033,  ..., 95829, 98067, 99882]),
+       values=tensor([0.4976, 0.5701, 0.5342,  ..., 0.4648, 0.3219, 0.3156]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.9967, 0.6546, 0.0107,  ..., 0.1473, 0.4856, 0.1261])
+tensor([0.4285, 0.0770, 0.0067,  ..., 0.8194, 0.8320, 0.3047])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 8.548935651779175 seconds
+Time: 9.995946168899536 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4693', '-ss', '100000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 12.009309530258179}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3901', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.46561336517334}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       80,      177,  ...,  9999782,
-                             9999890, 10000000]),
-       col_indices=tensor([ 1894,  3295,  3747,  ..., 98404, 98823, 99018]),
-       values=tensor([0.1540, 0.7163, 0.3077,  ..., 0.3211, 0.5255, 0.5012]),
+tensor(crow_indices=tensor([       0,      122,      223,  ...,  9999792,
+                             9999895, 10000000]),
+       col_indices=tensor([ 1544,  1798,  2744,  ..., 97459, 97993, 98974]),
+       values=tensor([0.0071, 0.6500, 0.6831,  ..., 0.9318, 0.2280, 0.2243]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8104, 0.7178, 0.6885,  ..., 0.8661, 0.7147, 0.1559])
+tensor([0.4502, 0.2455, 0.1160,  ..., 0.7937, 0.8261, 0.8290])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 12.009309530258179 seconds
+Time: 10.46561336517334 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       80,      177,  ...,  9999782,
-                             9999890, 10000000]),
-       col_indices=tensor([ 1894,  3295,  3747,  ..., 98404, 98823, 99018]),
-       values=tensor([0.1540, 0.7163, 0.3077,  ..., 0.3211, 0.5255, 0.5012]),
+tensor(crow_indices=tensor([       0,      122,      223,  ...,  9999792,
+                             9999895, 10000000]),
+       col_indices=tensor([ 1544,  1798,  2744,  ..., 97459, 97993, 98974]),
+       values=tensor([0.0071, 0.6500, 0.6831,  ..., 0.9318, 0.2280, 0.2243]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8104, 0.7178, 0.6885,  ..., 0.8661, 0.7147, 0.1559])
+tensor([0.4502, 0.2455, 0.1160,  ..., 0.7937, 0.8261, 0.8290])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 12.009309530258179 seconds
+Time: 10.46561336517334 seconds
 
-[41.32, 39.94, 39.97, 39.81, 39.83, 40.24, 40.5, 40.22, 40.21, 41.33]
-[128.95]
-17.09426212310791
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 12.009309530258179, 'TIME_S_1KI': 2.558983492490556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2204.305100774765, 'W': 128.95}
-[41.32, 39.94, 39.97, 39.81, 39.83, 40.24, 40.5, 40.22, 40.21, 41.33, 40.63, 40.93, 40.28, 39.66, 39.87, 41.7, 39.67, 40.03, 39.68, 39.78]
-724.0699999999999
-36.2035
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 12.009309530258179, 'TIME_S_1KI': 2.558983492490556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2204.305100774765, 'W': 128.95, 'J_1KI': 469.7006394150362, 'W_1KI': 27.477093543575535, 'W_D': 92.7465, 'J_D': 1585.4329820008277, 'W_D_1KI': 19.76273172810569, 'J_D_1KI': 4.211108401471487}
+[40.65, 40.02, 40.14, 40.39, 40.44, 40.48, 42.08, 40.13, 39.85, 39.7]
+[124.74]
+14.953179597854614
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3901, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.46561336517334, 'TIME_S_1KI': 2.682802708324363, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1865.2596230363845, 'W': 124.74}
+[40.65, 40.02, 40.14, 40.39, 40.44, 40.48, 42.08, 40.13, 39.85, 39.7, 40.82, 39.8, 39.83, 39.68, 39.82, 39.74, 40.19, 39.72, 39.68, 39.92]
+722.5350000000001
+36.12675
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3901, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.46561336517334, 'TIME_S_1KI': 2.682802708324363, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1865.2596230363845, 'W': 124.74, 'J_1KI': 478.14909588218006, 'W_1KI': 31.976416303511918, 'W_D': 88.61325, 'J_D': 1325.0498419995904, 'W_D_1KI': 22.715521661112536, 'J_D_1KI': 5.8229996567835265}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..10e69e4
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 494, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.353509664535522, "TIME_S_1KI": 20.958521588128587, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3731.4962283182144, "W": 99.42, "J_1KI": 7553.636089712984, "W_1KI": 201.25506072874495, "W_D": 63.24625, "J_D": 2373.799470230043, "W_D_1KI": 128.02884615384616, "J_D_1KI": 259.1677047648708}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..17582d7
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.1213040351867676}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      1022,      2009,  ...,
+                             99997908,  99998970, 100000000]),
+       col_indices=tensor([  163,   253,   363,  ..., 99772, 99825, 99860]),
+       values=tensor([0.0148, 0.7993, 0.6700,  ..., 0.9519, 0.4393, 0.7559]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.5071, 0.9341, 0.1013,  ..., 0.3446, 0.4492, 0.8404])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 2.1213040351867676 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '494', '-ss', '100000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.353509664535522}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       964,      1956,  ...,
+                             99998009,  99999004, 100000000]),
+       col_indices=tensor([   38,    40,   408,  ..., 99675, 99715, 99925]),
+       values=tensor([0.6358, 0.6526, 0.7974,  ..., 0.8113, 0.8396, 0.0716]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.6561, 0.8272, 0.0568,  ..., 0.8206, 0.9827, 0.1445])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 10.353509664535522 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       964,      1956,  ...,
+                             99998009,  99999004, 100000000]),
+       col_indices=tensor([   38,    40,   408,  ..., 99675, 99715, 99925]),
+       values=tensor([0.6358, 0.6526, 0.7974,  ..., 0.8113, 0.8396, 0.0716]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.6561, 0.8272, 0.0568,  ..., 0.8206, 0.9827, 0.1445])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 10.353509664535522 seconds
+
+[41.04, 40.08, 40.58, 39.71, 40.86, 39.62, 39.7, 39.56, 39.69, 39.62]
+[99.42]
+37.53265166282654
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 494, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.353509664535522, 'TIME_S_1KI': 20.958521588128587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3731.4962283182144, 'W': 99.42}
+[41.04, 40.08, 40.58, 39.71, 40.86, 39.62, 39.7, 39.56, 39.69, 39.62, 46.01, 39.86, 40.26, 39.93, 40.17, 40.1, 39.63, 41.02, 39.56, 39.62]
+723.475
+36.17375
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 494, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.353509664535522, 'TIME_S_1KI': 20.958521588128587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3731.4962283182144, 'W': 99.42, 'J_1KI': 7553.636089712984, 'W_1KI': 201.25506072874495, 'W_D': 63.24625, 'J_D': 2373.799470230043, 'W_D_1KI': 128.02884615384616, 'J_D_1KI': 259.1677047648708}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.json
index 5f6b7ac..ef46be1 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 99857, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.432250738143921, "TIME_S_1KI": 0.10447190220158749, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1536.9044136524199, "W": 115.09999999999998, "J_1KI": 15.391053342804408, "W_1KI": 1.152648287050482, "W_D": 79.15799999999997, "J_D": 1056.9789711198803, "W_D_1KI": 0.7927135804200004, "J_D_1KI": 0.007938487841813797}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 99236, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.395461559295654, "TIME_S_1KI": 0.10475494336022868, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1532.423426902294, "W": 114.49, "J_1KI": 15.442212774621044, "W_1KI": 1.1537143778467491, "W_D": 78.631, "J_D": 1052.4586119377614, "W_D_1KI": 0.7923636583497925, "J_D_1KI": 0.007984639227193684}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.output
index 5aa8a41..a845885 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.043670654296875}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.09090495109558105}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      1,  ...,  99996,  99998,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99999, 100000,
                             100000]),
-       col_indices=tensor([ 6609, 19255, 81333,  ..., 81128, 51531, 76130]),
-       values=tensor([0.9876, 0.0139, 0.8085,  ..., 0.3685, 0.4758, 0.0266]),
+       col_indices=tensor([59722, 26553, 65309,  ..., 96537, 72323, 51178]),
+       values=tensor([0.8619, 0.8559, 0.4334,  ..., 0.5838, 0.3974, 0.6330]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1735, 0.8240, 0.8190,  ..., 0.4288, 0.7745, 0.1715])
+tensor([0.4984, 0.6054, 0.7345,  ..., 0.7298, 0.5548, 0.3418])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 0.043670654296875 seconds
+Time: 0.09090495109558105 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24043', '-ss', '100000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.5281074047088623}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '11550', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.2220795154571533}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ...,  99997,  99999,
+tensor(crow_indices=tensor([     0,      1,      1,  ..., 100000, 100000,
                             100000]),
-       col_indices=tensor([69039, 75318, 84133,  ..., 16483, 23976, 47642]),
-       values=tensor([0.3961, 0.2517, 0.3876,  ..., 0.3761, 0.7912, 0.1675]),
+       col_indices=tensor([97989, 94542,  6731,  ...,   193,  6668, 85074]),
+       values=tensor([0.2761, 0.7667, 0.4159,  ..., 0.3284, 0.0255, 0.9429]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.9918, 0.3750, 0.7737,  ..., 0.5214, 0.0832, 0.2225])
+tensor([0.3205, 0.1159, 0.9286,  ..., 0.1316, 0.0577, 0.6513])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 2.5281074047088623 seconds
+Time: 1.2220795154571533 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '99857', '-ss', '100000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.432250738143921}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '99236', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.395461559295654}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998, 100000,
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99999, 100000,
                             100000]),
-       col_indices=tensor([18969, 38131, 43029,  ..., 81495,  1519, 27704]),
-       values=tensor([0.3850, 0.3770, 0.8820,  ..., 0.3865, 0.0804, 0.8829]),
+       col_indices=tensor([81931, 10489, 91860,  ..., 16255, 66492, 51947]),
+       values=tensor([0.9027, 0.7980, 0.1818,  ..., 0.8534, 0.1736, 0.7236]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4374, 0.1348, 0.8967,  ..., 0.5157, 0.0353, 0.0014])
+tensor([0.8190, 0.0065, 0.8917,  ..., 0.2823, 0.5210, 0.8907])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.432250738143921 seconds
+Time: 10.395461559295654 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998, 100000,
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99999, 100000,
                             100000]),
-       col_indices=tensor([18969, 38131, 43029,  ..., 81495,  1519, 27704]),
-       values=tensor([0.3850, 0.3770, 0.8820,  ..., 0.3865, 0.0804, 0.8829]),
+       col_indices=tensor([81931, 10489, 91860,  ..., 16255, 66492, 51947]),
+       values=tensor([0.9027, 0.7980, 0.1818,  ..., 0.8534, 0.1736, 0.7236]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4374, 0.1348, 0.8967,  ..., 0.5157, 0.0353, 0.0014])
+tensor([0.8190, 0.0065, 0.8917,  ..., 0.2823, 0.5210, 0.8907])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.432250738143921 seconds
+Time: 10.395461559295654 seconds
 
-[40.36, 39.67, 39.74, 39.69, 39.75, 39.62, 40.16, 41.41, 40.17, 40.09]
-[115.1]
-13.35277509689331
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 99857, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.432250738143921, 'TIME_S_1KI': 0.10447190220158749, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1536.9044136524199, 'W': 115.09999999999998}
-[40.36, 39.67, 39.74, 39.69, 39.75, 39.62, 40.16, 41.41, 40.17, 40.09, 40.33, 39.61, 39.56, 41.58, 39.51, 39.51, 39.84, 39.34, 39.38, 39.82]
-718.8400000000001
-35.94200000000001
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 99857, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.432250738143921, 'TIME_S_1KI': 0.10447190220158749, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1536.9044136524199, 'W': 115.09999999999998, 'J_1KI': 15.391053342804408, 'W_1KI': 1.152648287050482, 'W_D': 79.15799999999997, 'J_D': 1056.9789711198803, 'W_D_1KI': 0.7927135804200004, 'J_D_1KI': 0.007938487841813797}
+[40.35, 39.53, 39.89, 39.5, 41.28, 39.51, 39.64, 39.86, 39.48, 39.41]
+[114.49]
+13.384779691696167
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 99236, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.395461559295654, 'TIME_S_1KI': 0.10475494336022868, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1532.423426902294, 'W': 114.49}
+[40.35, 39.53, 39.89, 39.5, 41.28, 39.51, 39.64, 39.86, 39.48, 39.41, 40.47, 40.01, 39.54, 39.38, 39.48, 39.86, 39.66, 39.85, 39.97, 41.25]
+717.1800000000001
+35.859
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 99236, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.395461559295654, 'TIME_S_1KI': 0.10475494336022868, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1532.423426902294, 'W': 114.49, 'J_1KI': 15.442212774621044, 'W_1KI': 1.1537143778467491, 'W_D': 78.631, 'J_D': 1052.4586119377614, 'W_D_1KI': 0.7923636583497925, 'J_D_1KI': 0.007984639227193684}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.json
index 80ab5da..7a818be 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 81276, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.078009605407715, "TIME_S_1KI": 0.12399736214144047, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1722.4750410318375, "W": 132.75, "J_1KI": 21.192911081153568, "W_1KI": 1.6333234903292484, "W_D": 96.411, "J_D": 1250.9645286698342, "W_D_1KI": 1.1862173335301935, "J_D_1KI": 0.014594927574317062}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 83410, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.996037721633911, "TIME_S_1KI": 0.13183116798506067, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1795.560396194458, "W": 132.0, "J_1KI": 21.526919987944584, "W_1KI": 1.582544059465292, "W_D": 95.85775, "J_D": 1303.9271179417372, "W_D_1KI": 1.149235703153099, "J_D_1KI": 0.013778152537502687}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.output
index 0f8dc83..a884f81 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.04085874557495117}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.053716182708740234}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      9,  ..., 499987, 499993,
+tensor(crow_indices=tensor([     0,      5,      8,  ..., 499985, 499992,
                             500000]),
-       col_indices=tensor([ 4658, 51132, 55767,  ..., 77897, 84680, 91168]),
-       values=tensor([0.8716, 0.7460, 0.9968,  ..., 0.7762, 0.8585, 0.9878]),
+       col_indices=tensor([ 3808, 29545, 30689,  ..., 61411, 72054, 99531]),
+       values=tensor([0.0219, 0.7195, 0.4237,  ..., 0.5900, 0.7914, 0.0313]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.7678, 0.5187, 0.4774,  ..., 0.8664, 0.3724, 0.0254])
+tensor([0.7421, 0.2952, 0.1401,  ..., 0.8551, 0.8773, 0.7735])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 0.04085874557495117 seconds
+Time: 0.053716182708740234 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '25698', '-ss', '100000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.3198819160461426}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19547', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.460651159286499}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      4,  ..., 499992, 499998,
+tensor(crow_indices=tensor([     0,      2,      6,  ..., 499989, 499996,
                             500000]),
-       col_indices=tensor([33478, 35089, 63624,  ..., 93258,  3464, 77760]),
-       values=tensor([0.8303, 0.5286, 0.9064,  ..., 0.8655, 0.5788, 0.5903]),
+       col_indices=tensor([55237, 65503, 31375,  ..., 76224, 77752, 79533]),
+       values=tensor([0.6791, 0.3647, 0.0311,  ..., 0.1415, 0.5862, 0.6146]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.0892, 0.6340, 0.1475,  ..., 0.5230, 0.0009, 0.8265])
+tensor([0.0236, 0.3685, 0.8621,  ..., 0.9429, 0.5386, 0.6335])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 3.3198819160461426 seconds
+Time: 2.460651159286499 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '81276', '-ss', '100000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.078009605407715}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '83410', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.996037721633911}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      9,  ..., 499997, 500000,
+tensor(crow_indices=tensor([     0,      4,      8,  ..., 499990, 499995,
                             500000]),
-       col_indices=tensor([38450, 44227, 69625,  ...,  8507, 39094, 82179]),
-       values=tensor([0.2677, 0.9845, 0.1042,  ..., 0.9974, 0.0756, 0.3422]),
+       col_indices=tensor([17836, 30465, 31708,  ..., 69225, 71763, 99122]),
+       values=tensor([0.9507, 0.2665, 0.6401,  ..., 0.4112, 0.5507, 0.3231]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.8400, 0.1962, 0.3075,  ..., 0.6034, 0.5737, 0.0994])
+tensor([0.5095, 0.9305, 0.2807,  ..., 0.2466, 0.1767, 0.8179])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.078009605407715 seconds
+Time: 10.996037721633911 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      9,  ..., 499997, 500000,
+tensor(crow_indices=tensor([     0,      4,      8,  ..., 499990, 499995,
                             500000]),
-       col_indices=tensor([38450, 44227, 69625,  ...,  8507, 39094, 82179]),
-       values=tensor([0.2677, 0.9845, 0.1042,  ..., 0.9974, 0.0756, 0.3422]),
+       col_indices=tensor([17836, 30465, 31708,  ..., 69225, 71763, 99122]),
+       values=tensor([0.9507, 0.2665, 0.6401,  ..., 0.4112, 0.5507, 0.3231]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.8400, 0.1962, 0.3075,  ..., 0.6034, 0.5737, 0.0994])
+tensor([0.5095, 0.9305, 0.2807,  ..., 0.2466, 0.1767, 0.8179])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.078009605407715 seconds
+Time: 10.996037721633911 seconds
 
-[41.19, 39.74, 39.62, 40.23, 39.69, 39.5, 44.76, 39.57, 39.56, 39.98]
-[132.75]
-12.975329875946045
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 81276, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.078009605407715, 'TIME_S_1KI': 0.12399736214144047, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1722.4750410318375, 'W': 132.75}
-[41.19, 39.74, 39.62, 40.23, 39.69, 39.5, 44.76, 39.57, 39.56, 39.98, 40.29, 39.57, 39.62, 45.06, 39.69, 39.48, 40.04, 39.95, 40.05, 39.84]
-726.78
-36.339
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 81276, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.078009605407715, 'TIME_S_1KI': 0.12399736214144047, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1722.4750410318375, 'W': 132.75, 'J_1KI': 21.192911081153568, 'W_1KI': 1.6333234903292484, 'W_D': 96.411, 'J_D': 1250.9645286698342, 'W_D_1KI': 1.1862173335301935, 'J_D_1KI': 0.014594927574317062}
+[40.63, 39.65, 39.67, 39.68, 40.38, 40.41, 40.18, 39.64, 39.74, 41.83]
+[132.0]
+13.60273027420044
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 83410, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.996037721633911, 'TIME_S_1KI': 0.13183116798506067, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1795.560396194458, 'W': 132.0}
+[40.63, 39.65, 39.67, 39.68, 40.38, 40.41, 40.18, 39.64, 39.74, 41.83, 41.59, 40.04, 39.76, 39.63, 40.43, 39.58, 40.65, 39.49, 39.56, 44.66]
+722.845
+36.142250000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 83410, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.996037721633911, 'TIME_S_1KI': 0.13183116798506067, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1795.560396194458, 'W': 132.0, 'J_1KI': 21.526919987944584, 'W_1KI': 1.582544059465292, 'W_D': 95.85775, 'J_D': 1303.9271179417372, 'W_D_1KI': 1.149235703153099, 'J_D_1KI': 0.013778152537502687}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.json
index 0d37837..da82dae 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 280711, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.449347734451294, "TIME_S_1KI": 0.03722457521953644, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1289.5745413303375, "W": 98.5, "J_1KI": 4.593957989998032, "W_1KI": 0.35089469240606885, "W_D": 62.83475, "J_D": 822.6405473183394, "W_D_1KI": 0.22384142409809377, "J_D_1KI": 0.0007974088086968226}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 219306, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 35.10541844367981, "TIME_S_1KI": 0.16007504784948795, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1057.4668346881867, "W": 97.9, "J_1KI": 4.821878264562696, "W_1KI": 0.4464082150055174, "W_D": 61.93775000000001, "J_D": 669.0205969377757, "W_D_1KI": 0.2824261534112154, "J_D_1KI": 0.001287817722320481}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.output
index d302c45..12fb597 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.019724130630493164}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6370301246643066}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ...,  9997,  9999, 10000]),
-       col_indices=tensor([ 730, 4220, 7544,  ..., 4458, 7562, 5619]),
-       values=tensor([0.0181, 0.7832, 0.5914,  ..., 0.2469, 0.2734, 0.2796]),
+tensor(crow_indices=tensor([    0,     2,     3,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([1761, 2053, 4137,  ...,  881, 5922, 6796]),
+       values=tensor([0.5342, 0.2362, 0.4713,  ..., 0.3700, 0.5232, 0.3591]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.6994, 0.7339, 0.7582,  ..., 0.9456, 0.1186, 0.3856])
+tensor([0.7431, 0.7442, 0.3927,  ..., 0.0109, 0.6031, 0.1086])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 0.019724130630493164 seconds
+Time: 0.6370301246643066 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '53234', '-ss', '10000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.991217851638794}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1648', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.0789031982421875}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     5,     5,  ...,  9999,  9999, 10000]),
-       col_indices=tensor([2031, 5960, 7493,  ..., 3747, 8534, 6060]),
-       values=tensor([0.1847, 0.1000, 0.1920,  ..., 0.9911, 0.4392, 0.2330]),
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9992,  9994, 10000]),
+       col_indices=tensor([5434, 5706, 2706,  ..., 6154, 6878, 9484]),
+       values=tensor([0.7658, 0.7554, 0.9636,  ..., 0.3967, 0.1504, 0.1698]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.7239, 0.0636, 0.4781,  ..., 0.2276, 0.2279, 0.8613])
+tensor([0.1722, 0.0043, 0.4306,  ..., 0.6854, 0.6622, 0.9446])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 1.991217851638794 seconds
+Time: 0.0789031982421875 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '280711', '-ss', '10000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.449347734451294}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '219306', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 35.10541844367981}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ...,  9997,  9999, 10000]),
-       col_indices=tensor([8732,   42, 2512,  ..., 1373, 9550, 9690]),
-       values=tensor([0.4706, 0.1126, 0.6045,  ..., 0.0102, 0.1178, 0.6557]),
+tensor(crow_indices=tensor([    0,     3,     4,  ...,  9998, 10000, 10000]),
+       col_indices=tensor([3315, 5391, 5786,  ..., 9646, 2823, 7853]),
+       values=tensor([0.3181, 0.3008, 0.6377,  ..., 0.9012, 0.7876, 0.7122]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.4976, 0.6299, 0.3127,  ..., 0.9623, 0.9434, 0.7070])
+tensor([0.3394, 0.6194, 0.9177,  ..., 0.2625, 0.9091, 0.8518])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.449347734451294 seconds
+Time: 35.10541844367981 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ...,  9997,  9999, 10000]),
-       col_indices=tensor([8732,   42, 2512,  ..., 1373, 9550, 9690]),
-       values=tensor([0.4706, 0.1126, 0.6045,  ..., 0.0102, 0.1178, 0.6557]),
+tensor(crow_indices=tensor([    0,     3,     4,  ...,  9998, 10000, 10000]),
+       col_indices=tensor([3315, 5391, 5786,  ..., 9646, 2823, 7853]),
+       values=tensor([0.3181, 0.3008, 0.6377,  ..., 0.9012, 0.7876, 0.7122]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.4976, 0.6299, 0.3127,  ..., 0.9623, 0.9434, 0.7070])
+tensor([0.3394, 0.6194, 0.9177,  ..., 0.2625, 0.9091, 0.8518])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.449347734451294 seconds
+Time: 35.10541844367981 seconds
 
-[40.09, 39.2, 39.41, 39.39, 39.38, 42.02, 40.51, 39.25, 39.25, 39.51]
-[98.5]
-13.092127323150635
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 280711, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.449347734451294, 'TIME_S_1KI': 0.03722457521953644, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1289.5745413303375, 'W': 98.5}
-[40.09, 39.2, 39.41, 39.39, 39.38, 42.02, 40.51, 39.25, 39.25, 39.51, 40.09, 39.17, 39.95, 40.04, 39.24, 39.23, 39.51, 39.16, 39.2, 39.1]
-713.3050000000001
-35.66525
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 280711, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.449347734451294, 'TIME_S_1KI': 0.03722457521953644, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1289.5745413303375, 'W': 98.5, 'J_1KI': 4.593957989998032, 'W_1KI': 0.35089469240606885, 'W_D': 62.83475, 'J_D': 822.6405473183394, 'W_D_1KI': 0.22384142409809377, 'J_D_1KI': 0.0007974088086968226}
+[40.99, 41.44, 39.95, 40.1, 39.6, 39.61, 39.97, 41.1, 39.92, 39.69]
+[97.9]
+10.801499843597412
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 219306, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 35.10541844367981, 'TIME_S_1KI': 0.16007504784948795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1057.4668346881867, 'W': 97.9}
+[40.99, 41.44, 39.95, 40.1, 39.6, 39.61, 39.97, 41.1, 39.92, 39.69, 40.29, 40.15, 39.6, 39.59, 39.38, 39.37, 39.69, 39.8, 39.74, 39.5]
+719.2449999999999
+35.96225
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 219306, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 35.10541844367981, 'TIME_S_1KI': 0.16007504784948795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1057.4668346881867, 'W': 97.9, 'J_1KI': 4.821878264562696, 'W_1KI': 0.4464082150055174, 'W_D': 61.93775000000001, 'J_D': 669.0205969377757, 'W_D_1KI': 0.2824261534112154, 'J_D_1KI': 0.001287817722320481}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.json
index 9665b3b..31e8c17 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 193546, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.657205820083618, "TIME_S_1KI": 0.05506290917964524, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1424.268603067398, "W": 108.67, "J_1KI": 7.358811874528009, "W_1KI": 0.5614685914459612, "W_D": 72.91375, "J_D": 955.6341663467883, "W_D_1KI": 0.3767256879501513, "J_D_1KI": 0.001946440060503195}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 190785, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.442018032073975, "TIME_S_1KI": 0.054731860639326856, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1412.9676432013512, "W": 108.65, "J_1KI": 7.406073030905738, "W_1KI": 0.5694892156091936, "W_D": 72.7955, "J_D": 946.6883209449053, "W_D_1KI": 0.38155777445815975, "J_D_1KI": 0.0019999359197953705}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.output
index 77b5a62..eb0c584 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.025844097137451172}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.023200273513793945}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     12,     28,  ...,  99969,  99983,
+tensor(crow_indices=tensor([     0,      6,     16,  ...,  99984,  99994,
                             100000]),
-       col_indices=tensor([1079, 2122, 3254,  ..., 9373, 9823, 9958]),
-       values=tensor([0.1589, 0.8596, 0.7837,  ..., 0.1493, 0.1272, 0.2084]),
+       col_indices=tensor([ 877, 1361, 5328,  ..., 2956, 4479, 8265]),
+       values=tensor([0.7502, 0.1905, 0.1800,  ..., 0.1428, 0.2404, 0.5567]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0719, 0.4122, 0.7875,  ..., 0.0407, 0.8322, 0.6511])
+tensor([0.3837, 0.7818, 0.3585,  ..., 0.1122, 0.3287, 0.9396])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 0.025844097137451172 seconds
+Time: 0.023200273513793945 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '40628', '-ss', '10000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.4707634449005127}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '45258', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.860283851623535}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     12,     18,  ...,  99974,  99987,
+tensor(crow_indices=tensor([     0,      9,     24,  ...,  99972,  99984,
                             100000]),
-       col_indices=tensor([ 792, 1032, 1238,  ..., 8561, 8731, 9370]),
-       values=tensor([0.4488, 0.9659, 0.1268,  ..., 0.7863, 0.6709, 0.3638]),
+       col_indices=tensor([ 507,  571, 1974,  ..., 8031, 8511, 8620]),
+       values=tensor([0.4538, 0.2010, 0.5381,  ..., 0.8680, 0.9127, 0.3188]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8213, 0.7389, 0.9585,  ..., 0.8858, 0.0787, 0.3979])
+tensor([0.4786, 0.3866, 0.1367,  ..., 0.0637, 0.2113, 0.9673])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 2.4707634449005127 seconds
+Time: 2.860283851623535 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '172656', '-ss', '10000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.366683721542358}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '166140', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.143608093261719}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     10,     24,  ...,  99973,  99986,
+tensor(crow_indices=tensor([     0,      8,     20,  ...,  99973,  99987,
                             100000]),
-       col_indices=tensor([ 684, 3301, 3344,  ..., 8499, 8709, 9229]),
-       values=tensor([0.0104, 0.6771, 0.5927,  ..., 0.6883, 0.2524, 0.4550]),
+       col_indices=tensor([ 842, 2941, 3621,  ..., 6856, 7349, 9824]),
+       values=tensor([0.3470, 0.6068, 0.6754,  ..., 0.9311, 0.7786, 0.1425]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4786, 0.6837, 0.1379,  ..., 0.3005, 0.2266, 0.1673])
+tensor([0.7247, 0.2486, 0.0596,  ..., 0.5982, 0.5318, 0.1045])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,19 +56,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 9.366683721542358 seconds
+Time: 9.143608093261719 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '193546', '-ss', '10000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.657205820083618}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '190785', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.442018032073975}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     13,     24,  ...,  99982,  99990,
+tensor(crow_indices=tensor([     0,     13,     23,  ...,  99982,  99991,
                             100000]),
-       col_indices=tensor([ 667,  823, 2535,  ..., 7218, 8112, 8309]),
-       values=tensor([0.9044, 0.9079, 0.6825,  ..., 0.1587, 0.6143, 0.0618]),
+       col_indices=tensor([1528, 2535, 3076,  ..., 7834, 8468, 8792]),
+       values=tensor([0.1851, 0.9405, 0.0866,  ..., 0.2777, 0.6232, 0.3496]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.5914, 0.6686, 0.5823,  ..., 0.5362, 0.3609, 0.2297])
+tensor([0.1487, 0.9958, 0.2037,  ..., 0.3870, 0.3266, 0.1189])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -76,16 +76,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.657205820083618 seconds
+Time: 10.442018032073975 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     13,     24,  ...,  99982,  99990,
+tensor(crow_indices=tensor([     0,     13,     23,  ...,  99982,  99991,
                             100000]),
-       col_indices=tensor([ 667,  823, 2535,  ..., 7218, 8112, 8309]),
-       values=tensor([0.9044, 0.9079, 0.6825,  ..., 0.1587, 0.6143, 0.0618]),
+       col_indices=tensor([1528, 2535, 3076,  ..., 7834, 8468, 8792]),
+       values=tensor([0.1851, 0.9405, 0.0866,  ..., 0.2777, 0.6232, 0.3496]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.5914, 0.6686, 0.5823,  ..., 0.5362, 0.3609, 0.2297])
+tensor([0.1487, 0.9958, 0.2037,  ..., 0.3870, 0.3266, 0.1189])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -93,13 +93,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.657205820083618 seconds
+Time: 10.442018032073975 seconds
 
-[40.9, 39.63, 39.53, 39.38, 39.44, 39.45, 39.92, 39.64, 39.85, 39.99]
-[108.67]
-13.106364250183105
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 193546, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.657205820083618, 'TIME_S_1KI': 0.05506290917964524, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1424.268603067398, 'W': 108.67}
-[40.9, 39.63, 39.53, 39.38, 39.44, 39.45, 39.92, 39.64, 39.85, 39.99, 39.96, 39.48, 39.45, 39.41, 39.63, 39.38, 40.94, 39.79, 39.91, 39.74]
-715.125
-35.75625
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 193546, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.657205820083618, 'TIME_S_1KI': 0.05506290917964524, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1424.268603067398, 'W': 108.67, 'J_1KI': 7.358811874528009, 'W_1KI': 0.5614685914459612, 'W_D': 72.91375, 'J_D': 955.6341663467883, 'W_D_1KI': 0.3767256879501513, 'J_D_1KI': 0.001946440060503195}
+[41.13, 39.88, 39.62, 39.91, 39.95, 40.5, 39.94, 39.62, 40.31, 39.46]
+[108.65]
+13.004764318466187
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 190785, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.442018032073975, 'TIME_S_1KI': 0.054731860639326856, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1412.9676432013512, 'W': 108.65}
+[41.13, 39.88, 39.62, 39.91, 39.95, 40.5, 39.94, 39.62, 40.31, 39.46, 40.18, 39.45, 39.6, 39.53, 40.04, 39.99, 39.86, 39.34, 39.48, 39.37]
+717.0899999999999
+35.854499999999994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 190785, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.442018032073975, 'TIME_S_1KI': 0.054731860639326856, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1412.9676432013512, 'W': 108.65, 'J_1KI': 7.406073030905738, 'W_1KI': 0.5694892156091936, 'W_D': 72.7955, 'J_D': 946.6883209449053, 'W_D_1KI': 0.38155777445815975, 'J_D_1KI': 0.0019999359197953705}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.json
index 4c6ec73..88ae591 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 102691, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.217256307601929, "TIME_S_1KI": 0.09949514862648069, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1713.9247414016722, "W": 132.92, "J_1KI": 16.690116382172462, "W_1KI": 1.294368542520766, "W_D": 96.75025, "J_D": 1247.5372194688318, "W_D_1KI": 0.9421492633239523, "J_D_1KI": 0.009174604038561825}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 103627, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.609182357788086, "TIME_S_1KI": 0.10237855344445063, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1720.314517021179, "W": 134.76, "J_1KI": 16.6010259586901, "W_1KI": 1.3004332847616933, "W_D": 98.40899999999999, "J_D": 1256.266186594963, "W_D_1KI": 0.9496463276945197, "J_D_1KI": 0.009164082022007004}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.output
index 7818ee2..9074ecb 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.027860403060913086}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.0329434871673584}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      86,     184,  ...,  999787,
-                             999901, 1000000]),
-       col_indices=tensor([  81,   93,  211,  ..., 9891, 9936, 9983]),
-       values=tensor([0.0273, 0.9948, 0.2764,  ..., 0.0318, 0.5538, 0.8532]),
+tensor(crow_indices=tensor([      0,     121,     243,  ...,  999828,
+                             999916, 1000000]),
+       col_indices=tensor([  22,   79,  111,  ..., 9424, 9628, 9890]),
+       values=tensor([0.5175, 0.7264, 0.7156,  ..., 0.6361, 0.8828, 0.5482]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8459, 0.7440, 0.9932,  ..., 0.5464, 0.7654, 0.2266])
+tensor([0.5630, 0.9387, 0.8377,  ..., 0.1993, 0.7981, 0.3313])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 0.027860403060913086 seconds
+Time: 0.0329434871673584 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '37687', '-ss', '10000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.853411912918091}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '31872', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.229402542114258}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      90,     178,  ...,  999807,
-                             999899, 1000000]),
-       col_indices=tensor([   9,   87,  435,  ..., 9776, 9821, 9947]),
-       values=tensor([0.6051, 0.3509, 0.6551,  ..., 0.3060, 0.1178, 0.2325]),
+tensor(crow_indices=tensor([      0,     102,     190,  ...,  999801,
+                             999892, 1000000]),
+       col_indices=tensor([   5,   53,  116,  ..., 9752, 9921, 9934]),
+       values=tensor([0.1910, 0.3732, 0.9811,  ..., 0.5962, 0.0722, 0.9092]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6802, 0.0969, 0.8232,  ..., 0.8757, 0.6573, 0.4893])
+tensor([0.7610, 0.7524, 0.2994,  ..., 0.6189, 0.3748, 0.4768])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 3.853411912918091 seconds
+Time: 3.229402542114258 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '102691', '-ss', '10000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.217256307601929}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '103627', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.609182357788086}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      98,     191,  ...,  999800,
-                             999904, 1000000]),
-       col_indices=tensor([  18,   19,   89,  ..., 9675, 9719, 9959]),
-       values=tensor([0.5811, 0.2000, 0.4195,  ..., 0.8918, 0.7545, 0.5786]),
+tensor(crow_indices=tensor([      0,     111,     198,  ...,  999814,
+                             999913, 1000000]),
+       col_indices=tensor([  10,  117,  189,  ..., 9629, 9777, 9974]),
+       values=tensor([0.2221, 0.1933, 0.3520,  ..., 0.0590, 0.4848, 0.5886]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.3032, 0.6522, 0.8844,  ..., 0.7793, 0.6874, 0.5546])
+tensor([0.5413, 0.7704, 0.2091,  ..., 0.3119, 0.8409, 0.8046])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.217256307601929 seconds
+Time: 10.609182357788086 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      98,     191,  ...,  999800,
-                             999904, 1000000]),
-       col_indices=tensor([  18,   19,   89,  ..., 9675, 9719, 9959]),
-       values=tensor([0.5811, 0.2000, 0.4195,  ..., 0.8918, 0.7545, 0.5786]),
+tensor(crow_indices=tensor([      0,     111,     198,  ...,  999814,
+                             999913, 1000000]),
+       col_indices=tensor([  10,  117,  189,  ..., 9629, 9777, 9974]),
+       values=tensor([0.2221, 0.1933, 0.3520,  ..., 0.0590, 0.4848, 0.5886]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.3032, 0.6522, 0.8844,  ..., 0.7793, 0.6874, 0.5546])
+tensor([0.5413, 0.7704, 0.2091,  ..., 0.3119, 0.8409, 0.8046])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.217256307601929 seconds
+Time: 10.609182357788086 seconds
 
-[40.93, 39.91, 40.43, 39.4, 39.51, 40.07, 39.51, 39.35, 39.39, 44.7]
-[132.92]
-12.894408226013184
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102691, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.217256307601929, 'TIME_S_1KI': 0.09949514862648069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1713.9247414016722, 'W': 132.92}
-[40.93, 39.91, 40.43, 39.4, 39.51, 40.07, 39.51, 39.35, 39.39, 44.7, 40.04, 39.9, 39.44, 39.42, 39.81, 39.87, 45.14, 39.82, 39.78, 39.62]
-723.395
-36.16975
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102691, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.217256307601929, 'TIME_S_1KI': 0.09949514862648069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1713.9247414016722, 'W': 132.92, 'J_1KI': 16.690116382172462, 'W_1KI': 1.294368542520766, 'W_D': 96.75025, 'J_D': 1247.5372194688318, 'W_D_1KI': 0.9421492633239523, 'J_D_1KI': 0.009174604038561825}
+[41.07, 40.24, 40.08, 40.72, 40.0, 39.5, 39.52, 44.93, 40.06, 39.45]
+[134.76]
+12.765765190124512
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 103627, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.609182357788086, 'TIME_S_1KI': 0.10237855344445063, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1720.314517021179, 'W': 134.76}
+[41.07, 40.24, 40.08, 40.72, 40.0, 39.5, 39.52, 44.93, 40.06, 39.45, 40.32, 39.39, 39.52, 40.09, 39.8, 44.62, 39.58, 39.55, 39.35, 39.3]
+727.02
+36.351
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 103627, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.609182357788086, 'TIME_S_1KI': 0.10237855344445063, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1720.314517021179, 'W': 134.76, 'J_1KI': 16.6010259586901, 'W_1KI': 1.3004332847616933, 'W_D': 98.40899999999999, 'J_D': 1256.266186594963, 'W_D_1KI': 0.9496463276945197, 'J_D_1KI': 0.009164082022007004}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.json
index 52cad91..515b811 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 27775, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.346900463104248, "TIME_S_1KI": 0.3725256692386768, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2138.1740951538086, "W": 151.25, "J_1KI": 76.98196562209931, "W_1KI": 5.445544554455445, "W_D": 115.164, "J_D": 1628.037563598633, "W_D_1KI": 4.146318631863187, "J_D_1KI": 0.1492823989869734}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 27766, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.416308164596558, "TIME_S_1KI": 0.37514615589557576, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2173.3968332290647, "W": 149.04, "J_1KI": 78.27547479756049, "W_1KI": 5.367715911546496, "W_D": 113.1685, "J_D": 1650.2956221234797, "W_D_1KI": 4.0757941367139665, "J_D_1KI": 0.14679082823287354}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.output
index 19caf4c..37becdc 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.07912850379943848}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.07421493530273438}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     485,     973,  ..., 4998984,
-                            4999512, 5000000]),
-       col_indices=tensor([  23,   33,   35,  ..., 9878, 9920, 9946]),
-       values=tensor([0.8956, 0.5440, 0.5650,  ..., 0.6571, 0.0981, 0.4530]),
+tensor(crow_indices=tensor([      0,     508,    1029,  ..., 4998978,
+                            4999492, 5000000]),
+       col_indices=tensor([   4,   25,   37,  ..., 9913, 9926, 9946]),
+       values=tensor([0.1924, 0.7374, 0.0518,  ..., 0.5778, 0.6928, 0.0428]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.3320, 0.0557, 0.6993,  ..., 0.8374, 0.3528, 0.6849])
+tensor([0.0706, 0.1319, 0.8441,  ..., 0.8315, 0.3967, 0.9583])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 0.07912850379943848 seconds
+Time: 0.07421493530273438 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '13269', '-ss', '10000', '-sd', '0.05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 5.016182899475098}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '14148', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 5.350146532058716}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     490,     975,  ..., 4999017,
-                            4999511, 5000000]),
-       col_indices=tensor([   5,    7,   17,  ..., 9925, 9927, 9956]),
-       values=tensor([0.3061, 0.0982, 0.7519,  ..., 0.4711, 0.1343, 0.2753]),
+tensor(crow_indices=tensor([      0,     491,    1021,  ..., 4998988,
+                            4999483, 5000000]),
+       col_indices=tensor([   2,   82,   84,  ..., 9944, 9947, 9986]),
+       values=tensor([0.2655, 0.6510, 0.4554,  ..., 0.8118, 0.7172, 0.4313]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.4300, 0.5593, 0.7816,  ..., 0.7590, 0.1985, 0.5681])
+tensor([0.3392, 0.3098, 0.7947,  ..., 0.5067, 0.5991, 0.2806])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 5.016182899475098 seconds
+Time: 5.350146532058716 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27775', '-ss', '10000', '-sd', '0.05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.346900463104248}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27766', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.416308164596558}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     516,     985,  ..., 4998986,
-                            4999503, 5000000]),
-       col_indices=tensor([   0,   38,   62,  ..., 9969, 9984, 9993]),
-       values=tensor([0.4538, 0.1922, 0.3497,  ..., 0.8541, 0.7038, 0.0561]),
+tensor(crow_indices=tensor([      0,     489,     982,  ..., 4999022,
+                            4999480, 5000000]),
+       col_indices=tensor([  17,   23,   33,  ..., 9974, 9981, 9997]),
+       values=tensor([0.3994, 0.6610, 0.6708,  ..., 0.9264, 0.3736, 0.5616]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.3516, 0.9610, 0.6827,  ..., 0.5287, 0.4040, 0.0575])
+tensor([0.2036, 0.5321, 0.8533,  ..., 0.2446, 0.1152, 0.9621])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.346900463104248 seconds
+Time: 10.416308164596558 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     516,     985,  ..., 4998986,
-                            4999503, 5000000]),
-       col_indices=tensor([   0,   38,   62,  ..., 9969, 9984, 9993]),
-       values=tensor([0.4538, 0.1922, 0.3497,  ..., 0.8541, 0.7038, 0.0561]),
+tensor(crow_indices=tensor([      0,     489,     982,  ..., 4999022,
+                            4999480, 5000000]),
+       col_indices=tensor([  17,   23,   33,  ..., 9974, 9981, 9997]),
+       values=tensor([0.3994, 0.6610, 0.6708,  ..., 0.9264, 0.3736, 0.5616]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.3516, 0.9610, 0.6827,  ..., 0.5287, 0.4040, 0.0575])
+tensor([0.2036, 0.5321, 0.8533,  ..., 0.2446, 0.1152, 0.9621])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.346900463104248 seconds
+Time: 10.416308164596558 seconds
 
-[41.25, 39.91, 39.68, 39.83, 39.58, 41.43, 40.59, 39.78, 40.26, 39.69]
-[151.25]
-14.136688232421875
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27775, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.346900463104248, 'TIME_S_1KI': 0.3725256692386768, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2138.1740951538086, 'W': 151.25}
-[41.25, 39.91, 39.68, 39.83, 39.58, 41.43, 40.59, 39.78, 40.26, 39.69, 40.82, 40.72, 40.11, 39.71, 39.6, 39.85, 39.65, 39.99, 39.77, 40.76]
-721.72
-36.086
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27775, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.346900463104248, 'TIME_S_1KI': 0.3725256692386768, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2138.1740951538086, 'W': 151.25, 'J_1KI': 76.98196562209931, 'W_1KI': 5.445544554455445, 'W_D': 115.164, 'J_D': 1628.037563598633, 'W_D_1KI': 4.146318631863187, 'J_D_1KI': 0.1492823989869734}
+[40.88, 39.74, 39.62, 39.95, 41.08, 39.43, 39.57, 39.93, 39.45, 39.37]
+[149.04]
+14.582641124725342
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27766, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.416308164596558, 'TIME_S_1KI': 0.37514615589557576, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2173.3968332290647, 'W': 149.04}
+[40.88, 39.74, 39.62, 39.95, 41.08, 39.43, 39.57, 39.93, 39.45, 39.37, 40.17, 39.68, 39.59, 39.61, 39.55, 39.52, 40.72, 39.82, 39.91, 40.1]
+717.4300000000001
+35.871500000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27766, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.416308164596558, 'TIME_S_1KI': 0.37514615589557576, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2173.3968332290647, 'W': 149.04, 'J_1KI': 78.27547479756049, 'W_1KI': 5.367715911546496, 'W_D': 113.1685, 'J_D': 1650.2956221234797, 'W_D_1KI': 4.0757941367139665, 'J_D_1KI': 0.14679082823287354}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json
index 6336d4d..4016a02 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4427, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.78234076499939, "TIME_S_1KI": 2.4355863485428935, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1855.6078462219239, "W": 121.94, "J_1KI": 419.15695645401485, "W_1KI": 27.544612604472555, "W_D": 85.48649999999999, "J_D": 1300.8809262428283, "W_D_1KI": 19.310255251863563, "J_D_1KI": 4.361927999065633}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4516, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.659436702728271, "TIME_S_1KI": 2.360371280497846, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1934.6844965219498, "W": 122.57, "J_1KI": 428.4066644202723, "W_1KI": 27.141275465013283, "W_D": 86.43999999999998, "J_D": 1364.3968987464903, "W_D_1KI": 19.140832595217002, "J_D_1KI": 4.238448316035651}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output
index 68c803b..e9b85bc 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.23713254928588867}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.25728321075439453}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      967,     1927,  ...,  9997983,
-                             9998974, 10000000]),
-       col_indices=tensor([   2,    7,   17,  ..., 9977, 9981, 9986]),
-       values=tensor([0.0113, 0.4578, 0.3712,  ..., 0.8300, 0.4518, 0.5288]),
+tensor(crow_indices=tensor([       0,     1018,     2040,  ...,  9998067,
+                             9999061, 10000000]),
+       col_indices=tensor([   3,   36,   63,  ..., 9974, 9977, 9997]),
+       values=tensor([0.8836, 0.0615, 0.3071,  ..., 0.0562, 0.8753, 0.9316]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.6464, 0.5946, 0.9135,  ..., 0.7384, 0.8851, 0.3138])
+tensor([0.4941, 0.9379, 0.9279,  ..., 0.6862, 0.5933, 0.8774])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 0.23713254928588867 seconds
+Time: 0.25728321075439453 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4427', '-ss', '10000', '-sd', '0.1', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.78234076499939}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4081', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.48789381980896}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1000,     1968,  ...,  9997976,
-                             9998957, 10000000]),
-       col_indices=tensor([  18,   35,   37,  ..., 9972, 9974, 9993]),
-       values=tensor([0.5495, 0.5155, 0.6909,  ..., 0.5748, 0.2988, 0.6189]),
+tensor(crow_indices=tensor([       0,     1009,     2010,  ...,  9997989,
+                             9998993, 10000000]),
+       col_indices=tensor([  21,   25,   26,  ..., 9986, 9990, 9997]),
+       values=tensor([0.5236, 0.7266, 0.2242,  ..., 0.1473, 0.8152, 0.7468]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.2327, 0.3005, 0.5005,  ..., 0.5867, 0.2890, 0.0524])
+tensor([0.7137, 0.8450, 0.7472,  ..., 0.9518, 0.9096, 0.7253])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 10.78234076499939 seconds
+Time: 9.48789381980896 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4516', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.659436702728271}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1000,     1968,  ...,  9997976,
-                             9998957, 10000000]),
-       col_indices=tensor([  18,   35,   37,  ..., 9972, 9974, 9993]),
-       values=tensor([0.5495, 0.5155, 0.6909,  ..., 0.5748, 0.2988, 0.6189]),
+tensor(crow_indices=tensor([       0,      991,     1952,  ...,  9997980,
+                             9999013, 10000000]),
+       col_indices=tensor([  15,   17,   23,  ..., 9988, 9992, 9997]),
+       values=tensor([0.8039, 0.3382, 0.4278,  ..., 0.1060, 0.4366, 0.4507]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.2327, 0.3005, 0.5005,  ..., 0.5867, 0.2890, 0.0524])
+tensor([0.1772, 0.5080, 0.9123,  ..., 0.8797, 0.3828, 0.1213])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +56,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 10.78234076499939 seconds
+Time: 10.659436702728271 seconds
 
-[40.15, 39.73, 39.55, 39.93, 39.94, 39.96, 44.96, 39.82, 40.15, 39.43]
-[121.94]
-15.217384338378906
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4427, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.78234076499939, 'TIME_S_1KI': 2.4355863485428935, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1855.6078462219239, 'W': 121.94}
-[40.15, 39.73, 39.55, 39.93, 39.94, 39.96, 44.96, 39.82, 40.15, 39.43, 40.99, 45.07, 39.68, 40.13, 40.01, 39.56, 39.72, 40.75, 40.01, 39.63]
-729.07
-36.453500000000005
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4427, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.78234076499939, 'TIME_S_1KI': 2.4355863485428935, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1855.6078462219239, 'W': 121.94, 'J_1KI': 419.15695645401485, 'W_1KI': 27.544612604472555, 'W_D': 85.48649999999999, 'J_D': 1300.8809262428283, 'W_D_1KI': 19.310255251863563, 'J_D_1KI': 4.361927999065633}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      991,     1952,  ...,  9997980,
+                             9999013, 10000000]),
+       col_indices=tensor([  15,   17,   23,  ..., 9988, 9992, 9997]),
+       values=tensor([0.8039, 0.3382, 0.4278,  ..., 0.1060, 0.4366, 0.4507]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1772, 0.5080, 0.9123,  ..., 0.8797, 0.3828, 0.1213])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.659436702728271 seconds
+
+[41.13, 40.13, 39.96, 40.14, 39.81, 40.27, 39.7, 39.78, 39.58, 39.57]
+[122.57]
+15.78432321548462
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4516, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.659436702728271, 'TIME_S_1KI': 2.360371280497846, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1934.6844965219498, 'W': 122.57}
+[41.13, 40.13, 39.96, 40.14, 39.81, 40.27, 39.7, 39.78, 39.58, 39.57, 41.84, 40.09, 40.2, 39.67, 39.68, 42.84, 40.03, 39.84, 39.63, 39.96]
+722.6000000000001
+36.13000000000001
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4516, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.659436702728271, 'TIME_S_1KI': 2.360371280497846, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1934.6844965219498, 'W': 122.57, 'J_1KI': 428.4066644202723, 'W_1KI': 27.141275465013283, 'W_D': 86.43999999999998, 'J_D': 1364.3968987464903, 'W_D_1KI': 19.140832595217002, 'J_D_1KI': 4.238448316035651}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json
index 3673122..278787d 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2210, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.475984573364258, "TIME_S_1KI": 4.740264512834506, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2065.6901198005676, "W": 119.44, "J_1KI": 934.701411674465, "W_1KI": 54.04524886877828, "W_D": 83.048, "J_D": 1436.2979995746614, "W_D_1KI": 37.57828054298643, "J_D_1KI": 17.00374685203006}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2205, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.094979763031006, "TIME_S_1KI": 4.578222114753291, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2048.2452651548383, "W": 120.01999999999998, "J_1KI": 928.9094173037815, "W_1KI": 54.43083900226756, "W_D": 83.94674999999998, "J_D": 1432.6240061042902, "W_D_1KI": 38.07108843537414, "J_D_1KI": 17.26579974393385}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output
index 5e9953a..20a5467 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.5103092193603516}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.4761507511138916}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2001,     3993,  ..., 19996027,
-                            19997998, 20000000]),
-       col_indices=tensor([   4,    8,   12,  ..., 9988, 9991, 9998]),
-       values=tensor([0.1397, 0.5991, 0.8904,  ..., 0.1163, 0.3047, 0.7503]),
+tensor(crow_indices=tensor([       0,     2041,     4060,  ..., 19995939,
+                            19998009, 20000000]),
+       col_indices=tensor([   5,    6,    7,  ..., 9974, 9989, 9994]),
+       values=tensor([0.7672, 0.1156, 0.6832,  ..., 0.8423, 0.9155, 0.0866]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.7325, 0.0863, 0.4494,  ..., 0.5445, 0.3494, 0.7015])
+tensor([0.0377, 0.2575, 0.6177,  ..., 0.7817, 0.7902, 0.6305])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 0.5103092193603516 seconds
+Time: 0.4761507511138916 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2057', '-ss', '10000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.769307136535645}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2205', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.094979763031006}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1965,     3996,  ..., 19995929,
-                            19997992, 20000000]),
-       col_indices=tensor([   4,    9,   15,  ..., 9975, 9986, 9992]),
-       values=tensor([0.0708, 0.7889, 0.9973,  ..., 0.4384, 0.2830, 0.3299]),
+tensor(crow_indices=tensor([       0,     2010,     4012,  ..., 19996040,
+                            19998053, 20000000]),
+       col_indices=tensor([   0,    4,    5,  ..., 9985, 9988, 9991]),
+       values=tensor([0.1815, 0.5931, 0.3008,  ..., 0.6351, 0.8074, 0.0454]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.8359, 0.1884, 0.2769,  ..., 0.8252, 0.8191, 0.5472])
+tensor([0.7445, 0.8255, 0.9105,  ..., 0.5241, 0.5119, 0.2642])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 9.769307136535645 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2210', '-ss', '10000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.475984573364258}
+Time: 10.094979763031006 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2066,     4070,  ..., 19995990,
-                            19998002, 20000000]),
-       col_indices=tensor([   1,    2,    8,  ..., 9986, 9990, 9993]),
-       values=tensor([0.6258, 0.8376, 0.0180,  ..., 0.7990, 0.4511, 0.0511]),
+tensor(crow_indices=tensor([       0,     2010,     4012,  ..., 19996040,
+                            19998053, 20000000]),
+       col_indices=tensor([   0,    4,    5,  ..., 9985, 9988, 9991]),
+       values=tensor([0.1815, 0.5931, 0.3008,  ..., 0.6351, 0.8074, 0.0454]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.7373, 0.4078, 0.5568,  ..., 0.6016, 0.2858, 0.4434])
+tensor([0.7445, 0.8255, 0.9105,  ..., 0.5241, 0.5119, 0.2642])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,30 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 10.475984573364258 seconds
+Time: 10.094979763031006 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2066,     4070,  ..., 19995990,
-                            19998002, 20000000]),
-       col_indices=tensor([   1,    2,    8,  ..., 9986, 9990, 9993]),
-       values=tensor([0.6258, 0.8376, 0.0180,  ..., 0.7990, 0.4511, 0.0511]),
-       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.7373, 0.4078, 0.5568,  ..., 0.6016, 0.2858, 0.4434])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 20000000
-Density: 0.2
-Time: 10.475984573364258 seconds
-
-[40.41, 45.21, 40.37, 40.41, 40.26, 39.65, 40.47, 41.87, 39.92, 39.65]
-[119.44]
-17.294793367385864
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2210, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.475984573364258, 'TIME_S_1KI': 4.740264512834506, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2065.6901198005676, 'W': 119.44}
-[40.41, 45.21, 40.37, 40.41, 40.26, 39.65, 40.47, 41.87, 39.92, 39.65, 40.39, 39.85, 39.69, 40.06, 40.15, 39.58, 40.59, 39.58, 39.95, 40.01]
-727.8399999999999
-36.391999999999996
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2210, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.475984573364258, 'TIME_S_1KI': 4.740264512834506, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2065.6901198005676, 'W': 119.44, 'J_1KI': 934.701411674465, 'W_1KI': 54.04524886877828, 'W_D': 83.048, 'J_D': 1436.2979995746614, 'W_D_1KI': 37.57828054298643, 'J_D_1KI': 17.00374685203006}
+[46.59, 39.84, 39.85, 39.77, 40.13, 40.49, 39.59, 39.67, 39.54, 39.65]
+[120.02]
+17.065866231918335
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2205, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.094979763031006, 'TIME_S_1KI': 4.578222114753291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2048.2452651548383, 'W': 120.01999999999998}
+[46.59, 39.84, 39.85, 39.77, 40.13, 40.49, 39.59, 39.67, 39.54, 39.65, 40.65, 39.88, 39.99, 39.88, 39.9, 39.95, 39.9, 39.56, 40.32, 39.52]
+721.465
+36.07325
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2205, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.094979763031006, 'TIME_S_1KI': 4.578222114753291, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2048.2452651548383, 'W': 120.01999999999998, 'J_1KI': 928.9094173037815, 'W_1KI': 54.43083900226756, 'W_D': 83.94674999999998, 'J_D': 1432.6240061042902, 'W_D_1KI': 38.07108843537414, 'J_D_1KI': 17.26579974393385}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json
index 21b0219..49ab440 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1434, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.430570602416992, "TIME_S_1KI": 7.273759136971403, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2171.006755914688, "W": 115.95999999999998, "J_1KI": 1513.9517126322787, "W_1KI": 80.8647140864714, "W_D": 79.27299999999998, "J_D": 1484.1515915973182, "W_D_1KI": 55.281032078103195, "J_D_1KI": 38.55023157468842}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1481, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.553231000900269, "TIME_S_1KI": 7.125746793315509, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2207.1686755657197, "W": 116.60000000000001, "J_1KI": 1490.3232110504523, "W_1KI": 78.73058744091831, "W_D": 80.35075, "J_D": 1520.9919250275493, "W_D_1KI": 54.254388926401084, "J_D_1KI": 36.6336184513174}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output
index 29bb118..88bfec9 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.7320935726165771}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.7448434829711914}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2976,     5887,  ..., 29993981,
-                            29996974, 30000000]),
-       col_indices=tensor([   2,   12,   13,  ..., 9995, 9997, 9999]),
-       values=tensor([0.2872, 0.6919, 0.0045,  ..., 0.7234, 0.8152, 0.1470]),
+tensor(crow_indices=tensor([       0,     3028,     6029,  ..., 29993945,
+                            29997046, 30000000]),
+       col_indices=tensor([   3,    6,    9,  ..., 9989, 9994, 9995]),
+       values=tensor([0.2334, 0.2063, 0.5218,  ..., 0.4045, 0.0852, 0.7956]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.3759, 0.5048, 0.7452,  ..., 0.9323, 0.0206, 0.6020])
+tensor([0.9496, 0.6426, 0.7887,  ..., 0.2739, 0.8783, 0.2377])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 0.7320935726165771 seconds
+Time: 0.7448434829711914 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1434', '-ss', '10000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.430570602416992}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1409', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.988589763641357}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2946,     5856,  ..., 29993956,
-                            29997054, 30000000]),
-       col_indices=tensor([   1,    3,   10,  ..., 9992, 9994, 9995]),
-       values=tensor([0.6658, 0.8893, 0.2640,  ..., 0.2436, 0.9944, 0.7745]),
+tensor(crow_indices=tensor([       0,     3038,     6082,  ..., 29994099,
+                            29997000, 30000000]),
+       col_indices=tensor([   1,   11,   12,  ..., 9989, 9991, 9992]),
+       values=tensor([0.0184, 0.7499, 0.9505,  ..., 0.0295, 0.2513, 0.0037]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.7478, 0.4417, 0.0487,  ..., 0.7713, 0.8445, 0.5646])
+tensor([0.7073, 0.1266, 0.0657,  ..., 0.5297, 0.8116, 0.3350])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 10.430570602416992 seconds
+Time: 9.988589763641357 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1481', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.553231000900269}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2946,     5856,  ..., 29993956,
-                            29997054, 30000000]),
-       col_indices=tensor([   1,    3,   10,  ..., 9992, 9994, 9995]),
-       values=tensor([0.6658, 0.8893, 0.2640,  ..., 0.2436, 0.9944, 0.7745]),
+tensor(crow_indices=tensor([       0,     3022,     5996,  ..., 29993950,
+                            29996975, 30000000]),
+       col_indices=tensor([   0,    1,    4,  ..., 9978, 9992, 9996]),
+       values=tensor([0.2043, 0.8510, 0.1478,  ..., 0.0867, 0.6823, 0.3008]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.7478, 0.4417, 0.0487,  ..., 0.7713, 0.8445, 0.5646])
+tensor([0.1816, 0.4570, 0.5794,  ..., 0.4377, 0.8669, 0.6802])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +56,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 10.430570602416992 seconds
+Time: 10.553231000900269 seconds
 
-[40.39, 40.27, 40.31, 40.01, 39.78, 40.12, 45.36, 39.94, 40.02, 52.21]
-[115.96]
-18.722031354904175
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1434, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.430570602416992, 'TIME_S_1KI': 7.273759136971403, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2171.006755914688, 'W': 115.95999999999998}
-[40.39, 40.27, 40.31, 40.01, 39.78, 40.12, 45.36, 39.94, 40.02, 52.21, 41.63, 40.16, 40.0, 40.35, 41.93, 39.55, 39.78, 39.55, 39.63, 39.73]
-733.74
-36.687
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1434, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.430570602416992, 'TIME_S_1KI': 7.273759136971403, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2171.006755914688, 'W': 115.95999999999998, 'J_1KI': 1513.9517126322787, 'W_1KI': 80.8647140864714, 'W_D': 79.27299999999998, 'J_D': 1484.1515915973182, 'W_D_1KI': 55.281032078103195, 'J_D_1KI': 38.55023157468842}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3022,     5996,  ..., 29993950,
+                            29996975, 30000000]),
+       col_indices=tensor([   0,    1,    4,  ..., 9978, 9992, 9996]),
+       values=tensor([0.2043, 0.8510, 0.1478,  ..., 0.0867, 0.6823, 0.3008]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.1816, 0.4570, 0.5794,  ..., 0.4377, 0.8669, 0.6802])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.553231000900269 seconds
+
+[40.7, 39.95, 41.13, 39.75, 39.96, 39.81, 39.8, 40.3, 40.1, 40.79]
+[116.6]
+18.929405450820923
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1481, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.553231000900269, 'TIME_S_1KI': 7.125746793315509, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2207.1686755657197, 'W': 116.60000000000001}
+[40.7, 39.95, 41.13, 39.75, 39.96, 39.81, 39.8, 40.3, 40.1, 40.79, 40.83, 39.9, 39.87, 39.53, 39.71, 39.99, 39.53, 44.76, 40.0, 39.47]
+724.985
+36.24925
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1481, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.553231000900269, 'TIME_S_1KI': 7.125746793315509, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2207.1686755657197, 'W': 116.60000000000001, 'J_1KI': 1490.3232110504523, 'W_1KI': 78.73058744091831, 'W_D': 80.35075, 'J_D': 1520.9919250275493, 'W_D_1KI': 54.254388926401084, 'J_D_1KI': 36.6336184513174}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..251a245
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1051, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.514868974685669, "TIME_S_1KI": 10.004632706646689, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2300.9219420003888, "W": 111.81999999999998, "J_1KI": 2189.269212179247, "W_1KI": 106.39391056137009, "W_D": 75.59924999999998, "J_D": 1555.6069855461712, "W_D_1KI": 71.93078020932444, "J_D_1KI": 68.44032370059413}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..eda5681
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.4.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.9982414245605469}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4012,     7981,  ..., 39991879,
+                            39995963, 40000000]),
+       col_indices=tensor([  10,   11,   14,  ..., 9991, 9992, 9999]),
+       values=tensor([0.1419, 0.4895, 0.7407,  ..., 0.3538, 0.7528, 0.8593]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.6249, 0.2212, 0.6359,  ..., 0.9566, 0.8492, 0.7458])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 0.9982414245605469 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1051', '-ss', '10000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.514868974685669}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3996,     7991,  ..., 39991996,
+                            39996043, 40000000]),
+       col_indices=tensor([   0,    1,    8,  ..., 9995, 9997, 9998]),
+       values=tensor([0.0226, 0.5124, 0.4820,  ..., 0.6985, 0.7901, 0.5930]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.2895, 0.6282, 0.6522,  ..., 0.0069, 0.2490, 0.3252])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 10.514868974685669 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3996,     7991,  ..., 39991996,
+                            39996043, 40000000]),
+       col_indices=tensor([   0,    1,    8,  ..., 9995, 9997, 9998]),
+       values=tensor([0.0226, 0.5124, 0.4820,  ..., 0.6985, 0.7901, 0.5930]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.2895, 0.6282, 0.6522,  ..., 0.0069, 0.2490, 0.3252])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 10.514868974685669 seconds
+
+[40.61, 41.42, 39.83, 39.75, 39.61, 39.54, 39.65, 39.44, 40.5, 39.44]
+[111.82]
+20.5770161151886
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1051, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.514868974685669, 'TIME_S_1KI': 10.004632706646689, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2300.9219420003888, 'W': 111.81999999999998}
+[40.61, 41.42, 39.83, 39.75, 39.61, 39.54, 39.65, 39.44, 40.5, 39.44, 41.27, 40.95, 39.61, 39.66, 40.18, 44.62, 39.52, 39.76, 39.99, 39.45]
+724.415
+36.220749999999995
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1051, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.514868974685669, 'TIME_S_1KI': 10.004632706646689, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2300.9219420003888, 'W': 111.81999999999998, 'J_1KI': 2189.269212179247, 'W_1KI': 106.39391056137009, 'W_D': 75.59924999999998, 'J_D': 1555.6069855461712, 'W_D_1KI': 71.93078020932444, 'J_D_1KI': 68.44032370059413}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..c052723
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 895, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.780815124511719, "TIME_S_1KI": 12.045603491074546, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2479.408223028183, "W": 110.17, "J_1KI": 2770.288517349925, "W_1KI": 123.09497206703911, "W_D": 73.96549999999999, "J_D": 1664.6153119759558, "W_D_1KI": 82.64301675977653, "J_D_1KI": 92.33856621204082}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..04c06a7
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.5.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 1.1728613376617432}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     5004,    10037,  ..., 49990101,
+                            49995102, 50000000]),
+       col_indices=tensor([   1,    2,    4,  ..., 9993, 9994, 9999]),
+       values=tensor([0.6341, 0.8760, 0.7654,  ..., 0.7207, 0.8990, 0.3578]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.6162, 0.5934, 0.0411,  ..., 0.5420, 0.7601, 0.3985])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 1.1728613376617432 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '895', '-ss', '10000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.780815124511719}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4874,     9936,  ..., 49989936,
+                            49995021, 50000000]),
+       col_indices=tensor([   2,    4,    5,  ..., 9994, 9996, 9998]),
+       values=tensor([0.2487, 0.9013, 0.3272,  ..., 0.3285, 0.4935, 0.2106]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.1115, 0.0772, 0.3726,  ..., 0.1024, 0.4966, 0.6352])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 10.780815124511719 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4874,     9936,  ..., 49989936,
+                            49995021, 50000000]),
+       col_indices=tensor([   2,    4,    5,  ..., 9994, 9996, 9998]),
+       values=tensor([0.2487, 0.9013, 0.3272,  ..., 0.3285, 0.4935, 0.2106]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.1115, 0.0772, 0.3726,  ..., 0.1024, 0.4966, 0.6352])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 10.780815124511719 seconds
+
+[41.06, 40.51, 39.91, 39.52, 39.91, 41.08, 40.18, 39.56, 39.99, 45.52]
+[110.17]
+22.50529384613037
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 895, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.780815124511719, 'TIME_S_1KI': 12.045603491074546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2479.408223028183, 'W': 110.17}
+[41.06, 40.51, 39.91, 39.52, 39.91, 41.08, 40.18, 39.56, 39.99, 45.52, 40.27, 39.95, 40.09, 41.56, 39.52, 40.05, 39.98, 39.48, 39.55, 39.65]
+724.09
+36.2045
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 895, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.780815124511719, 'TIME_S_1KI': 12.045603491074546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2479.408223028183, 'W': 110.17, 'J_1KI': 2770.288517349925, 'W_1KI': 123.09497206703911, 'W_D': 73.96549999999999, 'J_D': 1664.6153119759558, 'W_D_1KI': 82.64301675977653, 'J_D_1KI': 92.33856621204082}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.json
index 7eea71b..a50e2bb 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 349456, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.173964023590088, "TIME_S_1KI": 0.03197531026392475, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1252.9686733055114, "W": 97.63, "J_1KI": 3.585483360724988, "W_1KI": 0.2793770889611282, "W_D": 61.798, "J_D": 793.1061976127625, "W_D_1KI": 0.17684057506524428, "J_D_1KI": 0.0005060453249200021}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 360318, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.527426958084106, "TIME_S_1KI": 0.029217044272237598, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1308.6020423936845, "W": 97.79, "J_1KI": 3.631797585448644, "W_1KI": 0.2713991529704317, "W_D": 61.57600000000001, "J_D": 823.9950850028993, "W_D_1KI": 0.17089348852957667, "J_D_1KI": 0.00047428518289282433}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.output
index 518e71c..fe1a991 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.output
@@ -1,1024 +1,373 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.018783092498779297}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
-       col_indices=tensor([7710,  770, 4407, 1870, 7174, 5318, 1450, 1184, 2850,
-                           4403, 2123,  255,  810, 7400,  565, 3550, 9613, 7496,
-                           6015,  975, 9271, 7248, 4063, 4805, 7196, 7869, 4589,
-                           3665,   14, 7810, 5136, 5919, 2466, 4173, 9881, 3733,
-                           1870, 1230, 6453, 5008, 3988, 9434, 1211, 7162, 7902,
-                           9745, 4411, 5286, 1509, 4833, 7496, 1798,    4, 3586,
-                           7060, 2811, 8112, 4764, 9435, 3836, 5882, 3125, 9850,
-                           2118, 4178, 9802, 8785, 5451, 5614, 8254, 6937, 7688,
-                           7331, 3332, 6913, 3221, 6350, 4032, 1334, 7216,  129,
-                           9945, 7932, 9294, 1550, 7754, 4771, 5013,  294, 9524,
-                           9420, 8530, 7837, 2039, 9838, 1792, 5287, 5887,  573,
-                           7513, 5802, 9931, 5757, 4848, 2498, 9852,  172, 7356,
-                           7917, 1181,  539, 4514, 1534, 8369, 4234, 9382, 7442,
-                           6827, 9408, 9891, 2006, 5444, 9700, 4515, 5848, 7202,
-                           8465, 8202, 7227, 5428, 6226, 7163, 1915, 9424, 7937,
-                           3286, 7876,  401, 9372, 5269,  534, 8486, 5070, 2186,
-                           2780, 8211, 5164, 9491, 8801, 2669, 4834, 6041,  906,
-                           6304, 6419, 5278, 8990, 5734, 1221,  935, 3464, 2356,
-                           7735, 7209, 5212, 1318, 8268, 9084, 3729,  231, 1156,
-                           1335, 6818, 8619, 6611, 8933, 2141,  101, 2488, 1604,
-                           1138, 1432, 2355, 1314, 1098, 1619, 4109, 4763, 1605,
-                           2774, 7361,  843, 6381, 1534, 9790, 4775, 7164, 7105,
-                           6239, 3980,  382,  411, 7505, 7659, 3367, 1766, 3195,
-                           9470, 2528, 9322, 6959, 2216,  491, 8725, 2371, 4382,
-                           5718, 5217, 7058, 4457, 5435, 1249, 9086, 4957, 5280,
-                           7131, 4958, 8168, 4939, 4037, 3075, 3367, 7266, 6066,
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-                           5318, 2866, 8999, 8361, 5772, 5641,  171, 8207, 6823,
-                           1799, 2385, 3933, 8099, 9437, 4173, 4273, 4399, 7947,
-                            538, 8128, 6522, 1001, 6458, 9133,  842, 3215, 3937,
-                           8256, 4826, 8778, 6085, 7131, 1758, 1134, 7002, 8167,
-                            700, 4064, 7323, 8384, 4717, 7001, 6775, 3204,  701,
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-                           4722, 9012, 2854, 9405, 9514, 7715, 2699, 2312, 9518,
-                           1435, 2191,   85, 1881, 1234, 2331, 4237, 5757, 7932,
-                           9712, 3332, 7705, 1404, 8567, 6763, 9879, 1736, 3894,
-                           5687, 8039, 1164,  621, 2677, 2713, 7254, 2093, 2442,
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-                           5449, 2272, 9439,  522, 2753, 9349, 9909, 7912, 2140,
-                           3823,  565, 5366, 4263, 9476, 8456, 9778, 4716, 8861,
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-                           8141, 2407, 5529, 1677, 2350, 6893, 7197, 3753, 5887,
-                           8876, 4491, 4643, 8990,  596, 8161, 7846, 2289, 9180,
-                           7037, 5854, 6062, 8432,  824, 4812, 3129, 8178, 4110,
-                           9628, 3692,  997, 3675, 9501, 8580, 8343, 4389, 3215,
-                           2282, 3336, 4379, 5429, 3859, 5531, 1391, 1842, 9054,
-                           3059, 3924, 4109, 4585, 1402, 5590, 2428, 5822, 4347,
-                           9652, 4937, 8518, 2936, 4541, 4664, 7316, 3628, 1576,
-                            100, 6717, 6869, 5782, 4769, 6381,  420, 7325, 9706,
-                           5457, 6444, 6589,  843, 7648, 9948,  200, 1616, 3739,
-                           1287, 4261, 1655, 8236, 7002,  480, 4835, 8836, 2589,
-                           7842, 2577, 3714, 9973, 9407, 6133, 4479, 8904,  166,
-                           7918, 5703, 6259, 4920, 9996, 9898, 9260, 9581, 5155,
-                           2417, 2267, 2847, 3259, 4611, 7016, 3338, 8196, 6441,
-                           9820, 9477,  466, 5776, 9899,  731, 9644, 9431,  476,
-                           9327, 5566, 7989, 3039, 9320, 1202,  381, 1915, 2369,
-                           3818, 8016, 6121, 6692, 1702, 1859, 3194, 8553, 3794,
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-                           9690, 7905, 3279, 6036, 7950, 6116, 3751, 5294, 4615,
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-                           2905, 2774, 1985, 3126, 6084, 5700, 1229, 5879, 8154,
-                           2148, 4874, 2092, 9170, 3482,  664, 5554, 1113, 9484,
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-Time: 0.018783092498779297 seconds
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-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '55901', '-ss', '10000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.679638147354126}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
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-       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.3431, 0.4493, 0.0337,  ..., 0.1419, 0.4440, 0.1516])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 1.679638147354126 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '349456', '-ss', '10000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.173964023590088}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.02082538604736328}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    1,    1,  ..., 1000, 1000, 1000]),
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+                      1.9882e-01, 1.1897e-01, 8.5784e-01, 6.6522e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.5348, 0.7986, 0.2200,  ..., 0.0453, 0.2085, 0.0080])
+tensor([0.2548, 0.7292, 0.0167,  ..., 0.4496, 0.0224, 0.9290])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1026,268 +375,271 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 11.173964023590088 seconds
+Time: 0.02082538604736328 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '50419', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.4692554473876953}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    1,    1,  ..., 1000, 1000, 1000]),
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+                      0.2658, 0.8369, 0.0505, 0.3261, 0.4490, 0.3279, 0.1204,
+                      0.2416, 0.3865, 0.1735, 0.9687, 0.4705, 0.5012, 0.7498,
+                      0.1842, 0.9457, 0.2389, 0.2729, 0.0209, 0.5914, 0.4534,
+                      0.2782, 0.0655, 0.6488, 0.9723, 0.4052, 0.1188, 0.3484,
+                      0.5745, 0.4265, 0.9198, 0.6865, 0.4844, 0.4980, 0.6529,
+                      0.7642, 0.5329, 0.5549, 0.3109, 0.3843, 0.2091]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.5348, 0.7986, 0.2200,  ..., 0.0453, 0.2085, 0.0080])
+tensor([0.6698, 0.4290, 0.4685,  ..., 0.2920, 0.9011, 0.2731])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1295,13 +647,554 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 11.173964023590088 seconds
+Time: 1.4692554473876953 seconds
 
-[40.48, 41.2, 39.7, 39.62, 39.96, 39.55, 39.84, 39.41, 40.04, 39.52]
-[97.63]
-12.83384895324707
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 349456, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.173964023590088, 'TIME_S_1KI': 0.03197531026392475, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1252.9686733055114, 'W': 97.63}
-[40.48, 41.2, 39.7, 39.62, 39.96, 39.55, 39.84, 39.41, 40.04, 39.52, 40.24, 39.6, 39.87, 39.86, 39.87, 39.92, 39.37, 39.31, 39.74, 39.32]
-716.6399999999999
-35.831999999999994
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 349456, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.173964023590088, 'TIME_S_1KI': 0.03197531026392475, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1252.9686733055114, 'W': 97.63, 'J_1KI': 3.585483360724988, 'W_1KI': 0.2793770889611282, 'W_D': 61.798, 'J_D': 793.1061976127625, 'W_D_1KI': 0.17684057506524428, 'J_D_1KI': 0.0005060453249200021}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '360318', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.527426958084106}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([5766, 7973, 2780, 3211, 9594, 8202, 4276, 3468, 2763,
+                           8172,  125, 6130,  209, 4027, 1805, 4863, 7827, 7923,
+                           3604, 7708,  857, 2121, 5611, 3524, 1803, 5126, 6664,
+                           5422, 9198, 8270, 9855, 8562, 4744, 5249, 7489, 3063,
+                           6337,  838, 6681, 3140, 8354, 4214, 3457, 9437, 9530,
+                           7940, 1764, 8298, 8134, 3741, 2409,  877, 1958, 5798,
+                           4406, 8279, 5232,  156, 5823, 3467, 4772, 6553, 3983,
+                           1528, 7910, 4804, 3175, 9452, 8577, 1586, 8580, 2673,
+                           1982, 1239, 8183, 2424, 7278, 9218, 1945, 4169, 4883,
+                           8116, 3091, 1525, 9572, 6102, 7072, 6015, 2321, 4696,
+                           4138,  870, 5212, 8282, 6668, 2421, 8337, 2293, 6466,
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+                           4843, 2503, 4723, 1482, 8959, 3694,  866, 8594, 5963,
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+                           6644, 3097, 1394, 6059, 8155, 2846, 1777, 6456, 8176,
+                           7371, 3358, 6829,  157, 1846, 7927, 3646, 7977, 3389,
+                           8996, 5123, 7577,  810, 5309, 6645, 5201, 6517, 9349,
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+                           4541, 2592, 4090,  372, 8070, 8656,  550, 3575, 2398,
+                           6124, 3969, 4102,  973, 6050, 5302, 7479,  298, 1762,
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+                           1353, 2831, 2162, 1680, 7185, 1025, 5353, 3100, 7889,
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+                           8330, 5599, 3505, 5819, 8034, 3497, 5576, 2073, 6030,
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+                      0.1192, 0.9060, 0.2876, 0.0179, 0.2806, 0.5330, 0.0535,
+                      0.8679, 0.6396, 0.9573, 0.3695, 0.7753, 0.1809, 0.4376,
+                      0.2260, 0.3963, 0.6636, 0.7233, 0.8930, 0.6234, 0.6296,
+                      0.0101, 0.1070, 0.4268, 0.6794, 0.1008, 0.6421, 0.1477,
+                      0.9139, 0.6810, 0.1847, 0.6845, 0.0817, 0.7445, 0.4639,
+                      0.9411, 0.7143, 0.1906, 0.2476, 0.7680, 0.7234]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7076, 0.7623, 0.0310,  ..., 0.6712, 0.8172, 0.8298])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.527426958084106 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([5766, 7973, 2780, 3211, 9594, 8202, 4276, 3468, 2763,
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+                           8310]),
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+                      0.9247, 0.8111, 0.0353, 0.8826, 0.1547, 0.6781, 0.1169,
+                      0.4152, 0.9039, 0.6496, 0.1482, 0.7520, 0.7951, 0.4736,
+                      0.0454, 0.0622, 0.6978, 0.2267, 0.9860, 0.6663, 0.3956,
+                      0.2141, 0.3549, 0.0910, 0.2909, 0.5470, 0.7548, 0.0972,
+                      0.4757, 0.3250, 0.5499, 0.2243, 0.5297, 0.8612, 0.2274,
+                      0.0708, 0.5436, 0.9682, 0.7714, 0.8643, 0.1466, 0.6872,
+                      0.5096, 0.0677, 0.4723, 0.2781, 0.2643, 0.7250, 0.5338,
+                      0.9605, 0.4880, 0.8087, 0.3404, 0.9499, 0.2186, 0.5648,
+                      0.1601, 0.4222, 0.4619, 0.9274, 0.9196, 0.5713, 0.0610,
+                      0.2794, 0.8313, 0.2162, 0.2106, 0.1214, 0.6217, 0.5001,
+                      0.6973, 0.2053, 0.6011, 0.1748, 0.9410, 0.1447, 0.2945,
+                      0.6250, 0.9228, 0.9360, 0.5967, 0.4091, 0.1808, 0.3765,
+                      0.1337, 0.5400, 0.9876, 0.6793, 0.7304, 0.3127, 0.5520,
+                      0.6348, 0.1406, 0.6950, 0.0929, 0.0627, 0.4506, 0.3951,
+                      0.3316, 0.5265, 0.5732, 0.8916, 0.5309, 0.1661, 0.8605,
+                      0.4698, 0.9510, 0.7370, 0.1038, 0.2103, 0.5296, 0.6083,
+                      0.3996, 0.3776, 0.7427, 0.9500, 0.4692, 0.4301, 0.2318,
+                      0.6475, 0.6818, 0.7210, 0.4957, 0.3573, 0.1896, 0.8362,
+                      0.0892, 0.9289, 0.0416, 0.7232, 0.0997, 0.1239, 0.9977,
+                      0.4358, 0.5742, 0.1304, 0.2148, 0.2201, 0.4924, 0.9838,
+                      0.4169, 0.4513, 0.6597, 0.5735, 0.7650, 0.1572, 0.3173,
+                      0.0146, 0.3963, 0.3763, 0.1653, 0.1574, 0.5134, 0.0387,
+                      0.9809, 0.8512, 0.4693, 0.5948, 0.2904, 0.1408, 0.4624,
+                      0.8130, 0.5553, 0.5373, 0.2494, 0.8360, 0.7498, 0.9334,
+                      0.9454, 0.3945, 0.9701, 0.6077, 0.5653, 0.6160, 0.2710,
+                      0.6122, 0.6190, 0.1168, 0.2814, 0.5760, 0.8999, 0.6450,
+                      0.0683, 0.5070, 0.2019, 0.8585, 0.1942, 0.2940, 0.5216,
+                      0.3103, 0.6126, 0.6675, 0.8710, 0.7201, 0.0100, 0.0375,
+                      0.0963, 0.7717, 0.1452, 0.0646, 0.5055, 0.9412, 0.1846,
+                      0.6680, 0.1879, 0.4989, 0.0299, 0.1019, 0.2317, 0.9361,
+                      0.3513, 0.7939, 0.6448, 0.0672, 0.3504, 0.7322, 0.1915,
+                      0.9575, 0.1252, 0.0016, 0.2806, 0.1718, 0.3189, 0.9448,
+                      0.8493, 0.4983, 0.7286, 0.9555, 0.2622, 0.8758, 0.8372,
+                      0.5610, 0.8622, 0.9444, 0.1900, 0.1986, 0.2019, 0.1261,
+                      0.5115, 0.8721, 0.0151, 0.3809, 0.2622, 0.9685, 0.2802,
+                      0.4487, 0.0746, 0.0535, 0.8313, 0.2256, 0.3443, 0.6934,
+                      0.8284, 0.7207, 0.7301, 0.8063, 0.2291, 0.1601, 0.6156,
+                      0.5280, 0.1061, 0.8350, 0.8964, 0.7324, 0.3965, 0.6242,
+                      0.2963, 0.1018, 0.1467, 0.4504, 0.5584, 0.6791, 0.9586,
+                      0.0520, 0.7818, 0.3798, 0.9374, 0.8768, 0.2057, 0.9814,
+                      0.1150, 0.7122, 0.3423, 0.3305, 0.0945, 0.8800, 0.5622,
+                      0.1210, 0.3190, 0.5406, 0.1019, 0.4162, 0.4732, 0.2433,
+                      0.6735, 0.8204, 0.0250, 0.4980, 0.9544, 0.9459, 0.0656,
+                      0.6166, 0.6686, 0.0882, 0.4563, 0.0171, 0.2508, 0.3079,
+                      0.1192, 0.9060, 0.2876, 0.0179, 0.2806, 0.5330, 0.0535,
+                      0.8679, 0.6396, 0.9573, 0.3695, 0.7753, 0.1809, 0.4376,
+                      0.2260, 0.3963, 0.6636, 0.7233, 0.8930, 0.6234, 0.6296,
+                      0.0101, 0.1070, 0.4268, 0.6794, 0.1008, 0.6421, 0.1477,
+                      0.9139, 0.6810, 0.1847, 0.6845, 0.0817, 0.7445, 0.4639,
+                      0.9411, 0.7143, 0.1906, 0.2476, 0.7680, 0.7234]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7076, 0.7623, 0.0310,  ..., 0.6712, 0.8172, 0.8298])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.527426958084106 seconds
+
+[40.29, 41.7, 40.43, 39.93, 40.63, 41.16, 39.74, 39.77, 39.65, 39.51]
+[97.79]
+13.381757259368896
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 360318, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.527426958084106, 'TIME_S_1KI': 0.029217044272237598, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1308.6020423936845, 'W': 97.79}
+[40.29, 41.7, 40.43, 39.93, 40.63, 41.16, 39.74, 39.77, 39.65, 39.51, 40.23, 41.08, 39.76, 40.17, 39.91, 40.85, 39.5, 40.14, 40.16, 39.37]
+724.28
+36.214
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 360318, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.527426958084106, 'TIME_S_1KI': 0.029217044272237598, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1308.6020423936845, 'W': 97.79, 'J_1KI': 3.631797585448644, 'W_1KI': 0.2713991529704317, 'W_D': 61.57600000000001, 'J_D': 823.9950850028993, 'W_D_1KI': 0.17089348852957667, 'J_D_1KI': 0.00047428518289282433}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.json
index 6ded063..2bce2c3 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 308023, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.28289532661438, "TIME_S_1KI": 0.03338353086170311, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1226.4741826629638, "W": 98.08, "J_1KI": 3.9817616952726382, "W_1KI": 0.3184177804904179, "W_D": 61.967, "J_D": 774.8870888772011, "W_D_1KI": 0.20117653551845155, "J_D_1KI": 0.0006531217977828004}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 319440, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.952518463134766, "TIME_S_1KI": 0.03428662178542063, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1362.7280988121033, "W": 98.16000000000001, "J_1KI": 4.265990792675004, "W_1KI": 0.3072877535687453, "W_D": 52.57025000000001, "J_D": 729.8182236815096, "W_D_1KI": 0.16457002880040073, "J_D_1KI": 0.0005151829100939166}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.output
index df70699..10ed23c 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.019933462142944336}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.019902944564819336}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    2,    5,  ..., 4999, 5000, 5000]),
-       col_indices=tensor([4080, 6557, 3158,  ..., 4357, 6307, 2550]),
-       values=tensor([0.9910, 0.3414, 0.4855,  ..., 0.2598, 0.6108, 0.2815]),
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 4999, 4999, 5000]),
+       col_indices=tensor([2372, 5760, 6848,  ..., 5311, 7243, 8029]),
+       values=tensor([0.3104, 0.6761, 0.3545,  ..., 0.8200, 0.7445, 0.5520]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.2787, 0.7388, 0.8319,  ..., 0.5413, 0.0496, 0.2437])
+tensor([0.5031, 0.8529, 0.5243,  ..., 0.2679, 0.5423, 0.7335])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 0.019933462142944336 seconds
+Time: 0.019902944564819336 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52675', '-ss', '10000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.7956035137176514}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52756', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.877525806427002}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([ 408,  476, 3837,  ..., 3097, 8388, 8856]),
-       values=tensor([0.3698, 0.9808, 0.6496,  ..., 0.7839, 0.4021, 0.3346]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 4997, 4999, 5000]),
+       col_indices=tensor([6754,  721, 8092,  ..., 4456, 5626, 9090]),
+       values=tensor([0.2896, 0.1011, 0.3253,  ..., 0.8144, 0.5407, 0.9332]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.1775, 0.8809, 0.7204,  ..., 0.4994, 0.6943, 0.3851])
+tensor([0.9789, 0.3880, 0.5470,  ..., 0.8477, 0.3284, 0.8468])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 1.7956035137176514 seconds
+Time: 1.877525806427002 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '308023', '-ss', '10000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.28289532661438}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '295036', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.697813034057617}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    2,  ..., 4999, 5000, 5000]),
-       col_indices=tensor([2483, 6584, 3017,  ...,  870, 3138, 2052]),
-       values=tensor([0.7385, 0.7043, 0.9061,  ..., 0.4377, 0.8515, 0.3180]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 4998, 4999, 5000]),
+       col_indices=tensor([ 740, 7946, 4530,  ..., 5174,  247,  935]),
+       values=tensor([0.0112, 0.8926, 0.5684,  ..., 0.2376, 0.1963, 0.7042]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.1916, 0.9837, 0.2990,  ..., 0.4110, 0.2807, 0.4933])
+tensor([0.7472, 0.6061, 0.9715,  ..., 0.9591, 0.6644, 0.1656])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.28289532661438 seconds
+Time: 9.697813034057617 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '319440', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.952518463134766}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    2,  ..., 4999, 5000, 5000]),
-       col_indices=tensor([2483, 6584, 3017,  ...,  870, 3138, 2052]),
-       values=tensor([0.7385, 0.7043, 0.9061,  ..., 0.4377, 0.8515, 0.3180]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([2136, 3821, 7264,  ..., 9985, 4627, 3401]),
+       values=tensor([0.8150, 0.5813, 0.6488,  ..., 0.6046, 0.0821, 0.1459]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.1916, 0.9837, 0.2990,  ..., 0.4110, 0.2807, 0.4933])
+tensor([0.7848, 0.8694, 0.6164,  ..., 0.4553, 0.9967, 0.4418])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +72,29 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.28289532661438 seconds
+Time: 10.952518463134766 seconds
 
-[40.25, 39.88, 39.44, 44.89, 40.01, 39.55, 39.45, 39.48, 39.91, 41.29]
-[98.08]
-12.504834651947021
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 308023, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.28289532661438, 'TIME_S_1KI': 0.03338353086170311, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1226.4741826629638, 'W': 98.08}
-[40.25, 39.88, 39.44, 44.89, 40.01, 39.55, 39.45, 39.48, 39.91, 41.29, 44.96, 39.78, 39.79, 40.18, 39.36, 39.46, 39.39, 39.37, 39.43, 39.28]
-722.26
-36.113
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 308023, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.28289532661438, 'TIME_S_1KI': 0.03338353086170311, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1226.4741826629638, 'W': 98.08, 'J_1KI': 3.9817616952726382, 'W_1KI': 0.3184177804904179, 'W_D': 61.967, 'J_D': 774.8870888772011, 'W_D_1KI': 0.20117653551845155, 'J_D_1KI': 0.0006531217977828004}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([2136, 3821, 7264,  ..., 9985, 4627, 3401]),
+       values=tensor([0.8150, 0.5813, 0.6488,  ..., 0.6046, 0.0821, 0.1459]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.7848, 0.8694, 0.6164,  ..., 0.4553, 0.9967, 0.4418])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.952518463134766 seconds
+
+[42.53, 39.89, 39.54, 39.38, 39.41, 39.39, 39.72, 39.55, 39.61, 40.35]
+[98.16]
+13.882723093032837
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 319440, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.952518463134766, 'TIME_S_1KI': 0.03428662178542063, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1362.7280988121033, 'W': 98.16000000000001}
+[42.53, 39.89, 39.54, 39.38, 39.41, 39.39, 39.72, 39.55, 39.61, 40.35, 40.76, 45.98, 64.96, 63.35, 66.95, 64.56, 52.89, 67.11, 71.86, 71.65]
+911.7950000000001
+45.58975
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 319440, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.952518463134766, 'TIME_S_1KI': 0.03428662178542063, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1362.7280988121033, 'W': 98.16000000000001, 'J_1KI': 4.265990792675004, 'W_1KI': 0.3072877535687453, 'W_D': 52.57025000000001, 'J_D': 729.8182236815096, 'W_D_1KI': 0.16457002880040073, 'J_D_1KI': 0.0005151829100939166}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json
index f68b6b9..5670ac8 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1275, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.370937824249268, "TIME_S_1KI": 8.134068881764131, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2317.954887828827, "W": 118.94, "J_1KI": 1818.0038335912368, "W_1KI": 93.28627450980392, "W_D": 82.976, "J_D": 1617.072681793213, "W_D_1KI": 65.07921568627451, "J_D_1KI": 51.04252210688197}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1374, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.242573499679565, "TIME_S_1KI": 8.18236790369692, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2407.0519640755656, "W": 121.43, "J_1KI": 1751.8573246547055, "W_1KI": 88.37700145560407, "W_D": 84.9805, "J_D": 1684.5300126255752, "W_D_1KI": 61.84898107714702, "J_D_1KI": 45.01381446662811}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output
index 8fe4739..813cac8 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.8229217529296875}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.8090236186981201}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       48,       92,  ..., 24999906,
-                            24999954, 25000000]),
-       col_indices=tensor([ 13687,  16103,  22085,  ..., 466250, 497468,
-                           498839]),
-       values=tensor([0.1763, 0.0612, 0.1831,  ..., 0.7206, 0.9735, 0.4201]),
+tensor(crow_indices=tensor([       0,       48,       94,  ..., 24999925,
+                            24999965, 25000000]),
+       col_indices=tensor([ 23376,  31397,  31689,  ..., 443361, 445058,
+                           487681]),
+       values=tensor([0.4839, 0.8919, 0.4062,  ..., 0.7044, 0.3151, 0.1559]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.0392, 0.3068, 0.8540,  ..., 0.0771, 0.2433, 0.8939])
+tensor([0.1445, 0.7363, 0.4463,  ..., 0.9472, 0.1025, 0.0893])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 0.8229217529296875 seconds
+Time: 0.8090236186981201 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1275', '-ss', '500000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.370937824249268}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1297', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.907588481903076}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       45,       89,  ..., 24999893,
-                            24999957, 25000000]),
-       col_indices=tensor([ 25264,  35882,  38786,  ..., 487781, 491680,
-                           492236]),
-       values=tensor([0.0901, 0.4292, 0.0295,  ..., 0.7641, 0.5758, 0.3435]),
+tensor(crow_indices=tensor([       0,       35,       72,  ..., 24999893,
+                            24999944, 25000000]),
+       col_indices=tensor([ 16655,  42218,  84405,  ..., 471063, 480187,
+                           495612]),
+       values=tensor([0.8923, 0.1977, 0.5949,  ..., 0.6111, 0.0213, 0.1253]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.7878, 0.6485, 0.9023,  ..., 0.5055, 0.2764, 0.4227])
+tensor([0.7619, 0.7901, 0.5066,  ..., 0.1051, 0.1233, 0.1900])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +38,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 10.370937824249268 seconds
+Time: 9.907588481903076 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1374', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.242573499679565}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       45,       89,  ..., 24999893,
-                            24999957, 25000000]),
-       col_indices=tensor([ 25264,  35882,  38786,  ..., 487781, 491680,
-                           492236]),
-       values=tensor([0.0901, 0.4292, 0.0295,  ..., 0.7641, 0.5758, 0.3435]),
+tensor(crow_indices=tensor([       0,       39,       98,  ..., 24999895,
+                            24999956, 25000000]),
+       col_indices=tensor([ 49186,  57840,  60335,  ..., 478855, 479456,
+                           490342]),
+       values=tensor([0.8208, 0.1257, 0.0473,  ..., 0.1778, 0.0330, 0.2399]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.7878, 0.6485, 0.9023,  ..., 0.5055, 0.2764, 0.4227])
+tensor([0.2858, 0.5405, 0.2026,  ..., 0.3109, 0.2614, 0.2825])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +59,31 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 10.370937824249268 seconds
+Time: 11.242573499679565 seconds
 
-[40.75, 40.02, 39.59, 39.62, 39.74, 39.84, 39.97, 39.74, 40.73, 39.5]
-[118.94]
-19.488438606262207
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1275, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.370937824249268, 'TIME_S_1KI': 8.134068881764131, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2317.954887828827, 'W': 118.94}
-[40.75, 40.02, 39.59, 39.62, 39.74, 39.84, 39.97, 39.74, 40.73, 39.5, 41.45, 40.23, 40.12, 39.57, 39.77, 39.94, 39.75, 39.69, 40.29, 39.64]
-719.28
-35.964
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1275, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.370937824249268, 'TIME_S_1KI': 8.134068881764131, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2317.954887828827, 'W': 118.94, 'J_1KI': 1818.0038335912368, 'W_1KI': 93.28627450980392, 'W_D': 82.976, 'J_D': 1617.072681793213, 'W_D_1KI': 65.07921568627451, 'J_D_1KI': 51.04252210688197}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       39,       98,  ..., 24999895,
+                            24999956, 25000000]),
+       col_indices=tensor([ 49186,  57840,  60335,  ..., 478855, 479456,
+                           490342]),
+       values=tensor([0.8208, 0.1257, 0.0473,  ..., 0.1778, 0.0330, 0.2399]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.2858, 0.5405, 0.2026,  ..., 0.3109, 0.2614, 0.2825])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 11.242573499679565 seconds
+
+[41.4, 40.98, 40.32, 40.18, 40.32, 39.77, 39.88, 39.58, 40.12, 39.72]
+[121.43]
+19.822547674179077
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1374, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.242573499679565, 'TIME_S_1KI': 8.18236790369692, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2407.0519640755656, 'W': 121.43}
+[41.4, 40.98, 40.32, 40.18, 40.32, 39.77, 39.88, 39.58, 40.12, 39.72, 41.41, 40.06, 39.79, 41.99, 39.71, 39.74, 45.22, 40.21, 39.84, 40.03]
+728.99
+36.4495
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1374, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.242573499679565, 'TIME_S_1KI': 8.18236790369692, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2407.0519640755656, 'W': 121.43, 'J_1KI': 1751.8573246547055, 'W_1KI': 88.37700145560407, 'W_D': 84.9805, 'J_D': 1684.5300126255752, 'W_D_1KI': 61.84898107714702, 'J_D_1KI': 45.01381446662811}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.json
index 24823a1..6b52d9e 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 20602, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.051029920578003, "TIME_S_1KI": 0.4878667081146492, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2012.3513662075998, "W": 152.11, "J_1KI": 97.67747627451702, "W_1KI": 7.383263760799923, "W_D": 115.75800000000001, "J_D": 1531.429685421467, "W_D_1KI": 5.61877487622561, "J_D_1KI": 0.272729583352374}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 21617, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.82520842552185, "TIME_S_1KI": 0.5007729298941505, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2091.1719660377503, "W": 152.99, "J_1KI": 96.73738104444419, "W_1KI": 7.07730027293334, "W_D": 117.02850000000001, "J_D": 1599.6255861654283, "W_D_1KI": 5.413725308784754, "J_D_1KI": 0.25043832672363203}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.output
index 60d167b..6c81aa1 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08110809326171875}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.07646036148071289}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       6,       8,  ..., 2499988,
-                            2499994, 2500000]),
-       col_indices=tensor([ 61750, 191731, 192878,  ..., 292292, 347392,
-                           413452]),
-       values=tensor([0.4333, 0.7749, 0.6975,  ..., 0.5571, 0.2303, 0.6423]),
+tensor(crow_indices=tensor([      0,       7,       9,  ..., 2499989,
+                            2499996, 2500000]),
+       col_indices=tensor([102432, 128529, 159807,  ..., 283424, 312882,
+                           427124]),
+       values=tensor([0.3590, 0.8739, 0.1654,  ..., 0.5356, 0.8734, 0.7698]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7573, 0.7811, 0.2609,  ..., 0.7028, 0.0683, 0.1077])
+tensor([0.5883, 0.7167, 0.7101,  ..., 0.2003, 0.5848, 0.6856])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,41 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 0.08110809326171875 seconds
+Time: 0.07646036148071289 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12945', '-ss', '500000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.597289562225342}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '13732', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.670015335083008}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       4,       9,  ..., 2499996,
+tensor(crow_indices=tensor([      0,       4,      16,  ..., 2499988,
+                            2499996, 2500000]),
+       col_indices=tensor([ 59164,  71164, 341125,  ...,  45240, 225921,
+                           431340]),
+       values=tensor([0.0543, 0.5224, 0.7852,  ..., 0.1414, 0.5048, 0.3968]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.2230, 0.1381, 0.5919,  ..., 0.8762, 0.3274, 0.6312])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 6.670015335083008 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21617', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.82520842552185}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,      11,  ..., 2499992,
                             2499997, 2500000]),
-       col_indices=tensor([304373, 374974, 396567,  ..., 161828, 243938,
-                           306700]),
-       values=tensor([0.0234, 0.0111, 0.7752,  ..., 0.4123, 0.0911, 0.7333]),
+       col_indices=tensor([  6844,  10153, 255216,  ...,  42121,  91491,
+                           461513]),
+       values=tensor([0.9924, 0.6578, 0.5329,  ..., 0.0031, 0.7725, 0.4194]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.2563, 0.2400, 0.1997,  ..., 0.9331, 0.1838, 0.9541])
+tensor([0.1428, 0.3987, 0.7169,  ..., 0.0273, 0.0070, 0.3921])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,20 +59,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 6.597289562225342 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '20602', '-ss', '500000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.051029920578003}
+Time: 10.82520842552185 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       4,       7,  ..., 2499995,
-                            2499998, 2500000]),
-       col_indices=tensor([ 84683, 221772, 250792,  ..., 457280, 123381,
-                           490345]),
-       values=tensor([0.6671, 0.6498, 0.8275,  ..., 0.5282, 0.6912, 0.3058]),
+tensor(crow_indices=tensor([      0,       5,      11,  ..., 2499992,
+                            2499997, 2500000]),
+       col_indices=tensor([  6844,  10153, 255216,  ...,  42121,  91491,
+                           461513]),
+       values=tensor([0.9924, 0.6578, 0.5329,  ..., 0.0031, 0.7725, 0.4194]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8099, 0.6830, 0.6662,  ..., 0.4435, 0.6731, 0.4595])
+tensor([0.1428, 0.3987, 0.7169,  ..., 0.0273, 0.0070, 0.3921])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -59,31 +77,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.051029920578003 seconds
+Time: 10.82520842552185 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       4,       7,  ..., 2499995,
-                            2499998, 2500000]),
-       col_indices=tensor([ 84683, 221772, 250792,  ..., 457280, 123381,
-                           490345]),
-       values=tensor([0.6671, 0.6498, 0.8275,  ..., 0.5282, 0.6912, 0.3058]),
-       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8099, 0.6830, 0.6662,  ..., 0.4435, 0.6731, 0.4595])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 2500000
-Density: 1e-05
-Time: 10.051029920578003 seconds
-
-[40.55, 45.24, 39.91, 39.85, 39.84, 39.77, 40.43, 40.43, 42.0, 39.73]
-[152.11]
-13.22957968711853
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 20602, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.051029920578003, 'TIME_S_1KI': 0.4878667081146492, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2012.3513662075998, 'W': 152.11}
-[40.55, 45.24, 39.91, 39.85, 39.84, 39.77, 40.43, 40.43, 42.0, 39.73, 40.67, 40.21, 39.79, 39.83, 39.69, 40.04, 39.86, 39.69, 40.08, 39.81]
-727.04
-36.352
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 20602, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.051029920578003, 'TIME_S_1KI': 0.4878667081146492, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2012.3513662075998, 'W': 152.11, 'J_1KI': 97.67747627451702, 'W_1KI': 7.383263760799923, 'W_D': 115.75800000000001, 'J_D': 1531.429685421467, 'W_D_1KI': 5.61877487622561, 'J_D_1KI': 0.272729583352374}
+[40.4, 40.36, 39.63, 39.65, 40.33, 39.57, 40.07, 39.61, 39.74, 39.54]
+[152.99]
+13.668684005737305
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21617, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.82520842552185, 'TIME_S_1KI': 0.5007729298941505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2091.1719660377503, 'W': 152.99}
+[40.4, 40.36, 39.63, 39.65, 40.33, 39.57, 40.07, 39.61, 39.74, 39.54, 40.7, 39.74, 40.09, 39.86, 39.97, 40.05, 40.88, 39.9, 39.65, 39.62]
+719.23
+35.9615
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21617, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.82520842552185, 'TIME_S_1KI': 0.5007729298941505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2091.1719660377503, 'W': 152.99, 'J_1KI': 96.73738104444419, 'W_1KI': 7.07730027293334, 'W_D': 117.02850000000001, 'J_D': 1599.6255861654283, 'W_D_1KI': 5.413725308784754, 'J_D_1KI': 0.25043832672363203}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.json
index ac74c89..2eb0948 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2268, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.338330507278442, "TIME_S_1KI": 4.558346784514304, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1929.4526886749268, "W": 124.72, "J_1KI": 850.7286987102852, "W_1KI": 54.99118165784832, "W_D": 88.6155, "J_D": 1370.9061476368904, "W_D_1KI": 39.07208994708994, "J_D_1KI": 17.227552886723963}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2135, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.106867790222168, "TIME_S_1KI": 4.733895920478767, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1866.3490508079528, "W": 123.28, "J_1KI": 874.16817368054, "W_1KI": 57.742388758782205, "W_D": 87.33449999999999, "J_D": 1322.1662976783514, "W_D_1KI": 40.90608899297424, "J_D_1KI": 19.159760652446952}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.output
index 4f74cbe..5e3d223 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.output
@@ -1,36 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.46280455589294434}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.491588830947876}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       21,       39,  ..., 12499960,
-                            12499981, 12500000]),
-       col_indices=tensor([  5530,  18658,  36900,  ..., 388989, 426254,
-                           497258]),
-       values=tensor([0.8053, 0.3880, 0.4779,  ..., 0.4773, 0.4279, 0.6817]),
-       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.5886, 0.4606, 0.7255,  ..., 0.1606, 0.2608, 0.5232])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 12500000
-Density: 5e-05
-Time: 0.46280455589294434 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2268', '-ss', '500000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.338330507278442}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       30,       63,  ..., 12499957,
+tensor(crow_indices=tensor([       0,       31,       56,  ..., 12499950,
                             12499979, 12500000]),
-       col_indices=tensor([ 14790,  16334,  55074,  ..., 466420, 486794,
-                           499923]),
-       values=tensor([0.8543, 0.1686, 0.8292,  ..., 0.6567, 0.2357, 0.6950]),
+       col_indices=tensor([ 20037,  42363,  59004,  ..., 453758, 466137,
+                           477307]),
+       values=tensor([0.6776, 0.0862, 0.2736,  ..., 0.4363, 0.2767, 0.3172]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.8639, 0.3423, 0.4800,  ..., 0.1443, 0.7816, 0.0060])
+tensor([0.5582, 0.5777, 0.9845,  ..., 0.7624, 0.5547, 0.6204])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 10.338330507278442 seconds
+Time: 0.491588830947876 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2135', '-ss', '500000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.106867790222168}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       30,       63,  ..., 12499957,
-                            12499979, 12500000]),
-       col_indices=tensor([ 14790,  16334,  55074,  ..., 466420, 486794,
-                           499923]),
-       values=tensor([0.8543, 0.1686, 0.8292,  ..., 0.6567, 0.2357, 0.6950]),
+tensor(crow_indices=tensor([       0,       24,       49,  ..., 12499947,
+                            12499975, 12500000]),
+       col_indices=tensor([  4589,  81496, 110665,  ..., 429262, 449020,
+                           460708]),
+       values=tensor([0.3929, 0.7633, 0.9504,  ..., 0.5778, 0.1433, 0.6727]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.8639, 0.3423, 0.4800,  ..., 0.1443, 0.7816, 0.0060])
+tensor([0.3227, 0.6104, 0.4525,  ..., 0.1890, 0.8017, 0.8134])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +38,31 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 10.338330507278442 seconds
+Time: 10.106867790222168 seconds
 
-[40.4, 42.56, 40.55, 40.15, 39.72, 40.38, 39.75, 39.66, 39.85, 39.57]
-[124.72]
-15.470274925231934
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2268, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.338330507278442, 'TIME_S_1KI': 4.558346784514304, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1929.4526886749268, 'W': 124.72}
-[40.4, 42.56, 40.55, 40.15, 39.72, 40.38, 39.75, 39.66, 39.85, 39.57, 40.6, 40.35, 40.27, 40.13, 39.75, 39.68, 39.73, 39.6, 39.89, 39.57]
-722.09
-36.1045
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2268, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.338330507278442, 'TIME_S_1KI': 4.558346784514304, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1929.4526886749268, 'W': 124.72, 'J_1KI': 850.7286987102852, 'W_1KI': 54.99118165784832, 'W_D': 88.6155, 'J_D': 1370.9061476368904, 'W_D_1KI': 39.07208994708994, 'J_D_1KI': 17.227552886723963}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       24,       49,  ..., 12499947,
+                            12499975, 12500000]),
+       col_indices=tensor([  4589,  81496, 110665,  ..., 429262, 449020,
+                           460708]),
+       values=tensor([0.3929, 0.7633, 0.9504,  ..., 0.5778, 0.1433, 0.6727]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.3227, 0.6104, 0.4525,  ..., 0.1890, 0.8017, 0.8134])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.106867790222168 seconds
+
+[41.05, 39.9, 39.99, 39.71, 39.62, 39.5, 41.67, 39.56, 39.56, 39.91]
+[123.28]
+15.139106512069702
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2135, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.106867790222168, 'TIME_S_1KI': 4.733895920478767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1866.3490508079528, 'W': 123.28}
+[41.05, 39.9, 39.99, 39.71, 39.62, 39.5, 41.67, 39.56, 39.56, 39.91, 40.93, 40.32, 39.68, 39.56, 39.71, 39.68, 39.87, 39.55, 40.03, 40.11]
+718.9100000000001
+35.9455
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2135, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.106867790222168, 'TIME_S_1KI': 4.733895920478767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1866.3490508079528, 'W': 123.28, 'J_1KI': 874.16817368054, 'W_1KI': 57.742388758782205, 'W_D': 87.33449999999999, 'J_D': 1322.1662976783514, 'W_D_1KI': 40.90608899297424, 'J_D_1KI': 19.159760652446952}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.json
index 366bd80..b4235dc 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 89538, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.289806604385376, "TIME_S_1KI": 0.11492111287258344, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1521.4007213592529, "W": 116.96, "J_1KI": 16.991676398392332, "W_1KI": 1.3062610288369183, "W_D": 80.71074999999999, "J_D": 1049.8751134699583, "W_D_1KI": 0.9014133663919228, "J_D_1KI": 0.010067383305322017}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 88363, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.692678213119507, "TIME_S_1KI": 0.1210085467120798, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1541.5747392845155, "W": 115.79, "J_1KI": 17.445930302100603, "W_1KI": 1.3103900954019216, "W_D": 79.897, "J_D": 1063.7118658313752, "W_D_1KI": 0.9041906680397905, "J_D_1KI": 0.010232684132949204}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.output
index aedb6df..f998b37 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.049301862716674805}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.04586005210876465}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,     13,  ..., 249993, 249996,
+tensor(crow_indices=tensor([     0,      1,      4,  ..., 249988, 249993,
                             250000]),
-       col_indices=tensor([ 1709, 19790, 28830,  ...,  3831, 22257, 48856]),
-       values=tensor([0.9244, 0.7522, 0.6687,  ..., 0.7540, 0.7318, 0.7260]),
+       col_indices=tensor([48756, 27344, 41514,  ..., 26596, 29730, 34292]),
+       values=tensor([0.9696, 0.8272, 0.9747,  ..., 0.0537, 0.5377, 0.5408]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0785, 0.8938, 0.5541,  ..., 0.5935, 0.2052, 0.2232])
+tensor([0.4023, 0.3988, 0.8031,  ..., 0.0466, 0.9788, 0.6602])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 0.049301862716674805 seconds
+Time: 0.04586005210876465 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21297', '-ss', '50000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.7824935913085938}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '22895', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.7205493450164795}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      8,  ..., 249989, 249993,
+tensor(crow_indices=tensor([     0,      4,      6,  ..., 249991, 249994,
                             250000]),
-       col_indices=tensor([16415, 16632, 32449,  ..., 45169, 45288, 48610]),
-       values=tensor([0.0101, 0.6954, 0.6241,  ..., 0.3711, 0.7246, 0.3748]),
+       col_indices=tensor([ 1690,  7661, 14833,  ..., 26670, 28443, 40583]),
+       values=tensor([0.3670, 0.4597, 0.1032,  ..., 0.6580, 0.1615, 0.4832]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.6515, 0.7514, 0.0204,  ..., 0.8861, 0.6124, 0.4798])
+tensor([0.0576, 0.8105, 0.5861,  ..., 0.6869, 0.4943, 0.9931])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 2.7824935913085938 seconds
+Time: 2.7205493450164795 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '80366', '-ss', '50000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.42440915107727}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '88363', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.692678213119507}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,     10,  ..., 249987, 249993,
+tensor(crow_indices=tensor([     0,      6,     12,  ..., 249987, 249994,
                             250000]),
-       col_indices=tensor([ 2445, 24855, 26173,  ..., 23560, 26333, 46130]),
-       values=tensor([0.2012, 0.2713, 0.8391,  ..., 0.5844, 0.7972, 0.4463]),
+       col_indices=tensor([ 2153, 14633, 16101,  ..., 25645, 38673, 48362]),
+       values=tensor([0.6843, 0.5058, 0.7514,  ..., 0.4931, 0.4973, 0.4238]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5580, 0.1767, 0.6905,  ..., 0.9860, 0.6709, 0.2165])
+tensor([0.1661, 0.3950, 0.7634,  ..., 0.8727, 0.6472, 0.3253])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,19 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 9.42440915107727 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '89538', '-ss', '50000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.289806604385376}
+Time: 10.692678213119507 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      7,  ..., 249988, 249996,
+tensor(crow_indices=tensor([     0,      6,     12,  ..., 249987, 249994,
                             250000]),
-       col_indices=tensor([ 2244, 34732,  7243,  ...,  9132, 13610, 19520]),
-       values=tensor([0.6983, 0.0446, 0.9216,  ..., 0.0232, 0.0374, 0.6300]),
+       col_indices=tensor([ 2153, 14633, 16101,  ..., 25645, 38673, 48362]),
+       values=tensor([0.6843, 0.5058, 0.7514,  ..., 0.4931, 0.4973, 0.4238]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8539, 0.6321, 0.4259,  ..., 0.2899, 0.6274, 0.3350])
+tensor([0.1661, 0.3950, 0.7634,  ..., 0.8727, 0.6472, 0.3253])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -76,30 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.289806604385376 seconds
+Time: 10.692678213119507 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      7,  ..., 249988, 249996,
-                            250000]),
-       col_indices=tensor([ 2244, 34732,  7243,  ...,  9132, 13610, 19520]),
-       values=tensor([0.6983, 0.0446, 0.9216,  ..., 0.0232, 0.0374, 0.6300]),
-       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8539, 0.6321, 0.4259,  ..., 0.2899, 0.6274, 0.3350])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 250000
-Density: 0.0001
-Time: 10.289806604385376 seconds
-
-[40.27, 39.54, 39.5, 39.65, 39.93, 39.44, 40.01, 39.51, 45.05, 39.37]
-[116.96]
-13.007872104644775
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 89538, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.289806604385376, 'TIME_S_1KI': 0.11492111287258344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1521.4007213592529, 'W': 116.96}
-[40.27, 39.54, 39.5, 39.65, 39.93, 39.44, 40.01, 39.51, 45.05, 39.37, 40.45, 39.41, 39.86, 39.36, 39.91, 44.54, 39.53, 39.97, 39.84, 39.78]
-724.985
-36.24925
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 89538, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.289806604385376, 'TIME_S_1KI': 0.11492111287258344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1521.4007213592529, 'W': 116.96, 'J_1KI': 16.991676398392332, 'W_1KI': 1.3062610288369183, 'W_D': 80.71074999999999, 'J_D': 1049.8751134699583, 'W_D_1KI': 0.9014133663919228, 'J_D_1KI': 0.010067383305322017}
+[40.57, 39.5, 40.22, 39.42, 39.56, 39.38, 39.65, 39.51, 39.53, 40.4]
+[115.79]
+13.313539505004883
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 88363, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.692678213119507, 'TIME_S_1KI': 0.1210085467120798, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1541.5747392845155, 'W': 115.79}
+[40.57, 39.5, 40.22, 39.42, 39.56, 39.38, 39.65, 39.51, 39.53, 40.4, 41.29, 39.45, 40.25, 39.78, 42.21, 39.36, 40.3, 39.38, 39.52, 39.42]
+717.86
+35.893
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 88363, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.692678213119507, 'TIME_S_1KI': 0.1210085467120798, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1541.5747392845155, 'W': 115.79, 'J_1KI': 17.445930302100603, 'W_1KI': 1.3103900954019216, 'W_D': 79.897, 'J_D': 1063.7118658313752, 'W_D_1KI': 0.9041906680397905, 'J_D_1KI': 0.010232684132949204}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.json
index 16ebf8c..d36f4c6 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 45908, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.364075660705566, "TIME_S_1KI": 0.22575750763931268, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1982.6904290771486, "W": 146.58, "J_1KI": 43.18834253457238, "W_1KI": 3.192907554238913, "W_D": 110.64150000000001, "J_D": 1496.5741786651613, "W_D_1KI": 2.410070140280561, "J_D_1KI": 0.05249782478610615}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 45817, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.300234079360962, "TIME_S_1KI": 0.22481249491151672, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1949.6090237426756, "W": 147.88, "J_1KI": 42.55208817126123, "W_1KI": 3.2276229347185543, "W_D": 111.8985, "J_D": 1475.238878437042, "W_D_1KI": 2.442292162297837, "J_D_1KI": 0.05330537054582005}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.output
index f9b44d5..3af936c 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.0605926513671875}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.05995535850524902}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      42,      99,  ..., 2499902,
-                            2499955, 2500000]),
-       col_indices=tensor([ 1009,  1628,  5292,  ..., 43455, 47256, 47946]),
-       values=tensor([0.2339, 0.7843, 0.8407,  ..., 0.0388, 0.2390, 0.6904]),
+tensor(crow_indices=tensor([      0,      46,     101,  ..., 2499911,
+                            2499946, 2500000]),
+       col_indices=tensor([  837,  3436,  3784,  ..., 49193, 49198, 49836]),
+       values=tensor([0.9403, 0.3671, 0.7748,  ..., 0.9889, 0.1996, 0.6173]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3494, 0.3893, 0.8826,  ..., 0.0693, 0.0070, 0.7582])
+tensor([0.0910, 0.9171, 0.2074,  ..., 0.6372, 0.3068, 0.1250])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,39 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 0.0605926513671875 seconds
+Time: 0.05995535850524902 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '17328', '-ss', '50000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.963207721710205}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '17513', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 4.013477563858032}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      45,      85,  ..., 2499905,
+tensor(crow_indices=tensor([      0,      46,      92,  ..., 2499887,
+                            2499939, 2500000]),
+       col_indices=tensor([  304,   816,  1770,  ..., 46946, 47011, 48726]),
+       values=tensor([0.3138, 0.4297, 0.4205,  ..., 0.5723, 0.1353, 0.1550]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3742, 0.5301, 0.3458,  ..., 0.7724, 0.2652, 0.5224])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 4.013477563858032 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '45817', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.300234079360962}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      50,      95,  ..., 2499900,
                             2499952, 2500000]),
-       col_indices=tensor([ 2138,  2192,  2629,  ..., 48532, 49646, 49876]),
-       values=tensor([0.7824, 0.0061, 0.7967,  ..., 0.1635, 0.4732, 0.5157]),
+       col_indices=tensor([  291,   577,   802,  ..., 47318, 48670, 49733]),
+       values=tensor([0.1845, 0.8430, 0.5042,  ..., 0.4194, 0.3359, 0.4011]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8165, 0.7580, 0.0903,  ..., 0.6290, 0.7559, 0.6116])
+tensor([0.6378, 0.1370, 0.7965,  ..., 0.0471, 0.0852, 0.0122])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 3.963207721710205 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '45908', '-ss', '50000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.364075660705566}
+Time: 10.300234079360962 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      48,      99,  ..., 2499901,
-                            2499955, 2500000]),
-       col_indices=tensor([ 2242,  2630,  4307,  ..., 47333, 48170, 49131]),
-       values=tensor([0.3970, 0.2919, 0.1690,  ..., 0.5693, 0.6652, 0.4283]),
+tensor(crow_indices=tensor([      0,      50,      95,  ..., 2499900,
+                            2499952, 2500000]),
+       col_indices=tensor([  291,   577,   802,  ..., 47318, 48670, 49733]),
+       values=tensor([0.1845, 0.8430, 0.5042,  ..., 0.4194, 0.3359, 0.4011]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7545, 0.7866, 0.4331,  ..., 0.1722, 0.5406, 0.9467])
+tensor([0.6378, 0.1370, 0.7965,  ..., 0.0471, 0.0852, 0.0122])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,30 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.364075660705566 seconds
+Time: 10.300234079360962 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      48,      99,  ..., 2499901,
-                            2499955, 2500000]),
-       col_indices=tensor([ 2242,  2630,  4307,  ..., 47333, 48170, 49131]),
-       values=tensor([0.3970, 0.2919, 0.1690,  ..., 0.5693, 0.6652, 0.4283]),
-       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7545, 0.7866, 0.4331,  ..., 0.1722, 0.5406, 0.9467])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 2500000
-Density: 0.001
-Time: 10.364075660705566 seconds
-
-[40.59, 39.91, 39.52, 39.54, 39.7, 39.41, 39.47, 40.95, 40.05, 39.39]
-[146.58]
-13.526336669921875
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 45908, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.364075660705566, 'TIME_S_1KI': 0.22575750763931268, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1982.6904290771486, 'W': 146.58}
-[40.59, 39.91, 39.52, 39.54, 39.7, 39.41, 39.47, 40.95, 40.05, 39.39, 40.78, 39.79, 39.69, 41.93, 40.24, 39.57, 39.58, 39.7, 39.52, 39.64]
-718.77
-35.9385
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 45908, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.364075660705566, 'TIME_S_1KI': 0.22575750763931268, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1982.6904290771486, 'W': 146.58, 'J_1KI': 43.18834253457238, 'W_1KI': 3.192907554238913, 'W_D': 110.64150000000001, 'J_D': 1496.5741786651613, 'W_D_1KI': 2.410070140280561, 'J_D_1KI': 0.05249782478610615}
+[46.28, 39.85, 39.61, 39.84, 39.94, 40.08, 39.93, 39.98, 39.68, 39.43]
+[147.88]
+13.183723449707031
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 45817, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.300234079360962, 'TIME_S_1KI': 0.22481249491151672, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1949.6090237426756, 'W': 147.88}
+[46.28, 39.85, 39.61, 39.84, 39.94, 40.08, 39.93, 39.98, 39.68, 39.43, 40.17, 39.51, 40.01, 40.53, 39.54, 39.43, 39.57, 39.81, 39.43, 39.9]
+719.63
+35.9815
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 45817, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.300234079360962, 'TIME_S_1KI': 0.22481249491151672, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1949.6090237426756, 'W': 147.88, 'J_1KI': 42.55208817126123, 'W_1KI': 3.2276229347185543, 'W_D': 111.8985, 'J_D': 1475.238878437042, 'W_D_1KI': 2.442292162297837, 'J_D_1KI': 0.05330537054582005}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json
index 20ca6ca..a7370ad 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1726, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.692668676376343, "TIME_S_1KI": 6.1950571705540805, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2256.2120828294755, "W": 117.97000000000001, "J_1KI": 1307.1912415002755, "W_1KI": 68.34878331402086, "W_D": 81.57950000000002, "J_D": 1560.2327168872362, "W_D_1KI": 47.26506373117035, "J_D_1KI": 27.384162069044237}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1804, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.344184398651123, "TIME_S_1KI": 6.28835055357601, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2300.324343466759, "W": 118.35000000000001, "J_1KI": 1275.1243589061855, "W_1KI": 65.60421286031043, "W_D": 82.3405, "J_D": 1600.421264074564, "W_D_1KI": 45.643292682926834, "J_D_1KI": 25.30116002379536}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output
index 939a7fb..1c44d60 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.6629180908203125}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.6471617221832275}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      514,      996,  ..., 24998956,
-                            24999472, 25000000]),
-       col_indices=tensor([    1,    94,   348,  ..., 49850, 49922, 49959]),
-       values=tensor([0.7408, 0.6252, 0.3689,  ..., 0.4667, 0.9642, 0.0582]),
+tensor(crow_indices=tensor([       0,      501,      980,  ..., 24998975,
+                            24999493, 25000000]),
+       col_indices=tensor([   65,    80,   124,  ..., 49855, 49857, 49996]),
+       values=tensor([0.2347, 0.8281, 0.9357,  ..., 0.1898, 0.4548, 0.5109]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.9247, 0.2733, 0.8266,  ..., 0.5422, 0.1520, 0.6812])
+tensor([0.1705, 0.9477, 0.0426,  ..., 0.3530, 0.9921, 0.0403])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 0.6629180908203125 seconds
+Time: 0.6471617221832275 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1583', '-ss', '50000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.627561807632446}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1622', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.437881469726562}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      562,     1043,  ..., 24999032,
-                            24999504, 25000000]),
-       col_indices=tensor([    4,    50,    78,  ..., 49916, 49920, 49965]),
-       values=tensor([0.0759, 0.2514, 0.9400,  ..., 0.2240, 0.9432, 0.3438]),
+tensor(crow_indices=tensor([       0,      500,     1005,  ..., 24999021,
+                            24999495, 25000000]),
+       col_indices=tensor([   20,   485,   487,  ..., 49816, 49880, 49908]),
+       values=tensor([0.8491, 0.8120, 0.9833,  ..., 0.7969, 0.8199, 0.5818]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.3732, 0.6785, 0.5695,  ..., 0.6003, 0.8169, 0.4003])
+tensor([0.4544, 0.6573, 0.4768,  ..., 0.0080, 0.3189, 0.0636])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 9.627561807632446 seconds
+Time: 9.437881469726562 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1726', '-ss', '50000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.692668676376343}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1804', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.344184398651123}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      457,      970,  ..., 24999002,
-                            24999486, 25000000]),
-       col_indices=tensor([  100,   360,   480,  ..., 49859, 49889, 49953]),
-       values=tensor([0.3856, 0.6378, 0.2660,  ..., 0.6784, 0.6537, 0.7029]),
+tensor(crow_indices=tensor([       0,      496,      963,  ..., 24998993,
+                            24999488, 25000000]),
+       col_indices=tensor([  360,   922,   929,  ..., 49746, 49786, 49807]),
+       values=tensor([0.4579, 0.0160, 0.6008,  ..., 0.8509, 0.1795, 0.5359]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.4800, 0.0280, 0.3242,  ..., 0.3544, 0.6298, 0.7207])
+tensor([0.1076, 0.4012, 0.5788,  ..., 0.7338, 0.7512, 0.3105])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 10.692668676376343 seconds
+Time: 11.344184398651123 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      457,      970,  ..., 24999002,
-                            24999486, 25000000]),
-       col_indices=tensor([  100,   360,   480,  ..., 49859, 49889, 49953]),
-       values=tensor([0.3856, 0.6378, 0.2660,  ..., 0.6784, 0.6537, 0.7029]),
+tensor(crow_indices=tensor([       0,      496,      963,  ..., 24998993,
+                            24999488, 25000000]),
+       col_indices=tensor([  360,   922,   929,  ..., 49746, 49786, 49807]),
+       values=tensor([0.4579, 0.0160, 0.6008,  ..., 0.8509, 0.1795, 0.5359]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.4800, 0.0280, 0.3242,  ..., 0.3544, 0.6298, 0.7207])
+tensor([0.1076, 0.4012, 0.5788,  ..., 0.7338, 0.7512, 0.3105])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 10.692668676376343 seconds
+Time: 11.344184398651123 seconds
 
-[41.47, 39.7, 39.74, 40.1, 40.28, 40.05, 40.37, 39.58, 39.73, 40.01]
-[117.97]
-19.125303745269775
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1726, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.692668676376343, 'TIME_S_1KI': 6.1950571705540805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2256.2120828294755, 'W': 117.97000000000001}
-[41.47, 39.7, 39.74, 40.1, 40.28, 40.05, 40.37, 39.58, 39.73, 40.01, 41.18, 40.13, 45.73, 39.77, 39.88, 40.17, 39.76, 40.02, 41.27, 40.4]
-727.81
-36.390499999999996
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1726, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.692668676376343, 'TIME_S_1KI': 6.1950571705540805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2256.2120828294755, 'W': 117.97000000000001, 'J_1KI': 1307.1912415002755, 'W_1KI': 68.34878331402086, 'W_D': 81.57950000000002, 'J_D': 1560.2327168872362, 'W_D_1KI': 47.26506373117035, 'J_D_1KI': 27.384162069044237}
+[40.38, 40.24, 40.1, 39.7, 39.9, 41.15, 39.98, 39.73, 39.7, 39.9]
+[118.35]
+19.436623096466064
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1804, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.344184398651123, 'TIME_S_1KI': 6.28835055357601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2300.324343466759, 'W': 118.35000000000001}
+[40.38, 40.24, 40.1, 39.7, 39.9, 41.15, 39.98, 39.73, 39.7, 39.9, 40.49, 39.72, 40.3, 40.03, 39.74, 39.88, 39.79, 39.71, 40.16, 39.95]
+720.19
+36.0095
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1804, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.344184398651123, 'TIME_S_1KI': 6.28835055357601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2300.324343466759, 'W': 118.35000000000001, 'J_1KI': 1275.1243589061855, 'W_1KI': 65.60421286031043, 'W_D': 82.3405, 'J_D': 1600.421264074564, 'W_D_1KI': 45.643292682926834, 'J_D_1KI': 25.30116002379536}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.05.json
new file mode 100644
index 0000000..dcc7159
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 355, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.030322074890137, "TIME_S_1KI": 28.254428379972214, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3807.8137480163573, "W": 95.36, "J_1KI": 10726.235909905232, "W_1KI": 268.61971830985914, "W_D": 58.324250000000006, "J_D": 2328.941705041349, "W_D_1KI": 164.293661971831, "J_D_1KI": 462.79904780797466}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.05.output
new file mode 100644
index 0000000..2499979
--- /dev/null
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.9571590423583984}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2454,      4967,  ...,
+                            124995021, 124997489, 125000000]),
+       col_indices=tensor([   26,    74,    80,  ..., 49981, 49991, 49992]),
+       values=tensor([0.1549, 0.4800, 0.7967,  ..., 0.3045, 0.9216, 0.3542]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.9869, 0.0702, 0.3082,  ..., 0.7358, 0.2978, 0.9233])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 2.9571590423583984 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '355', '-ss', '50000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.030322074890137}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2458,      4991,  ...,
+                            124995052, 124997552, 125000000]),
+       col_indices=tensor([    4,     7,    30,  ..., 49943, 49948, 49958]),
+       values=tensor([0.9374, 0.9751, 0.0126,  ..., 0.3756, 0.9643, 0.1575]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.6622, 0.7788, 0.7272,  ..., 0.9942, 0.4374, 0.8252])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 10.030322074890137 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2458,      4991,  ...,
+                            124995052, 124997552, 125000000]),
+       col_indices=tensor([    4,     7,    30,  ..., 49943, 49948, 49958]),
+       values=tensor([0.9374, 0.9751, 0.0126,  ..., 0.3756, 0.9643, 0.1575]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.6622, 0.7788, 0.7272,  ..., 0.9942, 0.4374, 0.8252])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 10.030322074890137 seconds
+
+[56.69, 40.33, 39.94, 40.83, 39.8, 39.71, 45.34, 39.69, 39.84, 40.29]
+[95.36]
+39.93093276023865
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 355, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.030322074890137, 'TIME_S_1KI': 28.254428379972214, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3807.8137480163573, 'W': 95.36}
+[56.69, 40.33, 39.94, 40.83, 39.8, 39.71, 45.34, 39.69, 39.84, 40.29, 40.99, 39.72, 40.01, 41.04, 39.84, 39.83, 45.74, 40.49, 39.69, 39.78]
+740.7149999999999
+37.03574999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 355, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.030322074890137, 'TIME_S_1KI': 28.254428379972214, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3807.8137480163573, 'W': 95.36, 'J_1KI': 10726.235909905232, 'W_1KI': 268.61971830985914, 'W_D': 58.324250000000006, 'J_D': 2328.941705041349, 'W_D_1KI': 164.293661971831, 'J_D_1KI': 462.79904780797466}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.json
index 53cb9a4..43cc709 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 125418, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.786596775054932, "TIME_S_1KI": 0.0860051729022543, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1353.9629184293747, "W": 103.38, "J_1KI": 10.795602851499583, "W_1KI": 0.8242835956561259, "W_D": 67.61375, "J_D": 885.5340518084168, "W_D_1KI": 0.5391072254381349, "J_D_1KI": 0.004298483674098893}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 126100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.468435764312744, "TIME_S_1KI": 0.09094715118408203, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1365.049603307247, "W": 103.35, "J_1KI": 10.825135632888557, "W_1KI": 0.8195876288659794, "W_D": 67.52475, "J_D": 891.8687295686602, "W_D_1KI": 0.5354857256145915, "J_D_1KI": 0.0042465164600681326}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.output
index b030af9..388fac8 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.05780529975891113}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.04126620292663574}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
-       col_indices=tensor([26335, 27290, 38418,  ..., 19756, 20120,  4010]),
-       values=tensor([0.3389, 0.0656, 0.4529,  ..., 0.8287, 0.8944, 0.8355]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([24401, 39088, 19902,  ...,  8711,  5895, 39358]),
+       values=tensor([0.9277, 0.4854, 0.8221,  ..., 0.5577, 0.9470, 0.1496]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.6330, 0.8862, 0.5805,  ..., 0.8180, 0.2124, 0.8337])
+tensor([0.1757, 0.4572, 0.4112,  ..., 0.1642, 0.8355, 0.9416])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,18 +15,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.05780529975891113 seconds
+Time: 0.04126620292663574 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18164', '-ss', '50000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.5206849575042725}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '25444', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.118638038635254}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 24999, 25000]),
-       col_indices=tensor([47070, 16594, 28343,  ..., 43440, 28747, 28655]),
-       values=tensor([0.5955, 0.4100, 0.8378,  ..., 0.8449, 0.3361, 0.6219]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 25000, 25000]),
+       col_indices=tensor([33127, 16683, 48525,  ...,  8852, 11318, 36089]),
+       values=tensor([0.1304, 0.0600, 0.7524,  ..., 0.8598, 0.7086, 0.6482]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0973, 0.1697, 0.0749,  ..., 0.0145, 0.6554, 0.2719])
+tensor([0.5990, 0.7765, 0.7637,  ..., 0.8510, 0.0540, 0.9906])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,18 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 1.5206849575042725 seconds
+Time: 2.118638038635254 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '125418', '-ss', '50000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.786596775054932}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '126100', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.468435764312744}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([27120, 12941, 15664,  ...,  3161, 41560, 29450]),
-       values=tensor([0.4509, 0.2974, 0.8733,  ..., 0.8770, 0.0483, 0.7990]),
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 2972, 25909, 36142,  ...,  6702,  9594, 36895]),
+       values=tensor([0.1019, 0.7173, 0.0602,  ..., 0.9921, 0.7905, 0.1013]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0817, 0.8974, 0.0414,  ..., 0.9825, 0.3309, 0.2047])
+tensor([0.0322, 0.1505, 0.0180,  ..., 0.8141, 0.7889, 0.6939])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,15 +53,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.786596775054932 seconds
+Time: 11.468435764312744 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([27120, 12941, 15664,  ...,  3161, 41560, 29450]),
-       values=tensor([0.4509, 0.2974, 0.8733,  ..., 0.8770, 0.0483, 0.7990]),
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 2972, 25909, 36142,  ...,  6702,  9594, 36895]),
+       values=tensor([0.1019, 0.7173, 0.0602,  ..., 0.9921, 0.7905, 0.1013]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0817, 0.8974, 0.0414,  ..., 0.9825, 0.3309, 0.2047])
+tensor([0.0322, 0.1505, 0.0180,  ..., 0.8141, 0.7889, 0.6939])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -69,13 +69,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.786596775054932 seconds
+Time: 11.468435764312744 seconds
 
-[40.88, 40.06, 39.87, 40.02, 39.61, 39.65, 39.87, 39.87, 39.51, 39.91]
-[103.38]
-13.096952199935913
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 125418, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.786596775054932, 'TIME_S_1KI': 0.0860051729022543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1353.9629184293747, 'W': 103.38}
-[40.88, 40.06, 39.87, 40.02, 39.61, 39.65, 39.87, 39.87, 39.51, 39.91, 40.56, 39.51, 39.82, 39.66, 39.34, 39.38, 39.55, 39.32, 39.66, 39.9]
-715.325
-35.76625
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 125418, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.786596775054932, 'TIME_S_1KI': 0.0860051729022543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1353.9629184293747, 'W': 103.38, 'J_1KI': 10.795602851499583, 'W_1KI': 0.8242835956561259, 'W_D': 67.61375, 'J_D': 885.5340518084168, 'W_D_1KI': 0.5391072254381349, 'J_D_1KI': 0.004298483674098893}
+[42.06, 39.46, 39.57, 39.5, 39.54, 39.4, 39.45, 39.76, 39.48, 39.85]
+[103.35]
+13.208027124404907
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 126100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.468435764312744, 'TIME_S_1KI': 0.09094715118408203, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1365.049603307247, 'W': 103.35}
+[42.06, 39.46, 39.57, 39.5, 39.54, 39.4, 39.45, 39.76, 39.48, 39.85, 41.35, 39.62, 39.91, 39.46, 40.32, 39.44, 39.48, 39.77, 41.03, 39.37]
+716.5049999999999
+35.82525
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 126100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.468435764312744, 'TIME_S_1KI': 0.09094715118408203, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1365.049603307247, 'W': 103.35, 'J_1KI': 10.825135632888557, 'W_1KI': 0.8195876288659794, 'W_D': 67.52475, 'J_D': 891.8687295686602, 'W_D_1KI': 0.5354857256145915, 'J_D_1KI': 0.0042465164600681326}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.json
index d831091..53264f0 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 106823, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.303539752960205, "TIME_S_1KI": 0.09645431932224527, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1429.2441740632057, "W": 111.25, "J_1KI": 13.379554721953191, "W_1KI": 1.0414423860030144, "W_D": 75.065, "J_D": 964.37046225667, "W_D_1KI": 0.7027044737556518, "J_D_1KI": 0.006578213247668122}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 107353, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.681612730026245, "TIME_S_1KI": 0.09949989967701178, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1431.663604259491, "W": 111.45, "J_1KI": 13.336037225410477, "W_1KI": 1.0381638147047592, "W_D": 75.3235, "J_D": 967.5900717401504, "W_D_1KI": 0.7016431771818207, "J_D_1KI": 0.006535850671912482}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.output
index a27a2d5..b6bc6a5 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.05483698844909668}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.04086589813232422}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      7,  ..., 124996, 124998,
+tensor(crow_indices=tensor([     0,      2,      4,  ..., 124997, 125000,
                             125000]),
-       col_indices=tensor([28119, 29640, 21715,  ..., 29199, 13516, 45728]),
-       values=tensor([0.3782, 0.4368, 0.3959,  ..., 0.8630, 0.5532, 0.4165]),
+       col_indices=tensor([41751, 43976,  3497,  ...,  3909, 25887, 38459]),
+       values=tensor([0.4106, 0.2816, 0.3184,  ..., 0.9617, 0.2316, 0.2041]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.8607, 0.2103, 0.4385,  ..., 0.0263, 0.3906, 0.3161])
+tensor([0.5700, 0.9216, 0.5233,  ..., 0.2364, 0.5823, 0.3818])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 0.05483698844909668 seconds
+Time: 0.04086589813232422 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19147', '-ss', '50000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.8820090293884277}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '25693', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.5129716396331787}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      6,  ..., 124991, 124992,
+tensor(crow_indices=tensor([     0,      1,      4,  ..., 124992, 124998,
                             125000]),
-       col_indices=tensor([32530, 36762,   311,  ..., 24158, 32618, 44758]),
-       values=tensor([0.9615, 0.3318, 0.5732,  ..., 0.8773, 0.1422, 0.4683]),
+       col_indices=tensor([46343, 20233, 31374,  ..., 45113,  7654, 17213]),
+       values=tensor([0.9969, 0.7071, 0.9644,  ..., 0.3342, 0.2384, 0.1403]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.1372, 0.3779, 0.3457,  ..., 0.7036, 0.6193, 0.2501])
+tensor([0.7937, 0.4795, 0.0028,  ..., 0.1875, 0.3993, 0.1011])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 1.8820090293884277 seconds
+Time: 2.5129716396331787 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '106823', '-ss', '50000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.303539752960205}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '107353', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.681612730026245}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      5,  ..., 124996, 124998,
+tensor(crow_indices=tensor([     0,      3,      5,  ..., 124996, 124998,
                             125000]),
-       col_indices=tensor([16502, 37527, 11294,  ..., 28497,  8084, 35661]),
-       values=tensor([0.2823, 0.8232, 0.0849,  ..., 0.6885, 0.2665, 0.0851]),
+       col_indices=tensor([18651, 25437, 48526,  ..., 23704, 10356, 37225]),
+       values=tensor([0.9489, 0.4951, 0.8868,  ..., 0.5017, 0.1543, 0.5981]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.8734, 0.5898, 0.3749,  ..., 0.2817, 0.4056, 0.3872])
+tensor([0.0752, 0.5705, 0.6730,  ..., 0.8462, 0.4093, 0.2438])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.303539752960205 seconds
+Time: 10.681612730026245 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      5,  ..., 124996, 124998,
+tensor(crow_indices=tensor([     0,      3,      5,  ..., 124996, 124998,
                             125000]),
-       col_indices=tensor([16502, 37527, 11294,  ..., 28497,  8084, 35661]),
-       values=tensor([0.2823, 0.8232, 0.0849,  ..., 0.6885, 0.2665, 0.0851]),
+       col_indices=tensor([18651, 25437, 48526,  ..., 23704, 10356, 37225]),
+       values=tensor([0.9489, 0.4951, 0.8868,  ..., 0.5017, 0.1543, 0.5981]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.8734, 0.5898, 0.3749,  ..., 0.2817, 0.4056, 0.3872])
+tensor([0.0752, 0.5705, 0.6730,  ..., 0.8462, 0.4093, 0.2438])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.303539752960205 seconds
+Time: 10.681612730026245 seconds
 
-[40.0, 39.65, 39.5, 39.28, 44.73, 39.57, 39.57, 39.75, 39.32, 39.43]
-[111.25]
-12.84713864326477
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 106823, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.303539752960205, 'TIME_S_1KI': 0.09645431932224527, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1429.2441740632057, 'W': 111.25}
-[40.0, 39.65, 39.5, 39.28, 44.73, 39.57, 39.57, 39.75, 39.32, 39.43, 41.22, 44.93, 39.95, 39.46, 39.81, 39.42, 39.55, 39.68, 39.52, 39.37]
-723.7
-36.185
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 106823, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.303539752960205, 'TIME_S_1KI': 0.09645431932224527, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1429.2441740632057, 'W': 111.25, 'J_1KI': 13.379554721953191, 'W_1KI': 1.0414423860030144, 'W_D': 75.065, 'J_D': 964.37046225667, 'W_D_1KI': 0.7027044737556518, 'J_D_1KI': 0.006578213247668122}
+[40.06, 44.71, 39.63, 39.86, 39.97, 39.65, 40.56, 41.5, 40.08, 39.24]
+[111.45]
+12.845792770385742
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 107353, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.681612730026245, 'TIME_S_1KI': 0.09949989967701178, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1431.663604259491, 'W': 111.45}
+[40.06, 44.71, 39.63, 39.86, 39.97, 39.65, 40.56, 41.5, 40.08, 39.24, 40.3, 39.38, 39.45, 39.74, 39.72, 39.41, 40.03, 39.65, 39.73, 39.32]
+722.53
+36.1265
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 107353, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.681612730026245, 'TIME_S_1KI': 0.09949989967701178, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1431.663604259491, 'W': 111.45, 'J_1KI': 13.336037225410477, 'W_1KI': 1.0381638147047592, 'W_D': 75.3235, 'J_D': 967.5900717401504, 'W_D_1KI': 0.7016431771818207, 'J_D_1KI': 0.006535850671912482}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.json
index 59bfbf5..2af5597 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 423606, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.169308185577393, "TIME_S_1KI": 0.024006525369275677, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1199.050221145153, "W": 95.75, "J_1KI": 2.8305789369016328, "W_1KI": 0.2260355141334164, "W_D": 60.24975, "J_D": 754.4906116077303, "W_D_1KI": 0.14223063412699538, "J_D_1KI": 0.0003357616136858198}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 439130, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.833286046981812, "TIME_S_1KI": 0.024669883740536542, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1236.1940939807891, "W": 96.14, "J_1KI": 2.8150982487664, "W_1KI": 0.2189328900325644, "W_D": 60.80775, "J_D": 781.8824778267145, "W_D_1KI": 0.138473231161615, "J_D_1KI": 0.0003153353930763441}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.output
index ae86751..24549b0 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.018892288208007812}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.03937959671020508}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    4,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([1108, 1116, 4456,  ..., 2396,  548, 1385]),
-       values=tensor([0.8638, 0.8794, 0.8595,  ..., 0.2787, 0.2270, 0.2436]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([4278, 4518, 4983,  ..., 2661, 3298,  613]),
+       values=tensor([0.7810, 0.3230, 0.0701,  ..., 0.3279, 0.8382, 0.1710]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.1028, 0.3454, 0.0668,  ..., 0.0203, 0.1099, 0.2752])
+tensor([0.6892, 0.5908, 0.0931,  ..., 0.5677, 0.7492, 0.3206])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.018892288208007812 seconds
+Time: 0.03937959671020508 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '55578', '-ss', '5000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.3776216506958008}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '26663', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6375362873077393}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([1067, 4726, 2617,  ..., 4515, 4937,  207]),
-       values=tensor([0.7749, 0.8447, 0.6931,  ..., 0.5698, 0.5658, 0.7624]),
+tensor(crow_indices=tensor([   0,    1,    3,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([4464,   78, 2804,  ..., 4372,   12, 2382]),
+       values=tensor([0.2558, 0.8624, 0.3310,  ..., 0.1585, 0.2890, 0.7997]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.8259, 0.6183, 0.2744,  ..., 0.6644, 0.1716, 0.4385])
+tensor([0.1645, 0.8666, 0.7353,  ..., 0.9720, 0.2835, 0.6947])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 1.3776216506958008 seconds
+Time: 0.6375362873077393 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '423606', '-ss', '5000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.169308185577393}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '439130', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.833286046981812}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2497, 2498, 2500]),
-       col_indices=tensor([ 723, 3357, 4021,  ..., 1038, 2195, 2669]),
-       values=tensor([0.5380, 0.6250, 0.5522,  ..., 0.2239, 0.7354, 0.7870]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([4485, 3797,   96,  ...,  993, 4029, 4486]),
+       values=tensor([0.6070, 0.3408, 0.8766,  ..., 0.8191, 0.5508, 0.4514]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.8647, 0.0404, 0.2214,  ..., 0.2716, 0.6887, 0.8481])
+tensor([0.5455, 0.6563, 0.3370,  ..., 0.1536, 0.0207, 0.1893])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.169308185577393 seconds
+Time: 10.833286046981812 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2497, 2498, 2500]),
-       col_indices=tensor([ 723, 3357, 4021,  ..., 1038, 2195, 2669]),
-       values=tensor([0.5380, 0.6250, 0.5522,  ..., 0.2239, 0.7354, 0.7870]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([4485, 3797,   96,  ...,  993, 4029, 4486]),
+       values=tensor([0.6070, 0.3408, 0.8766,  ..., 0.8191, 0.5508, 0.4514]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.8647, 0.0404, 0.2214,  ..., 0.2716, 0.6887, 0.8481])
+tensor([0.5455, 0.6563, 0.3370,  ..., 0.1536, 0.0207, 0.1893])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.169308185577393 seconds
+Time: 10.833286046981812 seconds
 
-[40.09, 39.04, 39.2, 39.28, 39.5, 39.12, 39.02, 39.36, 39.0, 39.1]
-[95.75]
-12.522717714309692
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 423606, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.169308185577393, 'TIME_S_1KI': 0.024006525369275677, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1199.050221145153, 'W': 95.75}
-[40.09, 39.04, 39.2, 39.28, 39.5, 39.12, 39.02, 39.36, 39.0, 39.1, 39.76, 39.19, 39.37, 38.99, 39.74, 39.45, 39.52, 39.05, 39.58, 44.24]
-710.005
-35.50025
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 423606, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.169308185577393, 'TIME_S_1KI': 0.024006525369275677, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1199.050221145153, 'W': 95.75, 'J_1KI': 2.8305789369016328, 'W_1KI': 0.2260355141334164, 'W_D': 60.24975, 'J_D': 754.4906116077303, 'W_D_1KI': 0.14223063412699538, 'J_D_1KI': 0.0003357616136858198}
+[40.81, 39.45, 39.13, 38.94, 38.98, 39.39, 39.0, 39.64, 39.01, 40.46]
+[96.14]
+12.858270168304443
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 439130, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.833286046981812, 'TIME_S_1KI': 0.024669883740536542, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1236.1940939807891, 'W': 96.14}
+[40.81, 39.45, 39.13, 38.94, 38.98, 39.39, 39.0, 39.64, 39.01, 40.46, 39.76, 39.08, 39.11, 38.91, 39.52, 38.95, 39.43, 39.01, 39.09, 38.98]
+706.645
+35.33225
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 439130, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.833286046981812, 'TIME_S_1KI': 0.024669883740536542, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1236.1940939807891, 'W': 96.14, 'J_1KI': 2.8150982487664, 'W_1KI': 0.2189328900325644, 'W_D': 60.80775, 'J_D': 781.8824778267145, 'W_D_1KI': 0.138473231161615, 'J_D_1KI': 0.0003153353930763441}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.json
index 1eeb5de..bfd0516 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 247437, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.32590627670288, "TIME_S_1KI": 0.04173145599365851, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1233.848487944603, "W": 98.03, "J_1KI": 4.986515710846005, "W_1KI": 0.39618165432008956, "W_D": 62.5795, "J_D": 787.6529781835079, "W_D_1KI": 0.2529108419516887, "J_D_1KI": 0.001022122164234487}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 244760, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.325824975967407, "TIME_S_1KI": 0.042187550972247946, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1282.2631218481063, "W": 98.38, "J_1KI": 5.238858971433674, "W_1KI": 0.40194476221604836, "W_D": 62.769499999999994, "J_D": 818.1237551010846, "W_D_1KI": 0.2564532603366563, "J_D_1KI": 0.0010477743926158533}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.output
index f512a45..edb881e 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.016702651977539062}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.01943659782409668}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,    10,  ..., 24993, 24996, 25000]),
-       col_indices=tensor([ 203, 3164, 3874,  ..., 1660, 2575, 4898]),
-       values=tensor([0.2509, 0.0733, 0.7857,  ..., 0.9782, 0.1584, 0.7182]),
+tensor(crow_indices=tensor([    0,     4,     9,  ..., 24986, 24993, 25000]),
+       col_indices=tensor([ 944, 1102, 1187,  ..., 2452, 3392, 3609]),
+       values=tensor([0.7522, 0.3420, 0.2595,  ..., 0.1979, 0.1722, 0.7557]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.6754, 0.2035, 0.5445,  ..., 0.8964, 0.5875, 0.7630])
+tensor([0.0567, 0.9477, 0.6395,  ..., 0.6412, 0.4925, 0.4268])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.016702651977539062 seconds
+Time: 0.01943659782409668 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '62864', '-ss', '5000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.6676299571990967}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '54021', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.317448377609253}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     7,  ..., 24992, 24998, 25000]),
-       col_indices=tensor([1528, 1565, 2407,  ..., 4843,  196, 1526]),
-       values=tensor([0.9615, 0.4377, 0.6921,  ..., 0.0433, 0.3280, 0.0962]),
+tensor(crow_indices=tensor([    0,     4,     6,  ..., 24991, 24996, 25000]),
+       col_indices=tensor([1724, 1740, 2512,  ..., 2523, 3784, 4136]),
+       values=tensor([0.7377, 0.7615, 0.8356,  ..., 0.4074, 0.7017, 0.6251]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1897, 0.1898, 0.6419,  ..., 0.9248, 0.4513, 0.5147])
+tensor([0.1495, 0.6392, 0.8753,  ..., 0.6190, 0.2728, 0.3068])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 2.6676299571990967 seconds
+Time: 2.317448377609253 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '247437', '-ss', '5000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.32590627670288}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '244760', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.325824975967407}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     9,    13,  ..., 24992, 24996, 25000]),
-       col_indices=tensor([ 429,  548,  735,  ..., 2923, 3331, 3611]),
-       values=tensor([0.1470, 0.7094, 0.7244,  ..., 0.3013, 0.3840, 0.1701]),
+tensor(crow_indices=tensor([    0,     2,     3,  ..., 24992, 24995, 25000]),
+       col_indices=tensor([1055, 3537, 4773,  ..., 2200, 4406, 4831]),
+       values=tensor([0.3935, 0.5459, 0.0701,  ..., 0.5783, 0.3040, 0.1605]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0349, 0.9280, 0.8549,  ..., 0.2131, 0.1223, 0.0130])
+tensor([0.3300, 0.9008, 0.1999,  ..., 0.9229, 0.2876, 0.9678])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.32590627670288 seconds
+Time: 10.325824975967407 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     9,    13,  ..., 24992, 24996, 25000]),
-       col_indices=tensor([ 429,  548,  735,  ..., 2923, 3331, 3611]),
-       values=tensor([0.1470, 0.7094, 0.7244,  ..., 0.3013, 0.3840, 0.1701]),
+tensor(crow_indices=tensor([    0,     2,     3,  ..., 24992, 24995, 25000]),
+       col_indices=tensor([1055, 3537, 4773,  ..., 2200, 4406, 4831]),
+       values=tensor([0.3935, 0.5459, 0.0701,  ..., 0.5783, 0.3040, 0.1605]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0349, 0.9280, 0.8549,  ..., 0.2131, 0.1223, 0.0130])
+tensor([0.3300, 0.9008, 0.1999,  ..., 0.9229, 0.2876, 0.9678])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.32590627670288 seconds
+Time: 10.325824975967407 seconds
 
-[40.5, 39.52, 39.15, 39.13, 39.57, 40.5, 39.17, 39.06, 39.52, 39.1]
-[98.03]
-12.586437702178955
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 247437, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.32590627670288, 'TIME_S_1KI': 0.04173145599365851, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1233.848487944603, 'W': 98.03}
-[40.5, 39.52, 39.15, 39.13, 39.57, 40.5, 39.17, 39.06, 39.52, 39.1, 40.17, 39.6, 39.6, 39.15, 39.15, 39.05, 39.05, 39.08, 39.25, 39.15]
-709.01
-35.4505
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 247437, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.32590627670288, 'TIME_S_1KI': 0.04173145599365851, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1233.848487944603, 'W': 98.03, 'J_1KI': 4.986515710846005, 'W_1KI': 0.39618165432008956, 'W_D': 62.5795, 'J_D': 787.6529781835079, 'W_D_1KI': 0.2529108419516887, 'J_D_1KI': 0.001022122164234487}
+[39.76, 44.57, 39.04, 39.05, 39.19, 38.89, 39.14, 39.36, 39.43, 39.34]
+[98.38]
+13.033778429031372
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 244760, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.325824975967407, 'TIME_S_1KI': 0.042187550972247946, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.2631218481063, 'W': 98.38}
+[39.76, 44.57, 39.04, 39.05, 39.19, 38.89, 39.14, 39.36, 39.43, 39.34, 40.19, 39.51, 39.02, 39.31, 39.14, 40.3, 39.01, 39.01, 38.96, 39.27]
+712.21
+35.6105
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 244760, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.325824975967407, 'TIME_S_1KI': 0.042187550972247946, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.2631218481063, 'W': 98.38, 'J_1KI': 5.238858971433674, 'W_1KI': 0.40194476221604836, 'W_D': 62.769499999999994, 'J_D': 818.1237551010846, 'W_D_1KI': 0.2564532603366563, 'J_D_1KI': 0.0010477743926158533}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.json
index 685c3bf..eaa7f45 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 163068, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.72166895866394, "TIME_S_1KI": 0.07188209187985345, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1665.769057817459, "W": 116.82, "J_1KI": 10.215180524796153, "W_1KI": 0.7163882552064169, "W_D": 81.24949999999998, "J_D": 1158.5593482549189, "W_D_1KI": 0.4982553290651751, "J_D_1KI": 0.0030555064700933054}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 152771, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.930854320526123, "TIME_S_1KI": 0.07155058434209453, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1604.5886056470872, "W": 115.67, "J_1KI": 10.503227743793568, "W_1KI": 0.7571463170366104, "W_D": 79.54775000000001, "J_D": 1103.4962674406768, "W_D_1KI": 0.5206992819317804, "J_D_1KI": 0.0034083646891869554}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.output
index bc4d2ed..f40d406 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.02829742431640625}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.023181676864624023}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     58,    108,  ..., 249919, 249959,
+tensor(crow_indices=tensor([     0,     55,     94,  ..., 249894, 249941,
                             250000]),
-       col_indices=tensor([  73,  104,  551,  ..., 4719, 4888, 4958]),
-       values=tensor([0.4939, 0.4915, 0.0888,  ..., 0.3493, 0.1552, 0.1459]),
+       col_indices=tensor([  15,   20,  118,  ..., 4709, 4950, 4982]),
+       values=tensor([0.3949, 0.6252, 0.7734,  ..., 0.1433, 0.8658, 0.9936]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.1377, 0.0837, 0.3150,  ..., 0.5794, 0.8670, 0.3865])
+tensor([0.9810, 0.2218, 0.5564,  ..., 0.7421, 0.8578, 0.2367])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 0.02829742431640625 seconds
+Time: 0.023181676864624023 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '37105', '-ss', '5000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.3891899585723877}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '45294', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.113070011138916}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     55,     99,  ..., 249890, 249937,
+tensor(crow_indices=tensor([     0,     46,     98,  ..., 249898, 249953,
                             250000]),
-       col_indices=tensor([   6,   32,   44,  ..., 4844, 4921, 4988]),
-       values=tensor([0.1281, 0.2469, 0.7745,  ..., 0.0638, 0.9042, 0.9189]),
+       col_indices=tensor([   1,   15,   17,  ..., 4938, 4952, 4956]),
+       values=tensor([0.7179, 0.8487, 0.2483,  ..., 0.2443, 0.9815, 0.3824]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.6302, 0.1474, 0.6987,  ..., 0.1092, 0.0062, 0.2645])
+tensor([0.6142, 0.5826, 0.7574,  ..., 0.9763, 0.8571, 0.0747])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 2.3891899585723877 seconds
+Time: 3.113070011138916 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '163068', '-ss', '5000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.72166895866394}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '152771', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.930854320526123}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     45,     94,  ..., 249900, 249951,
+tensor(crow_indices=tensor([     0,     47,     98,  ..., 249882, 249937,
                             250000]),
-       col_indices=tensor([  17,  114,  188,  ..., 4806, 4921, 4968]),
-       values=tensor([0.1229, 0.8785, 0.6808,  ..., 0.9268, 0.7326, 0.7148]),
+       col_indices=tensor([ 114,  217,  246,  ..., 4895, 4908, 4986]),
+       values=tensor([0.9429, 0.8691, 0.4109,  ..., 0.0935, 0.8718, 0.7828]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4545, 0.7872, 0.8321,  ..., 0.0206, 0.6423, 0.1627])
+tensor([0.2849, 0.2833, 0.3387,  ..., 0.0747, 0.2812, 0.2237])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 11.72166895866394 seconds
+Time: 10.930854320526123 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     45,     94,  ..., 249900, 249951,
+tensor(crow_indices=tensor([     0,     47,     98,  ..., 249882, 249937,
                             250000]),
-       col_indices=tensor([  17,  114,  188,  ..., 4806, 4921, 4968]),
-       values=tensor([0.1229, 0.8785, 0.6808,  ..., 0.9268, 0.7326, 0.7148]),
+       col_indices=tensor([ 114,  217,  246,  ..., 4895, 4908, 4986]),
+       values=tensor([0.9429, 0.8691, 0.4109,  ..., 0.0935, 0.8718, 0.7828]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4545, 0.7872, 0.8321,  ..., 0.0206, 0.6423, 0.1627])
+tensor([0.2849, 0.2833, 0.3387,  ..., 0.0747, 0.2812, 0.2237])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 11.72166895866394 seconds
+Time: 10.930854320526123 seconds
 
-[40.87, 39.29, 39.18, 39.72, 39.08, 39.54, 39.6, 39.54, 39.86, 39.27]
-[116.82]
-14.259279727935791
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 163068, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.72166895866394, 'TIME_S_1KI': 0.07188209187985345, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1665.769057817459, 'W': 116.82}
-[40.87, 39.29, 39.18, 39.72, 39.08, 39.54, 39.6, 39.54, 39.86, 39.27, 40.71, 39.19, 39.43, 39.27, 39.46, 39.83, 39.54, 39.53, 39.37, 39.11]
-711.4100000000001
-35.5705
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 163068, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.72166895866394, 'TIME_S_1KI': 0.07188209187985345, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1665.769057817459, 'W': 116.82, 'J_1KI': 10.215180524796153, 'W_1KI': 0.7163882552064169, 'W_D': 81.24949999999998, 'J_D': 1158.5593482549189, 'W_D_1KI': 0.4982553290651751, 'J_D_1KI': 0.0030555064700933054}
+[39.91, 39.78, 40.82, 39.59, 39.84, 39.72, 39.23, 39.32, 39.18, 39.93]
+[115.67]
+13.872124195098877
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 152771, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.930854320526123, 'TIME_S_1KI': 0.07155058434209453, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1604.5886056470872, 'W': 115.67}
+[39.91, 39.78, 40.82, 39.59, 39.84, 39.72, 39.23, 39.32, 39.18, 39.93, 40.03, 39.32, 39.26, 39.5, 39.74, 39.61, 39.66, 39.33, 40.94, 55.34]
+722.4449999999999
+36.122249999999994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 152771, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.930854320526123, 'TIME_S_1KI': 0.07155058434209453, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1604.5886056470872, 'W': 115.67, 'J_1KI': 10.503227743793568, 'W_1KI': 0.7571463170366104, 'W_D': 79.54775000000001, 'J_D': 1103.4962674406768, 'W_D_1KI': 0.5206992819317804, 'J_D_1KI': 0.0034083646891869554}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json
index 022fbf8..7ef43d0 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 90395, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.401180028915405, "TIME_S_1KI": 0.11506366534559882, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1713.355565071106, "W": 132.0, "J_1KI": 18.954096632237466, "W_1KI": 1.4602577576193374, "W_D": 96.25175, "J_D": 1249.344481138885, "W_D_1KI": 1.0647906410752808, "J_D_1KI": 0.011779309044474592}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 91453, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.426929712295532, "TIME_S_1KI": 0.11401408059107446, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1730.480533401966, "W": 133.63, "J_1KI": 18.922075092145324, "W_1KI": 1.4611877139076903, "W_D": 97.79375, "J_D": 1266.4085958495737, "W_D_1KI": 1.0693334280996796, "J_D_1KI": 0.011692710223827316}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.output
index 6ede2ab..1b0d7e5 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03801298141479492}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03647494316101074}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     279,     546,  ..., 1249528,
-                            1249760, 1250000]),
-       col_indices=tensor([  17,   21,   26,  ..., 4944, 4980, 4991]),
-       values=tensor([0.9138, 0.1459, 0.7159,  ..., 0.0773, 0.4834, 0.3377]),
+tensor(crow_indices=tensor([      0,     224,     472,  ..., 1249485,
+                            1249755, 1250000]),
+       col_indices=tensor([  42,   63,   65,  ..., 4929, 4934, 4955]),
+       values=tensor([0.9997, 0.6564, 0.8793,  ..., 0.2501, 0.6294, 0.5996]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.0568, 0.1587, 0.8688,  ..., 0.8476, 0.8640, 0.6593])
+tensor([0.2116, 0.5433, 0.6548,  ..., 0.8394, 0.9621, 0.4668])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 0.03801298141479492 seconds
+Time: 0.03647494316101074 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27622', '-ss', '5000', '-sd', '0.05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 3.2084648609161377}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28786', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 3.304973840713501}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     251,     530,  ..., 1249473,
-                            1249739, 1250000]),
-       col_indices=tensor([  53,   63,   72,  ..., 4941, 4984, 4995]),
-       values=tensor([0.4190, 0.6332, 0.1682,  ..., 0.1102, 0.0295, 0.1696]),
+tensor(crow_indices=tensor([      0,     254,     516,  ..., 1249479,
+                            1249742, 1250000]),
+       col_indices=tensor([  25,   80,   95,  ..., 4912, 4972, 4985]),
+       values=tensor([0.8354, 0.9904, 0.9019,  ..., 0.7510, 0.5312, 0.2669]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.4081, 0.8253, 0.9060,  ..., 0.4379, 0.8960, 0.7193])
+tensor([0.3432, 0.7192, 0.5992,  ..., 0.6854, 0.7940, 0.1194])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 3.2084648609161377 seconds
+Time: 3.304973840713501 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '90395', '-ss', '5000', '-sd', '0.05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.401180028915405}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '91453', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.426929712295532}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     225,     477,  ..., 1249532,
-                            1249754, 1250000]),
-       col_indices=tensor([   0,    4,   16,  ..., 4911, 4963, 4980]),
-       values=tensor([0.5027, 0.8615, 0.8405,  ..., 0.5051, 0.1395, 0.2376]),
+tensor(crow_indices=tensor([      0,     268,     515,  ..., 1249493,
+                            1249744, 1250000]),
+       col_indices=tensor([   7,   45,   98,  ..., 4983, 4985, 4997]),
+       values=tensor([0.9678, 0.6032, 0.4563,  ..., 0.0809, 0.2549, 0.8523]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.7929, 0.2058, 0.1103,  ..., 0.0989, 0.8674, 0.8642])
+tensor([0.9236, 0.3779, 0.8296,  ..., 0.1378, 0.9857, 0.0479])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.401180028915405 seconds
+Time: 10.426929712295532 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     225,     477,  ..., 1249532,
-                            1249754, 1250000]),
-       col_indices=tensor([   0,    4,   16,  ..., 4911, 4963, 4980]),
-       values=tensor([0.5027, 0.8615, 0.8405,  ..., 0.5051, 0.1395, 0.2376]),
+tensor(crow_indices=tensor([      0,     268,     515,  ..., 1249493,
+                            1249744, 1250000]),
+       col_indices=tensor([   7,   45,   98,  ..., 4983, 4985, 4997]),
+       values=tensor([0.9678, 0.6032, 0.4563,  ..., 0.0809, 0.2549, 0.8523]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.7929, 0.2058, 0.1103,  ..., 0.0989, 0.8674, 0.8642])
+tensor([0.9236, 0.3779, 0.8296,  ..., 0.1378, 0.9857, 0.0479])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.401180028915405 seconds
+Time: 10.426929712295532 seconds
 
-[40.46, 40.0, 39.58, 39.65, 39.39, 39.96, 39.38, 39.29, 39.31, 39.34]
-[132.0]
-12.979966402053833
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 90395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.401180028915405, 'TIME_S_1KI': 0.11506366534559882, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1713.355565071106, 'W': 132.0}
-[40.46, 40.0, 39.58, 39.65, 39.39, 39.96, 39.38, 39.29, 39.31, 39.34, 40.66, 39.72, 41.06, 39.66, 39.37, 39.71, 39.69, 39.36, 39.44, 40.33]
-714.9649999999999
-35.74825
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 90395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.401180028915405, 'TIME_S_1KI': 0.11506366534559882, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1713.355565071106, 'W': 132.0, 'J_1KI': 18.954096632237466, 'W_1KI': 1.4602577576193374, 'W_D': 96.25175, 'J_D': 1249.344481138885, 'W_D_1KI': 1.0647906410752808, 'J_D_1KI': 0.011779309044474592}
+[46.82, 39.84, 39.43, 39.46, 39.39, 39.51, 39.45, 40.88, 39.31, 39.7]
+[133.63]
+12.949790716171265
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91453, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.426929712295532, 'TIME_S_1KI': 0.11401408059107446, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1730.480533401966, 'W': 133.63}
+[46.82, 39.84, 39.43, 39.46, 39.39, 39.51, 39.45, 40.88, 39.31, 39.7, 40.56, 39.85, 39.52, 39.47, 39.48, 39.65, 39.37, 39.3, 39.63, 39.29]
+716.7249999999999
+35.83624999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91453, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.426929712295532, 'TIME_S_1KI': 0.11401408059107446, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1730.480533401966, 'W': 133.63, 'J_1KI': 18.922075092145324, 'W_1KI': 1.4611877139076903, 'W_D': 97.79375, 'J_D': 1266.4085958495737, 'W_D_1KI': 1.0693334280996796, 'J_D_1KI': 0.011692710223827316}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json
index 28b4bfb..0659b36 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 52843, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.472357749938965, "TIME_S_1KI": 0.19817871335728413, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1834.1895242333412, "W": 138.65, "J_1KI": 34.710170206713116, "W_1KI": 2.623810154608936, "W_D": 102.62450000000001, "J_D": 1357.611127513051, "W_D_1KI": 1.9420642279961398, "J_D_1KI": 0.036751589198117815}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 52932, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.611177206039429, "TIME_S_1KI": 0.20046809502832744, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1857.9822360515595, "W": 137.3, "J_1KI": 35.10130424037557, "W_1KI": 2.5938940527469208, "W_D": 101.48075000000001, "J_D": 1373.266065558553, "W_D_1KI": 1.91719092420464, "J_D_1KI": 0.03621988445939394}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output
index 05a194a..29e9499 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.05003714561462402}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.048235177993774414}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     498,     977,  ..., 2499006,
-                            2499489, 2500000]),
-       col_indices=tensor([   2,   20,   22,  ..., 4935, 4945, 4946]),
-       values=tensor([0.7520, 0.3359, 0.1395,  ..., 0.8155, 0.8337, 0.5892]),
+tensor(crow_indices=tensor([      0,     503,     960,  ..., 2498968,
+                            2499463, 2500000]),
+       col_indices=tensor([   5,    6,   43,  ..., 4985, 4990, 4995]),
+       values=tensor([0.9774, 0.9396, 0.3252,  ..., 0.3425, 0.9018, 0.9739]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3287, 0.7670, 0.4633,  ..., 0.8662, 0.8996, 0.4236])
+tensor([0.9855, 0.5305, 0.7022,  ..., 0.7874, 0.9309, 0.2173])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 0.05003714561462402 seconds
+Time: 0.048235177993774414 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '20984', '-ss', '5000', '-sd', '0.1', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 4.169527769088745}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21768', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 4.318019390106201}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     527,    1017,  ..., 2498957,
-                            2499469, 2500000]),
-       col_indices=tensor([   0,    2,   46,  ..., 4971, 4981, 4983]),
-       values=tensor([0.4264, 0.7891, 0.1289,  ..., 0.9402, 0.9265, 0.8274]),
+tensor(crow_indices=tensor([      0,     497,     971,  ..., 2498991,
+                            2499512, 2500000]),
+       col_indices=tensor([  22,   34,   57,  ..., 4955, 4959, 4978]),
+       values=tensor([0.4334, 0.8588, 0.8612,  ..., 0.1945, 0.8902, 0.3845]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1592, 0.0229, 0.7345,  ..., 0.9022, 0.9396, 0.4003])
+tensor([0.0333, 0.8449, 0.6470,  ..., 0.5455, 0.8886, 0.2722])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 4.169527769088745 seconds
+Time: 4.318019390106201 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52843', '-ss', '5000', '-sd', '0.1', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.472357749938965}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52932', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.611177206039429}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     536,    1043,  ..., 2499033,
-                            2499531, 2500000]),
-       col_indices=tensor([   2,    4,    9,  ..., 4990, 4992, 4998]),
-       values=tensor([0.3950, 0.1857, 0.2386,  ..., 0.4312, 0.4990, 0.5416]),
+tensor(crow_indices=tensor([      0,     513,    1021,  ..., 2498990,
+                            2499487, 2500000]),
+       col_indices=tensor([   1,    6,   11,  ..., 4985, 4989, 4999]),
+       values=tensor([0.0321, 0.9116, 0.7036,  ..., 0.4498, 0.4675, 0.8894]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5233, 0.3838, 0.2090,  ..., 0.9440, 0.1891, 0.8384])
+tensor([0.1398, 0.0564, 0.1701,  ..., 0.8437, 0.6801, 0.5112])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.472357749938965 seconds
+Time: 10.611177206039429 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     536,    1043,  ..., 2499033,
-                            2499531, 2500000]),
-       col_indices=tensor([   2,    4,    9,  ..., 4990, 4992, 4998]),
-       values=tensor([0.3950, 0.1857, 0.2386,  ..., 0.4312, 0.4990, 0.5416]),
+tensor(crow_indices=tensor([      0,     513,    1021,  ..., 2498990,
+                            2499487, 2500000]),
+       col_indices=tensor([   1,    6,   11,  ..., 4985, 4989, 4999]),
+       values=tensor([0.0321, 0.9116, 0.7036,  ..., 0.4498, 0.4675, 0.8894]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5233, 0.3838, 0.2090,  ..., 0.9440, 0.1891, 0.8384])
+tensor([0.1398, 0.0564, 0.1701,  ..., 0.8437, 0.6801, 0.5112])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.472357749938965 seconds
+Time: 10.611177206039429 seconds
 
-[40.4, 39.54, 40.0, 39.38, 39.54, 39.86, 39.47, 40.22, 39.37, 39.4]
-[138.65]
-13.228918313980103
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52843, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.472357749938965, 'TIME_S_1KI': 0.19817871335728413, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1834.1895242333412, 'W': 138.65}
-[40.4, 39.54, 40.0, 39.38, 39.54, 39.86, 39.47, 40.22, 39.37, 39.4, 41.49, 40.09, 39.59, 39.6, 39.65, 39.39, 39.42, 39.69, 45.2, 39.71]
-720.51
-36.0255
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52843, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.472357749938965, 'TIME_S_1KI': 0.19817871335728413, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1834.1895242333412, 'W': 138.65, 'J_1KI': 34.710170206713116, 'W_1KI': 2.623810154608936, 'W_D': 102.62450000000001, 'J_D': 1357.611127513051, 'W_D_1KI': 1.9420642279961398, 'J_D_1KI': 0.036751589198117815}
+[40.25, 40.11, 39.45, 39.51, 39.44, 40.31, 40.2, 39.34, 39.39, 40.88]
+[137.3]
+13.532281398773193
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52932, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.611177206039429, 'TIME_S_1KI': 0.20046809502832744, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1857.9822360515595, 'W': 137.3}
+[40.25, 40.11, 39.45, 39.51, 39.44, 40.31, 40.2, 39.34, 39.39, 40.88, 40.72, 39.81, 39.61, 39.72, 39.46, 39.86, 39.86, 39.84, 39.74, 39.62]
+716.385
+35.81925
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52932, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.611177206039429, 'TIME_S_1KI': 0.20046809502832744, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1857.9822360515595, 'W': 137.3, 'J_1KI': 35.10130424037557, 'W_1KI': 2.5938940527469208, 'W_D': 101.48075000000001, 'J_D': 1373.266065558553, 'W_D_1KI': 1.91719092420464, 'J_D_1KI': 0.03621988445939394}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json
index 3a471cf..5d59e41 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28798, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.444172620773315, "TIME_S_1KI": 0.36267006808713503, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1937.146224603653, "W": 139.06, "J_1KI": 67.26669298575086, "W_1KI": 4.8288075560802834, "W_D": 102.9745, "J_D": 1434.4647195847035, "W_D_1KI": 3.5757517883186334, "J_D_1KI": 0.12416667089098664}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28675, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.367558717727661, "TIME_S_1KI": 0.36155392215266474, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1923.0010931968689, "W": 139.28, "J_1KI": 67.06193873397974, "W_1KI": 4.857192676547515, "W_D": 103.496, "J_D": 1428.9411339855194, "W_D_1KI": 3.6092763731473405, "J_D_1KI": 0.12586840010975905}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output
index fd0984f..5634c2b 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.06763482093811035}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.0704355239868164}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     956,    1961,  ..., 4997943,
-                            4998975, 5000000]),
-       col_indices=tensor([   6,   18,   19,  ..., 4986, 4993, 4998]),
-       values=tensor([0.4638, 0.8169, 0.7421,  ..., 0.5926, 0.1207, 0.0279]),
+tensor(crow_indices=tensor([      0,    1011,    2020,  ..., 4998002,
+                            4999000, 5000000]),
+       col_indices=tensor([   0,    2,    4,  ..., 4985, 4986, 4995]),
+       values=tensor([0.5170, 0.7696, 0.0601,  ..., 0.2830, 0.0857, 0.2394]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1902, 0.8341, 0.1608,  ..., 0.1172, 0.3175, 0.0262])
+tensor([0.2191, 0.6347, 0.0324,  ..., 0.6963, 0.4125, 0.5924])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 0.06763482093811035 seconds
+Time: 0.0704355239868164 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15524', '-ss', '5000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 5.659992933273315}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '14907', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 5.458455324172974}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     987,    2000,  ..., 4998018,
-                            4999001, 5000000]),
-       col_indices=tensor([   4,   16,   17,  ..., 4983, 4988, 4998]),
-       values=tensor([0.3168, 0.3066, 0.1113,  ..., 0.0328, 0.5136, 0.1275]),
+tensor(crow_indices=tensor([      0,     968,    1996,  ..., 4998022,
+                            4999009, 5000000]),
+       col_indices=tensor([   2,    4,    6,  ..., 4985, 4989, 4995]),
+       values=tensor([0.4424, 0.0227, 0.5193,  ..., 0.0048, 0.8974, 0.3541]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.9704, 0.5285, 0.3815,  ..., 0.6149, 0.3291, 0.1983])
+tensor([0.6849, 0.5933, 0.5488,  ..., 0.6403, 0.3844, 0.3278])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 5.659992933273315 seconds
+Time: 5.458455324172974 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28798', '-ss', '5000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.444172620773315}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28675', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.367558717727661}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1005,    2014,  ..., 4998103,
-                            4999056, 5000000]),
-       col_indices=tensor([   4,    9,   14,  ..., 4980, 4983, 4993]),
-       values=tensor([0.7293, 0.3445, 0.3834,  ..., 0.7374, 0.4715, 0.7945]),
+tensor(crow_indices=tensor([      0,    1035,    2088,  ..., 4997978,
+                            4999013, 5000000]),
+       col_indices=tensor([   6,   12,   18,  ..., 4989, 4993, 4999]),
+       values=tensor([0.0591, 0.4462, 0.0291,  ..., 0.8101, 0.4669, 0.6210]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.9531, 0.9906, 0.0327,  ..., 0.2819, 0.9884, 0.0185])
+tensor([0.3435, 0.0770, 0.4550,  ..., 0.4506, 0.4973, 0.4864])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.444172620773315 seconds
+Time: 10.367558717727661 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1005,    2014,  ..., 4998103,
-                            4999056, 5000000]),
-       col_indices=tensor([   4,    9,   14,  ..., 4980, 4983, 4993]),
-       values=tensor([0.7293, 0.3445, 0.3834,  ..., 0.7374, 0.4715, 0.7945]),
+tensor(crow_indices=tensor([      0,    1035,    2088,  ..., 4997978,
+                            4999013, 5000000]),
+       col_indices=tensor([   6,   12,   18,  ..., 4989, 4993, 4999]),
+       values=tensor([0.0591, 0.4462, 0.0291,  ..., 0.8101, 0.4669, 0.6210]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.9531, 0.9906, 0.0327,  ..., 0.2819, 0.9884, 0.0185])
+tensor([0.3435, 0.0770, 0.4550,  ..., 0.4506, 0.4973, 0.4864])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.444172620773315 seconds
+Time: 10.367558717727661 seconds
 
-[40.89, 39.68, 39.94, 39.87, 39.8, 39.94, 40.85, 40.96, 39.65, 40.01]
-[139.06]
-13.930290699005127
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28798, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.444172620773315, 'TIME_S_1KI': 0.36267006808713503, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1937.146224603653, 'W': 139.06}
-[40.89, 39.68, 39.94, 39.87, 39.8, 39.94, 40.85, 40.96, 39.65, 40.01, 41.32, 40.08, 40.09, 40.7, 40.16, 39.61, 39.76, 39.88, 39.81, 39.64]
-721.71
-36.0855
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28798, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.444172620773315, 'TIME_S_1KI': 0.36267006808713503, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1937.146224603653, 'W': 139.06, 'J_1KI': 67.26669298575086, 'W_1KI': 4.8288075560802834, 'W_D': 102.9745, 'J_D': 1434.4647195847035, 'W_D_1KI': 3.5757517883186334, 'J_D_1KI': 0.12416667089098664}
+[40.24, 39.6, 39.51, 39.42, 39.55, 39.78, 39.64, 39.82, 39.87, 40.57]
+[139.28]
+13.80672812461853
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28675, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.367558717727661, 'TIME_S_1KI': 0.36155392215266474, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1923.0010931968689, 'W': 139.28}
+[40.24, 39.6, 39.51, 39.42, 39.55, 39.78, 39.64, 39.82, 39.87, 40.57, 40.14, 39.5, 39.75, 39.83, 39.43, 39.58, 39.98, 39.51, 40.53, 39.81]
+715.6800000000001
+35.784000000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28675, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.367558717727661, 'TIME_S_1KI': 0.36155392215266474, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1923.0010931968689, 'W': 139.28, 'J_1KI': 67.06193873397974, 'W_1KI': 4.857192676547515, 'W_D': 103.496, 'J_D': 1428.9411339855194, 'W_D_1KI': 3.6092763731473405, 'J_D_1KI': 0.12586840010975905}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json
index 6f0dd43..a3c3be2 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 18401, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.148674011230469, "TIME_S_1KI": 0.5515283958062317, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1912.232966468334, "W": 137.63, "J_1KI": 103.92005687018825, "W_1KI": 7.479484810608119, "W_D": 101.70774999999999, "J_D": 1413.1287691296934, "W_D_1KI": 5.5272947122439, "J_D_1KI": 0.3003801267454975}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19347, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.572320699691772, "TIME_S_1KI": 0.5464578849274706, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2023.443679611683, "W": 136.87, "J_1KI": 104.58694782714028, "W_1KI": 7.07448183180855, "W_D": 100.796, "J_D": 1490.1368388261797, "W_D_1KI": 5.20990334418773, "J_D_1KI": 0.2692874008470424}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output
index 60a702d..6a49bae 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.093505859375}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.0887749195098877}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1476,    2946,  ..., 7497092,
-                            7498499, 7500000]),
-       col_indices=tensor([   3,    4,    9,  ..., 4993, 4995, 4998]),
-       values=tensor([0.7692, 0.9577, 0.6421,  ..., 0.4974, 0.8037, 0.4799]),
+tensor(crow_indices=tensor([      0,    1502,    3027,  ..., 7497008,
+                            7498539, 7500000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4996, 4998, 4999]),
+       values=tensor([0.3598, 0.2894, 0.8829,  ..., 0.3025, 0.7495, 0.3483]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.8552, 0.1634, 0.3191,  ..., 0.0243, 0.9305, 0.7580])
+tensor([0.0129, 0.4104, 0.6159,  ..., 0.6777, 0.7347, 0.9375])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 0.093505859375 seconds
+Time: 0.0887749195098877 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '11229', '-ss', '5000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 6.407301664352417}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '11827', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 6.910944223403931}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1491,    2991,  ..., 7496987,
-                            7498491, 7500000]),
-       col_indices=tensor([   6,    7,   10,  ..., 4987, 4988, 4999]),
-       values=tensor([0.9932, 0.6823, 0.0941,  ..., 0.3170, 0.1700, 0.5277]),
+tensor(crow_indices=tensor([      0,    1504,    3041,  ..., 7497033,
+                            7498540, 7500000]),
+       col_indices=tensor([  11,   12,   13,  ..., 4992, 4996, 4997]),
+       values=tensor([0.6635, 0.8462, 0.1881,  ..., 0.2418, 0.8339, 0.4223]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.7857, 0.3541, 0.7153,  ..., 0.0858, 0.7918, 0.2952])
+tensor([0.1539, 0.9828, 0.7502,  ..., 0.6822, 0.7232, 0.1164])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 6.407301664352417 seconds
+Time: 6.910944223403931 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18401', '-ss', '5000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.148674011230469}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '17969', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.75194501876831}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1524,    2999,  ..., 7496978,
-                            7498500, 7500000]),
-       col_indices=tensor([   0,    1,    3,  ..., 4975, 4979, 4999]),
-       values=tensor([0.7847, 0.2112, 0.1435,  ..., 0.9949, 0.2225, 0.8434]),
+tensor(crow_indices=tensor([      0,    1521,    3049,  ..., 7497131,
+                            7498579, 7500000]),
+       col_indices=tensor([   1,    6,   11,  ..., 4993, 4996, 4998]),
+       values=tensor([0.4767, 0.7925, 0.6152,  ..., 0.5404, 0.2176, 0.3422]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.9436, 0.7820, 0.2976,  ..., 0.7279, 0.8012, 0.5089])
+tensor([0.8753, 0.8267, 0.1281,  ..., 0.9770, 0.8043, 0.2049])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.148674011230469 seconds
+Time: 9.75194501876831 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19347', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.572320699691772}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1524,    2999,  ..., 7496978,
-                            7498500, 7500000]),
-       col_indices=tensor([   0,    1,    3,  ..., 4975, 4979, 4999]),
-       values=tensor([0.7847, 0.2112, 0.1435,  ..., 0.9949, 0.2225, 0.8434]),
+tensor(crow_indices=tensor([      0,    1485,    2948,  ..., 7496987,
+                            7498475, 7500000]),
+       col_indices=tensor([   9,   12,   14,  ..., 4981, 4995, 4998]),
+       values=tensor([0.3812, 0.2057, 0.2607,  ..., 0.9056, 0.6715, 0.4951]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.9436, 0.7820, 0.2976,  ..., 0.7279, 0.8012, 0.5089])
+tensor([0.5119, 0.3228, 0.8802,  ..., 0.4955, 0.2289, 0.1030])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +76,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.148674011230469 seconds
+Time: 10.572320699691772 seconds
 
-[40.88, 40.15, 40.48, 39.7, 39.9, 39.72, 39.73, 39.77, 39.68, 39.5]
-[137.63]
-13.894012689590454
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 18401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.148674011230469, 'TIME_S_1KI': 0.5515283958062317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1912.232966468334, 'W': 137.63}
-[40.88, 40.15, 40.48, 39.7, 39.9, 39.72, 39.73, 39.77, 39.68, 39.5, 40.73, 39.74, 41.44, 39.63, 39.52, 39.59, 39.98, 39.43, 39.67, 39.52]
-718.445
-35.922250000000005
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 18401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.148674011230469, 'TIME_S_1KI': 0.5515283958062317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1912.232966468334, 'W': 137.63, 'J_1KI': 103.92005687018825, 'W_1KI': 7.479484810608119, 'W_D': 101.70774999999999, 'J_D': 1413.1287691296934, 'W_D_1KI': 5.5272947122439, 'J_D_1KI': 0.3003801267454975}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1485,    2948,  ..., 7496987,
+                            7498475, 7500000]),
+       col_indices=tensor([   9,   12,   14,  ..., 4981, 4995, 4998]),
+       values=tensor([0.3812, 0.2057, 0.2607,  ..., 0.9056, 0.6715, 0.4951]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.5119, 0.3228, 0.8802,  ..., 0.4955, 0.2289, 0.1030])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.572320699691772 seconds
+
+[41.41, 39.74, 39.87, 40.98, 39.93, 39.8, 39.75, 40.15, 40.46, 39.9]
+[136.87]
+14.783690214157104
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19347, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.572320699691772, 'TIME_S_1KI': 0.5464578849274706, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2023.443679611683, 'W': 136.87}
+[41.41, 39.74, 39.87, 40.98, 39.93, 39.8, 39.75, 40.15, 40.46, 39.9, 40.45, 39.73, 39.75, 39.94, 39.71, 40.27, 40.08, 40.07, 40.21, 40.32]
+721.48
+36.074
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19347, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.572320699691772, 'TIME_S_1KI': 0.5464578849274706, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2023.443679611683, 'W': 136.87, 'J_1KI': 104.58694782714028, 'W_1KI': 7.07448183180855, 'W_D': 100.796, 'J_D': 1490.1368388261797, 'W_D_1KI': 5.20990334418773, 'J_D_1KI': 0.2692874008470424}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json
index b33500c..70f565b 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4623, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.869210720062256, "TIME_S_1KI": 2.3511163140952314, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1715.391318473816, "W": 123.94000000000001, "J_1KI": 371.0558768059303, "W_1KI": 26.809431105342853, "W_D": 87.73350000000002, "J_D": 1214.2753287019732, "W_D_1KI": 18.977611940298512, "J_D_1KI": 4.105042600107833}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 5031, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 11.223548650741577, "TIME_S_1KI": 2.230878284782663, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1917.9567476463317, "W": 124.24, "J_1KI": 381.2277375564165, "W_1KI": 24.694891671635858, "W_D": 87.814, "J_D": 1355.629860252857, "W_D_1KI": 17.454581594116476, "J_D_1KI": 3.469406001613293}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.output
index 915d5a7..c7e7247 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.2556135654449463}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.2790210247039795}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2026,     4021,  ...,  9995990,
-                             9997978, 10000000]),
-       col_indices=tensor([   1,    6,    9,  ..., 4991, 4993, 4997]),
-       values=tensor([0.3323, 0.6585, 0.3485,  ..., 0.6316, 0.2886, 0.7495]),
+tensor(crow_indices=tensor([       0,     2000,     3996,  ...,  9995976,
+                             9998017, 10000000]),
+       col_indices=tensor([   1,    3,    4,  ..., 4992, 4993, 4994]),
+       values=tensor([0.4344, 0.2681, 0.6913,  ..., 0.2550, 0.4913, 0.3141]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3914, 0.7431, 0.0627,  ..., 0.4218, 0.6007, 0.5832])
+tensor([0.3647, 0.9287, 0.3969,  ..., 0.1409, 0.3592, 0.3689])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 0.2556135654449463 seconds
+Time: 0.2790210247039795 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4107', '-ss', '5000', '-sd', '0.4', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 9.327423810958862}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3763', '-ss', '5000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 8.507438898086548}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2032,     4046,  ...,  9995943,
-                             9997941, 10000000]),
-       col_indices=tensor([   1,    2,    4,  ..., 4988, 4990, 4991]),
-       values=tensor([0.7887, 0.1218, 0.0752,  ..., 0.7697, 0.6176, 0.4928]),
+tensor(crow_indices=tensor([       0,     2023,     4020,  ...,  9995940,
+                             9997949, 10000000]),
+       col_indices=tensor([   1,    2,    4,  ..., 4996, 4997, 4998]),
+       values=tensor([0.1831, 0.0629, 0.7984,  ..., 0.9065, 0.2929, 0.1040]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3655, 0.9637, 0.0803,  ..., 0.0942, 0.4831, 0.3974])
+tensor([0.7862, 0.3853, 0.1330,  ..., 0.7853, 0.2639, 0.4428])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 9.327423810958862 seconds
+Time: 8.507438898086548 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4623', '-ss', '5000', '-sd', '0.4', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.869210720062256}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4644', '-ss', '5000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 9.691264152526855}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2080,     4074,  ...,  9996120,
-                             9998055, 10000000]),
-       col_indices=tensor([   0,    1,    3,  ..., 4988, 4989, 4998]),
-       values=tensor([0.8721, 0.2802, 0.5674,  ..., 0.7807, 0.4474, 0.7441]),
+tensor(crow_indices=tensor([       0,     2009,     4023,  ...,  9996049,
+                             9998045, 10000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4996, 4997, 4999]),
+       values=tensor([0.2930, 0.9632, 0.6423,  ..., 0.4736, 0.0662, 0.9617]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.9197, 0.0161, 0.2580,  ..., 0.5344, 0.2373, 0.6957])
+tensor([0.8864, 0.8642, 0.6244,  ..., 0.5949, 0.5906, 0.1491])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 10.869210720062256 seconds
+Time: 9.691264152526855 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '5031', '-ss', '5000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 11.223548650741577}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2080,     4074,  ...,  9996120,
-                             9998055, 10000000]),
-       col_indices=tensor([   0,    1,    3,  ..., 4988, 4989, 4998]),
-       values=tensor([0.8721, 0.2802, 0.5674,  ..., 0.7807, 0.4474, 0.7441]),
+tensor(crow_indices=tensor([       0,     1956,     4022,  ...,  9995993,
+                             9998029, 10000000]),
+       col_indices=tensor([   6,    8,    9,  ..., 4989, 4993, 4996]),
+       values=tensor([0.9230, 0.5039, 0.8574,  ..., 0.4809, 0.6177, 0.8143]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.9197, 0.0161, 0.2580,  ..., 0.5344, 0.2373, 0.6957])
+tensor([0.6063, 0.9276, 0.9282,  ..., 0.5823, 0.2993, 0.8060])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +76,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 10.869210720062256 seconds
+Time: 11.223548650741577 seconds
 
-[40.45, 40.3, 45.54, 40.13, 39.83, 39.68, 39.66, 40.04, 39.63, 41.25]
-[123.94]
-13.840497970581055
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4623, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.869210720062256, 'TIME_S_1KI': 2.3511163140952314, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1715.391318473816, 'W': 123.94000000000001}
-[40.45, 40.3, 45.54, 40.13, 39.83, 39.68, 39.66, 40.04, 39.63, 41.25, 41.52, 40.34, 39.67, 39.9, 39.74, 39.58, 39.63, 39.49, 39.57, 39.58]
-724.1299999999999
-36.20649999999999
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4623, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.869210720062256, 'TIME_S_1KI': 2.3511163140952314, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1715.391318473816, 'W': 123.94000000000001, 'J_1KI': 371.0558768059303, 'W_1KI': 26.809431105342853, 'W_D': 87.73350000000002, 'J_D': 1214.2753287019732, 'W_D_1KI': 18.977611940298512, 'J_D_1KI': 4.105042600107833}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1956,     4022,  ...,  9995993,
+                             9998029, 10000000]),
+       col_indices=tensor([   6,    8,    9,  ..., 4989, 4993, 4996]),
+       values=tensor([0.9230, 0.5039, 0.8574,  ..., 0.4809, 0.6177, 0.8143]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6063, 0.9276, 0.9282,  ..., 0.5823, 0.2993, 0.8060])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 11.223548650741577 seconds
+
+[41.4, 40.38, 40.49, 39.84, 39.9, 45.5, 40.66, 40.02, 39.82, 39.77]
+[124.24]
+15.437514066696167
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 5031, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 11.223548650741577, 'TIME_S_1KI': 2.230878284782663, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1917.9567476463317, 'W': 124.24}
+[41.4, 40.38, 40.49, 39.84, 39.9, 45.5, 40.66, 40.02, 39.82, 39.77, 40.48, 40.2, 41.05, 40.13, 40.42, 39.85, 39.76, 39.74, 39.96, 39.95]
+728.5200000000001
+36.426
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 5031, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 11.223548650741577, 'TIME_S_1KI': 2.230878284782663, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1917.9567476463317, 'W': 124.24, 'J_1KI': 381.2277375564165, 'W_1KI': 24.694891671635858, 'W_D': 87.814, 'J_D': 1355.629860252857, 'W_D_1KI': 17.454581594116476, 'J_D_1KI': 3.469406001613293}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json
index ef8b561..e95cfa6 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3775, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 11.108771324157715, "TIME_S_1KI": 2.94272088057158, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1948.3000312638283, "W": 122.57, "J_1KI": 516.1059685467094, "W_1KI": 32.46887417218543, "W_D": 86.1335, "J_D": 1369.1270355132817, "W_D_1KI": 22.816821192052977, "J_D_1KI": 6.044191044252445}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3639, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 11.423044204711914, "TIME_S_1KI": 3.1390613368265767, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1913.1958845329284, "W": 122.77, "J_1KI": 525.747701163212, "W_1KI": 33.737290464413306, "W_D": 86.43, "J_D": 1346.888656024933, "W_D_1KI": 23.75103050288541, "J_D_1KI": 6.526801457236991}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output
index 05d0f73..22d5b3a 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.2961087226867676}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.34267544746398926}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2490,     4975,  ..., 12494955,
-                            12497491, 12500000]),
-       col_indices=tensor([   0,    1,    2,  ..., 4994, 4996, 4998]),
-       values=tensor([0.9230, 0.7404, 0.0716,  ..., 0.8209, 0.3183, 0.8676]),
+tensor(crow_indices=tensor([       0,     2521,     4985,  ..., 12494959,
+                            12497459, 12500000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4995, 4998, 4999]),
+       values=tensor([0.3077, 0.5792, 0.1197,  ..., 0.8936, 0.1947, 0.7325]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.4086, 0.9880, 0.1016,  ..., 0.8907, 0.8066, 0.6446])
+tensor([0.4180, 0.8469, 0.8947,  ..., 0.8931, 0.7911, 0.6347])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 0.2961087226867676 seconds
+Time: 0.34267544746398926 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3545', '-ss', '5000', '-sd', '0.5', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.858964443206787}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3064', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 8.839110374450684}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2500,     5017,  ..., 12494975,
-                            12497542, 12500000]),
-       col_indices=tensor([   0,    1,    2,  ..., 4994, 4997, 4999]),
-       values=tensor([0.5138, 0.3202, 0.6371,  ..., 0.0572, 0.7854, 0.0609]),
+tensor(crow_indices=tensor([       0,     2441,     4940,  ..., 12494998,
+                            12497446, 12500000]),
+       col_indices=tensor([   2,    3,    5,  ..., 4995, 4998, 4999]),
+       values=tensor([0.9162, 0.0160, 0.4149,  ..., 0.8237, 0.2759, 0.8628]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.5537, 0.0044, 0.4461,  ..., 0.4637, 0.2205, 0.0434])
+tensor([0.9573, 0.7850, 0.3646,  ..., 0.3146, 0.0155, 0.4417])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 9.858964443206787 seconds
+Time: 8.839110374450684 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3775', '-ss', '5000', '-sd', '0.5', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 11.108771324157715}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3639', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 11.423044204711914}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2457,     4970,  ..., 12495118,
-                            12497586, 12500000]),
-       col_indices=tensor([   0,    5,    6,  ..., 4996, 4997, 4999]),
-       values=tensor([0.5982, 0.0244, 0.5821,  ..., 0.2791, 0.2569, 0.9852]),
+tensor(crow_indices=tensor([       0,     2446,     4933,  ..., 12494981,
+                            12497497, 12500000]),
+       col_indices=tensor([   2,    3,    4,  ..., 4994, 4997, 4999]),
+       values=tensor([0.9027, 0.0284, 0.4936,  ..., 0.0666, 0.4136, 0.7094]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.1130, 0.4034, 0.5297,  ..., 0.0598, 0.7079, 0.8853])
+tensor([0.5919, 0.5153, 0.6363,  ..., 0.8884, 0.6968, 0.4432])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 11.108771324157715 seconds
+Time: 11.423044204711914 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2457,     4970,  ..., 12495118,
-                            12497586, 12500000]),
-       col_indices=tensor([   0,    5,    6,  ..., 4996, 4997, 4999]),
-       values=tensor([0.5982, 0.0244, 0.5821,  ..., 0.2791, 0.2569, 0.9852]),
+tensor(crow_indices=tensor([       0,     2446,     4933,  ..., 12494981,
+                            12497497, 12500000]),
+       col_indices=tensor([   2,    3,    4,  ..., 4994, 4997, 4999]),
+       values=tensor([0.9027, 0.0284, 0.4936,  ..., 0.0666, 0.4136, 0.7094]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.1130, 0.4034, 0.5297,  ..., 0.0598, 0.7079, 0.8853])
+tensor([0.5919, 0.5153, 0.6363,  ..., 0.8884, 0.6968, 0.4432])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 11.108771324157715 seconds
+Time: 11.423044204711914 seconds
 
-[40.6, 40.02, 45.54, 40.47, 40.31, 40.18, 40.34, 40.25, 40.23, 39.68]
-[122.57]
-15.89540696144104
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3775, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 11.108771324157715, 'TIME_S_1KI': 2.94272088057158, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1948.3000312638283, 'W': 122.57}
-[40.6, 40.02, 45.54, 40.47, 40.31, 40.18, 40.34, 40.25, 40.23, 39.68, 40.3, 40.16, 40.89, 40.3, 40.11, 40.08, 40.03, 39.96, 39.79, 39.56]
-728.73
-36.4365
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3775, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 11.108771324157715, 'TIME_S_1KI': 2.94272088057158, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1948.3000312638283, 'W': 122.57, 'J_1KI': 516.1059685467094, 'W_1KI': 32.46887417218543, 'W_D': 86.1335, 'J_D': 1369.1270355132817, 'W_D_1KI': 22.816821192052977, 'J_D_1KI': 6.044191044252445}
+[41.72, 39.81, 39.85, 39.78, 45.53, 39.71, 40.25, 40.38, 39.72, 39.79]
+[122.77]
+15.583578109741211
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3639, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 11.423044204711914, 'TIME_S_1KI': 3.1390613368265767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1913.1958845329284, 'W': 122.77}
+[41.72, 39.81, 39.85, 39.78, 45.53, 39.71, 40.25, 40.38, 39.72, 39.79, 40.44, 39.88, 39.71, 39.85, 40.63, 41.4, 39.84, 39.98, 39.72, 39.57]
+726.8
+36.339999999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3639, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 11.423044204711914, 'TIME_S_1KI': 3.1390613368265767, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1913.1958845329284, 'W': 122.77, 'J_1KI': 525.747701163212, 'W_1KI': 33.737290464413306, 'W_D': 86.43, 'J_D': 1346.888656024933, 'W_D_1KI': 23.75103050288541, 'J_D_1KI': 6.526801457236991}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.json
index aad5720..9c857d9 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 475914, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.308423519134521, "TIME_S_1KI": 0.021660265340239036, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1189.9414702892302, "W": 94.85, "J_1KI": 2.5003287784961783, "W_1KI": 0.19930071399454524, "W_D": 59.248999999999995, "J_D": 743.3088262853622, "W_D_1KI": 0.12449518190261265, "J_D_1KI": 0.000261591762172604}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 490047, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.476571798324585, "TIME_S_1KI": 0.021378708161308168, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1229.4466451644898, "W": 95.2, "J_1KI": 2.50883414277506, "W_1KI": 0.19426708050452304, "W_D": 58.61600000000001, "J_D": 756.9878629512788, "W_D_1KI": 0.11961301671064206, "J_D_1KI": 0.00024408478515457101}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.output
index c0883dc..61661ae 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,75 +1,102 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.04127812385559082}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.018640756607055664}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([ 980, 1760, 2093,  785, 4671, 1948,  723, 1459, 1601,
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-       values=tensor([0.9726, 0.4932, 0.3912, 0.9738, 0.2382, 0.2194, 0.1583,
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-       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.4444, 0.7904, 0.6664,  ..., 0.7984, 0.5487, 0.5407])
+       col_indices=tensor([2210, 2525, 4536,  254, 1226, 2449,  366, 1857,  151,
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+                           1640, 2730, 1779, 3267,  430, 3515,  708, 4874,  633,
+                           1717, 3769, 2802, 4336, 3129, 3620,  550]),
+       values=tensor([7.5476e-01, 9.9584e-01, 3.6944e-01, 2.4679e-01,
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+                      2.4573e-01, 7.6478e-01, 1.7824e-02, 6.6916e-01,
+                      4.9662e-01, 8.3600e-01, 8.6759e-01, 2.9561e-01,
+                      2.9478e-01, 8.4606e-01, 9.1135e-01, 1.7097e-01,
+                      9.2858e-01, 8.2406e-01, 1.2371e-01, 7.0161e-01,
+                      8.4881e-01, 3.4036e-01, 9.0372e-01, 6.9052e-01,
+                      1.6420e-01, 2.3091e-02, 4.1037e-01, 1.6786e-01,
+                      2.5768e-01, 2.9182e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.9379, 0.1980, 0.4828,  ..., 0.6873, 0.0867, 0.0276])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -77,80 +104,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.04127812385559082 seconds
+Time: 0.018640756607055664 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '25437', '-ss', '5000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.5612115859985352}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '56328', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.2069127559661865}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([4513, 4874, 3027,  172, 3763, 1079,  239, 4529, 4955,
-                            608, 1066, 2737, 4445, 4271, 3658,  687, 3210, 2517,
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+                      0.1631, 0.0795, 0.4559, 0.7522, 0.3620]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.8285, 0.4420, 0.7749,  ..., 0.7287, 0.4578, 0.9435])
+tensor([0.6507, 0.8665, 0.7088,  ..., 0.1660, 0.7141, 0.7532])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -158,80 +185,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.5612115859985352 seconds
+Time: 1.2069127559661865 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '475914', '-ss', '5000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.308423519134521}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '490047', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.476571798324585}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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+                      0.7885, 0.6327, 0.3555, 0.8037, 0.0679, 0.3358, 0.4488,
+                      0.2846, 0.4860, 0.3112, 0.9987, 0.3595, 0.5644, 0.7766,
+                      0.8640, 0.9246, 0.3503, 0.1256, 0.5337, 0.1297, 0.5515,
+                      0.6635, 0.5154, 0.0632, 0.7639, 0.9128, 0.6392, 0.8302,
+                      0.4439, 0.2348, 0.2796, 0.1102, 0.8665, 0.0654, 0.2969,
+                      0.6779, 0.5154, 0.8782, 0.6349, 0.2534, 0.1900, 0.1089,
+                      0.2132, 0.1606, 0.4838, 0.5131, 0.2693, 0.2911, 0.3925,
+                      0.1298, 0.2734, 0.2859, 0.2402, 0.7284, 0.6809, 0.1017,
+                      0.5006, 0.7387, 0.5250, 0.6054, 0.3207, 0.8317, 0.0063,
+                      0.8124, 0.1682, 0.9358, 0.7971, 0.6026, 0.8063, 0.0277,
+                      0.9101, 0.2706, 0.6666, 0.9401, 0.9468, 0.6874, 0.1204,
+                      0.1461, 0.6930, 0.8474, 0.3668, 0.2632, 0.3152, 0.9470,
+                      0.1754, 0.9084, 0.1945, 0.9896, 0.9045, 0.7615, 0.9816,
+                      0.3493, 0.9428, 0.7718, 0.8234, 0.0894, 0.6485, 0.0109,
+                      0.8460, 0.6278, 0.2100, 0.7409, 0.3213, 0.8051, 0.7234,
+                      0.6970, 0.4116, 0.6352, 0.0029, 0.6761, 0.6077, 0.8527,
+                      0.5089, 0.6432, 0.8485, 0.4372, 0.2321, 0.3474, 0.5371,
+                      0.5527, 0.4008, 0.4177, 0.1217, 0.7348, 0.6489, 0.0080,
+                      0.9129, 0.6431, 0.1973, 0.6075, 0.8143, 0.0232, 0.3884,
+                      0.5022, 0.1042, 0.6880, 0.6811, 0.2932, 0.7890, 0.6577,
+                      0.7526, 0.0327, 0.3684, 0.3929, 0.5193, 0.6389, 0.8937,
+                      0.2414, 0.4405, 0.1415, 0.9714, 0.7709, 0.7504, 0.6900,
+                      0.1775, 0.9758, 0.6293, 0.6471, 0.1301, 0.8606, 0.1224,
+                      0.5565, 0.3881, 0.8292, 0.5099, 0.9753, 0.4458, 0.1070,
+                      0.9912, 0.6060, 0.3669, 0.5306, 0.4360, 0.7428, 0.4271,
+                      0.8779, 0.6385, 0.2427, 0.9170, 0.1454, 0.1927, 0.3375,
+                      0.3438, 0.8555, 0.8565, 0.6394, 0.2035]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.4130, 0.3379, 0.7498,  ..., 0.0848, 0.9618, 0.5893])
+tensor([0.4641, 0.6633, 0.6004,  ..., 0.4285, 0.2216, 0.5300])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -239,77 +266,77 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.308423519134521 seconds
+Time: 10.476571798324585 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([ 698, 3054, 4592, 1629, 4601, 3886, 4804,  318, 1415,
-                            433, 1872, 1429, 1550, 3511, 4304, 3637, 1101, 2710,
-                           4079,  541, 1194,   97, 2807, 2811, 3206, 3991, 2286,
-                           2681, 4835,   38, 1361, 1702, 1987, 1831,  485,  140,
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-                           1360, 4727, 3879,  832,  240, 2132, 2582, 1372, 4190,
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-                           3610, 3188, 2899, 3261, 1055, 1112, 4180, 1426, 3909,
-                           3409, 4510, 3025,  703, 1794,  225,  659, 2212, 1407,
-                           4739, 1542, 2238, 2858, 4535, 4405, 3841, 3716, 4156,
-                           4416, 2641, 4372, 1051,  980, 3180, 2782,  497,  394,
-                            605,  971,  455, 3831,  523, 3209,  733, 4726, 2765,
-                            226, 3470, 1720, 2299, 2372, 1447, 3202, 1153, 3498,
-                           4698, 2998, 1466,  363, 4324, 2506, 2090, 2285, 2204,
-                            303, 4406, 1500, 1826, 4080, 1378, 1816,  980,   66,
-                           4392, 4266, 2875,  275, 1828, 3108,  989, 3152, 2508,
-                           1550, 2008, 1066,  529,   18,    1, 3369, 4893, 2556,
-                           1068, 1671, 1086, 2667, 3511, 2891, 1749, 4140, 3150,
-                             43, 1921, 1837,  980, 1293, 1802, 2390,  249,  768,
-                           4105, 1721, 1435, 4658, 2745, 4338, 2320, 3879, 2641,
-                           3097, 1585, 3887, 4913, 4556, 2794, 2705,  299,   61,
-                           2384, 1270,  740, 2129, 3392, 3774, 4644, 4192, 4506,
-                            149, 4748, 3571, 1159, 4587, 1920,  982, 2347, 1650,
-                           1882, 1955, 3910,  197, 4484, 3655, 4387,  968, 3452,
-                            181, 2166, 1855, 2452,  189, 3074, 2522, 3426,  135,
-                           1267,  666, 4928, 2908, 4053, 4593,  448]),
-       values=tensor([0.8620, 0.7365, 0.7531, 0.5477, 0.5295, 0.4268, 0.2903,
-                      0.3296, 0.4852, 0.0295, 0.6605, 0.4770, 0.1707, 0.4638,
-                      0.1229, 0.6813, 0.2237, 0.9317, 0.9546, 0.4741, 0.7915,
-                      0.3909, 0.2549, 0.7853, 0.8207, 0.9924, 0.8328, 0.0293,
-                      0.3281, 0.4028, 0.9335, 0.8141, 0.3687, 0.4243, 0.5386,
-                      0.2123, 0.0695, 0.2792, 0.2453, 0.4935, 0.1675, 0.4387,
-                      0.5777, 0.6384, 0.5870, 0.2050, 0.9519, 0.0161, 0.0462,
-                      0.8312, 0.5114, 0.5703, 0.5170, 0.0110, 0.6229, 0.7339,
-                      0.2337, 0.7709, 0.7844, 0.2062, 0.2004, 0.9990, 0.4625,
-                      0.4209, 0.7064, 0.0680, 0.6043, 0.0073, 0.1383, 0.5359,
-                      0.1641, 0.0316, 0.0479, 0.9788, 0.0764, 0.0936, 0.1603,
-                      0.1581, 0.8855, 0.4285, 0.9101, 0.6054, 0.5164, 0.1839,
-                      0.2783, 0.0513, 0.4451, 0.7375, 0.3333, 0.1348, 0.3539,
-                      0.0102, 0.1620, 0.4960, 0.1201, 0.8615, 0.2151, 0.0085,
-                      0.8133, 0.8439, 0.5713, 0.6595, 0.6728, 0.2738, 0.1487,
-                      0.3205, 0.6933, 0.0963, 0.6731, 0.6903, 0.0043, 0.7900,
-                      0.7911, 0.9496, 0.4295, 0.5758, 0.2659, 0.3025, 0.6145,
-                      0.1511, 0.7265, 0.9480, 0.6751, 0.8138, 0.6361, 0.4149,
-                      0.8899, 0.3218, 0.3413, 0.2054, 0.1555, 0.4398, 0.9946,
-                      0.6820, 0.1566, 0.7238, 0.9562, 0.1023, 0.0696, 0.9724,
-                      0.8182, 0.0031, 0.0289, 0.5187, 0.0063, 0.5262, 0.9232,
-                      0.3694, 0.0136, 0.3019, 0.9633, 0.8770, 0.0826, 0.1792,
-                      0.6372, 0.5719, 0.7979, 0.1369, 0.9923, 0.7514, 0.0627,
-                      0.3337, 0.0132, 0.9026, 0.1169, 0.4065, 0.7302, 0.3087,
-                      0.4276, 0.6874, 0.0705, 0.4727, 0.3286, 0.7188, 0.3727,
-                      0.5310, 0.1979, 0.9773, 0.3076, 0.3372, 0.2546, 0.3340,
-                      0.4532, 0.0609, 0.2279, 0.8651, 0.8162, 0.8251, 0.1216,
-                      0.3049, 0.0805, 0.1284, 0.1859, 0.3690, 0.3435, 0.7762,
-                      0.7083, 0.6529, 0.8556, 0.1421, 0.4528, 0.4045, 0.9221,
-                      0.6914, 0.6437, 0.8815, 0.0609, 0.9680, 0.2115, 0.5295,
-                      0.5418, 0.8646, 0.6735, 0.1927, 0.2578, 0.4564, 0.0603,
-                      0.1414, 0.3382, 0.0772, 0.6503, 0.3586, 0.8775, 0.8840,
-                      0.9215, 0.4825, 0.2733, 0.0423, 0.6825, 0.8144, 0.0837,
-                      0.2758, 0.6188, 0.3276, 0.0762, 0.7932, 0.5621, 0.9067,
-                      0.7339, 0.1976, 0.8462, 0.5736, 0.2659, 0.7486, 0.3053,
-                      0.7429, 0.4272, 0.8072, 0.5183, 0.4677]),
+       col_indices=tensor([2879, 2548, 4235, 2177, 1968, 4792, 2118, 3824, 3830,
+                            419, 2935,  858, 3221, 1158, 3295, 4115, 4212, 4937,
+                           4105, 1905,  430, 1434, 4104,  172, 3831,  777, 1804,
+                           1906, 3629, 4755, 3973, 3704,  574, 3092,  296, 4473,
+                            178, 1394,  364, 1101, 1225,   72, 2310, 4099, 4749,
+                           4433, 3811, 1052, 4063,  946, 4833, 3504, 2256, 3467,
+                            380, 2912, 3726, 4501, 3719, 1032, 1375, 4002,  185,
+                           3479, 2598, 4766, 3328,  793, 1360, 4472,  484, 3670,
+                            533, 4829, 1260, 1285, 2295,  963, 1454, 4716, 3791,
+                           4404, 1941, 4961, 4734, 3669, 4605,  847, 1484, 3477,
+                            947, 3738,  664, 4986, 4204, 3808, 3474, 2661, 3309,
+                           3369,  153,  935, 2927, 3740,  791, 4992,  711, 2043,
+                            194, 4833,   79, 4635,  839,  299,  575, 1530, 2233,
+                           4374, 1754, 4418, 1039, 4935, 1345, 3862, 3955,  777,
+                           3857, 4284, 1102, 3982, 3631, 1213,  324, 1586, 2281,
+                           4410, 4374, 2992, 3918, 2289, 1015, 2745, 4492,  594,
+                           4664, 3401, 2974, 3118, 4743, 4680, 4438, 2693, 4451,
+                           1606, 3511, 4742, 2749, 2647, 1217,  405,  537, 1399,
+                            199, 4498, 2424,  286,  693,  270,  889, 2160, 4138,
+                           3608, 1489, 2742, 4553, 2358, 2877, 4564, 3315, 4926,
+                           4195, 4940, 4664,  172, 3985,  745,    3, 3830, 4058,
+                           2581,  776,  405,  898, 2791, 3728, 3234, 1387, 4346,
+                            295, 3823, 1053, 2346, 4176, 1488,  434, 1655, 1692,
+                           2590, 3984, 3595,  999, 1056, 2826, 3138, 3494, 2904,
+                           1058, 2210, 3296, 2486, 2896,  138, 3985, 1060, 2585,
+                           4071, 3584,  758, 4715, 2930, 1732, 1000, 3747, 4019,
+                           1473, 2790, 3984, 2969,  781,  585,  781, 4422,  445,
+                           2047, 2372, 1079, 2925,  955,  284,  951]),
+       values=tensor([0.2472, 0.6408, 0.2221, 0.5181, 0.6350, 0.9033, 0.8956,
+                      0.7487, 0.8455, 0.1032, 0.8217, 0.9140, 0.5672, 0.0509,
+                      0.3161, 0.8507, 0.6291, 0.4677, 0.4913, 0.1587, 0.9565,
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+                      0.2069, 0.0097, 0.3167, 0.5484, 0.6282, 0.4620, 0.0987,
+                      0.6949, 0.7304, 0.3076, 0.4382, 0.8483, 0.5431, 0.3061,
+                      0.7208, 0.7528, 0.0727, 0.1586, 0.4223, 0.7512, 0.1050,
+                      0.2044, 0.1480, 0.8843, 0.6474, 0.9923, 0.4300, 0.7404,
+                      0.8099, 0.0456, 0.9256, 0.2564, 0.9005, 0.6348, 0.2339,
+                      0.7885, 0.6327, 0.3555, 0.8037, 0.0679, 0.3358, 0.4488,
+                      0.2846, 0.4860, 0.3112, 0.9987, 0.3595, 0.5644, 0.7766,
+                      0.8640, 0.9246, 0.3503, 0.1256, 0.5337, 0.1297, 0.5515,
+                      0.6635, 0.5154, 0.0632, 0.7639, 0.9128, 0.6392, 0.8302,
+                      0.4439, 0.2348, 0.2796, 0.1102, 0.8665, 0.0654, 0.2969,
+                      0.6779, 0.5154, 0.8782, 0.6349, 0.2534, 0.1900, 0.1089,
+                      0.2132, 0.1606, 0.4838, 0.5131, 0.2693, 0.2911, 0.3925,
+                      0.1298, 0.2734, 0.2859, 0.2402, 0.7284, 0.6809, 0.1017,
+                      0.5006, 0.7387, 0.5250, 0.6054, 0.3207, 0.8317, 0.0063,
+                      0.8124, 0.1682, 0.9358, 0.7971, 0.6026, 0.8063, 0.0277,
+                      0.9101, 0.2706, 0.6666, 0.9401, 0.9468, 0.6874, 0.1204,
+                      0.1461, 0.6930, 0.8474, 0.3668, 0.2632, 0.3152, 0.9470,
+                      0.1754, 0.9084, 0.1945, 0.9896, 0.9045, 0.7615, 0.9816,
+                      0.3493, 0.9428, 0.7718, 0.8234, 0.0894, 0.6485, 0.0109,
+                      0.8460, 0.6278, 0.2100, 0.7409, 0.3213, 0.8051, 0.7234,
+                      0.6970, 0.4116, 0.6352, 0.0029, 0.6761, 0.6077, 0.8527,
+                      0.5089, 0.6432, 0.8485, 0.4372, 0.2321, 0.3474, 0.5371,
+                      0.5527, 0.4008, 0.4177, 0.1217, 0.7348, 0.6489, 0.0080,
+                      0.9129, 0.6431, 0.1973, 0.6075, 0.8143, 0.0232, 0.3884,
+                      0.5022, 0.1042, 0.6880, 0.6811, 0.2932, 0.7890, 0.6577,
+                      0.7526, 0.0327, 0.3684, 0.3929, 0.5193, 0.6389, 0.8937,
+                      0.2414, 0.4405, 0.1415, 0.9714, 0.7709, 0.7504, 0.6900,
+                      0.1775, 0.9758, 0.6293, 0.6471, 0.1301, 0.8606, 0.1224,
+                      0.5565, 0.3881, 0.8292, 0.5099, 0.9753, 0.4458, 0.1070,
+                      0.9912, 0.6060, 0.3669, 0.5306, 0.4360, 0.7428, 0.4271,
+                      0.8779, 0.6385, 0.2427, 0.9170, 0.1454, 0.1927, 0.3375,
+                      0.3438, 0.8555, 0.8565, 0.6394, 0.2035]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.4130, 0.3379, 0.7498,  ..., 0.0848, 0.9618, 0.5893])
+tensor([0.4641, 0.6633, 0.6004,  ..., 0.4285, 0.2216, 0.5300])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -317,13 +344,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.308423519134521 seconds
+Time: 10.476571798324585 seconds
 
-[39.69, 38.64, 38.76, 38.66, 38.74, 43.79, 39.13, 39.04, 39.27, 38.98]
-[94.85]
-12.54550838470459
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 475914, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.308423519134521, 'TIME_S_1KI': 0.021660265340239036, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.9414702892302, 'W': 94.85}
-[39.69, 38.64, 38.76, 38.66, 38.74, 43.79, 39.13, 39.04, 39.27, 38.98, 39.59, 39.06, 44.18, 38.71, 38.85, 39.28, 38.92, 38.81, 38.79, 40.52]
-712.02
-35.601
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 475914, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.308423519134521, 'TIME_S_1KI': 0.021660265340239036, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.9414702892302, 'W': 94.85, 'J_1KI': 2.5003287784961783, 'W_1KI': 0.19930071399454524, 'W_D': 59.248999999999995, 'J_D': 743.3088262853622, 'W_D_1KI': 0.12449518190261265, 'J_D_1KI': 0.000261591762172604}
+[41.03, 38.98, 39.26, 39.42, 39.05, 48.27, 44.68, 38.99, 43.85, 39.12]
+[95.2]
+12.914355516433716
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 490047, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.476571798324585, 'TIME_S_1KI': 0.021378708161308168, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1229.4466451644898, 'W': 95.2}
+[41.03, 38.98, 39.26, 39.42, 39.05, 48.27, 44.68, 38.99, 43.85, 39.12, 41.47, 39.11, 39.34, 39.38, 39.11, 44.02, 39.04, 39.8, 39.1, 38.94]
+731.68
+36.583999999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 490047, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.476571798324585, 'TIME_S_1KI': 0.021378708161308168, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1229.4466451644898, 'W': 95.2, 'J_1KI': 2.50883414277506, 'W_1KI': 0.19426708050452304, 'W_D': 58.61600000000001, 'J_D': 756.9878629512788, 'W_D_1KI': 0.11961301671064206, 'J_D_1KI': 0.00024408478515457101}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.json
index ae25247..257240e 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.json
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 463602, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.329545974731445, "TIME_S_1KI": 0.02228106430673605, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1198.2176897239685, "W": 95.11, "J_1KI": 2.5845826586683587, "W_1KI": 0.20515442124926123, "W_D": 59.724, "J_D": 752.4167101364135, "W_D_1KI": 0.12882601886963388, "J_D_1KI": 0.00027788063655815524}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 456925, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.096729040145874, "TIME_S_1KI": 0.022097125436660005, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1218.1025603413582, "W": 95.69, "J_1KI": 2.665869804325345, "W_1KI": 0.20942167751819227, "W_D": 60.22, "J_D": 766.5810030698776, "W_D_1KI": 0.13179405810581604, "J_D_1KI": 0.0002884369603453872}
diff --git a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.output
index 3c48d35..c94353e 100644
--- a/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.output
+++ b/pytorch/output_synthetic_16core/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.output
@@ -1,51 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.01933002471923828}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([ 393, 4092, 1605,  ..., 4543,  205, 1898]),
-       values=tensor([0.0363, 0.1593, 0.8850,  ..., 0.0884, 0.4054, 0.0261]),
-       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.7170, 0.2316, 0.8921,  ..., 0.0306, 0.1187, 0.4918])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 1250
-Density: 5e-05
-Time: 0.01933002471923828 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '54319', '-ss', '5000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.230255126953125}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([4735, 1903, 2985,  ..., 3889, 4420, 4686]),
-       values=tensor([0.8501, 0.7899, 0.6223,  ..., 0.9437, 0.2014, 0.9727]),
-       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.5741, 0.3449, 0.6519,  ..., 0.7953, 0.3519, 0.0286])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 1250
-Density: 5e-05
-Time: 1.230255126953125 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '463602', '-ss', '5000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.329545974731445}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.019748687744140625}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1248, 1249, 1250]),
-       col_indices=tensor([1727, 4803, 1040,  ..., 3710, 1053, 4648]),
-       values=tensor([0.0640, 0.8338, 0.2393,  ..., 0.0278, 0.9877, 0.3687]),
+       col_indices=tensor([1288,  418, 1217,  ..., 3615, 1050, 3739]),
+       values=tensor([0.1525, 0.4681, 0.7924,  ..., 0.1705, 0.9120, 0.8137]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.8593, 0.3881, 0.7226,  ..., 0.2122, 0.1433, 0.4534])
+tensor([0.6959, 0.5396, 0.0275,  ..., 0.0373, 0.0563, 0.7436])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.329545974731445 seconds
+Time: 0.019748687744140625 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '53168', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.2217841148376465}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1248, 1249, 1250]),
-       col_indices=tensor([1727, 4803, 1040,  ..., 3710, 1053, 4648]),
-       values=tensor([0.0640, 0.8338, 0.2393,  ..., 0.0278, 0.9877, 0.3687]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([1720,  308,  494,  ...,  419, 4959, 3638]),
+       values=tensor([0.7424, 0.0997, 0.6943,  ..., 0.3613, 0.1237, 0.5099]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.8593, 0.3881, 0.7226,  ..., 0.2122, 0.1433, 0.4534])
+tensor([0.4725, 0.9350, 0.5353,  ..., 0.3161, 0.1856, 0.9919])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +34,48 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.329545974731445 seconds
+Time: 1.2217841148376465 seconds
 
-[40.79, 39.42, 39.16, 38.83, 39.21, 39.28, 39.68, 38.8, 39.35, 39.35]
-[95.11]
-12.598230361938477
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 463602, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.329545974731445, 'TIME_S_1KI': 0.02228106430673605, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1198.2176897239685, 'W': 95.11}
-[40.79, 39.42, 39.16, 38.83, 39.21, 39.28, 39.68, 38.8, 39.35, 39.35, 40.35, 38.9, 38.98, 39.04, 38.98, 40.01, 39.46, 39.09, 39.7, 39.17]
-707.72
-35.386
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 463602, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.329545974731445, 'TIME_S_1KI': 0.02228106430673605, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1198.2176897239685, 'W': 95.11, 'J_1KI': 2.5845826586683587, 'W_1KI': 0.20515442124926123, 'W_D': 59.724, 'J_D': 752.4167101364135, 'W_D_1KI': 0.12882601886963388, 'J_D_1KI': 0.00027788063655815524}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '456925', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.096729040145874}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([1672,  172, 4731,  ..., 3430, 2179, 1580]),
+       values=tensor([0.3560, 0.5737, 0.3085,  ..., 0.2088, 0.5679, 0.6187]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.9752, 0.9948, 0.1696,  ..., 0.2234, 0.5842, 0.4674])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.096729040145874 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([1672,  172, 4731,  ..., 3430, 2179, 1580]),
+       values=tensor([0.3560, 0.5737, 0.3085,  ..., 0.2088, 0.5679, 0.6187]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.9752, 0.9948, 0.1696,  ..., 0.2234, 0.5842, 0.4674])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.096729040145874 seconds
+
+[40.16, 39.39, 39.13, 40.27, 39.54, 39.09, 39.14, 39.03, 40.02, 39.45]
+[95.69]
+12.729674577713013
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 456925, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.096729040145874, 'TIME_S_1KI': 0.022097125436660005, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1218.1025603413582, 'W': 95.69}
+[40.16, 39.39, 39.13, 40.27, 39.54, 39.09, 39.14, 39.03, 40.02, 39.45, 40.28, 39.58, 39.55, 39.03, 39.15, 39.03, 39.41, 39.02, 39.21, 39.73]
+709.4
+35.47
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 456925, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.096729040145874, 'TIME_S_1KI': 0.022097125436660005, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1218.1025603413582, 'W': 95.69, 'J_1KI': 2.665869804325345, 'W_1KI': 0.20942167751819227, 'W_D': 60.22, 'J_D': 766.5810030698776, 'W_D_1KI': 0.13179405810581604, 'J_D_1KI': 0.0002884369603453872}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..622b9fa
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 276, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.459847450256348, "TIME_S_1KI": 37.89799800817517, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 817.4248892712593, "W": 57.93, "J_1KI": 2961.6843814176063, "W_1KI": 209.89130434782606, "W_D": 41.04475, "J_D": 579.1645127553344, "W_D_1KI": 148.7128623188406, "J_D_1KI": 538.8147185465239}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..57968d5
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 3.7949230670928955}
+
+tensor(indices=tensor([[  259, 92406, 67214,  ..., 56126, 49758, 28077],
+                       [18855, 67153, 56435,  ..., 20353, 29983, 42584]]),
+       values=tensor([0.3156, 0.0350, 0.7564,  ..., 0.8554, 0.7583, 0.2188]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.1576, 0.3481, 0.0886,  ..., 0.2032, 0.6260, 0.4921])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 3.7949230670928955 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '276', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.459847450256348}
+
+tensor(indices=tensor([[19637, 46545, 80696,  ..., 21067, 33035, 70657],
+                       [76098, 99632, 25388,  ..., 15886, 74696, 29574]]),
+       values=tensor([0.9176, 0.5660, 0.0023,  ..., 0.9938, 0.3471, 0.0114]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.2242, 0.3499, 0.5367,  ..., 0.5506, 0.8782, 0.6678])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.459847450256348 seconds
+
+tensor(indices=tensor([[19637, 46545, 80696,  ..., 21067, 33035, 70657],
+                       [76098, 99632, 25388,  ..., 15886, 74696, 29574]]),
+       values=tensor([0.9176, 0.5660, 0.0023,  ..., 0.9938, 0.3471, 0.0114]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.2242, 0.3499, 0.5367,  ..., 0.5506, 0.8782, 0.6678])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.459847450256348 seconds
+
+[19.16, 19.05, 18.6, 18.49, 19.68, 18.59, 18.59, 18.46, 18.74, 18.83]
+[57.93]
+14.110562562942505
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 276, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.459847450256348, 'TIME_S_1KI': 37.89799800817517, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 817.4248892712593, 'W': 57.93}
+[19.16, 19.05, 18.6, 18.49, 19.68, 18.59, 18.59, 18.46, 18.74, 18.83, 19.21, 18.6, 18.67, 18.46, 18.59, 18.45, 18.54, 18.66, 19.59, 18.69]
+337.705
+16.88525
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 276, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.459847450256348, 'TIME_S_1KI': 37.89799800817517, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 817.4248892712593, 'W': 57.93, 'J_1KI': 2961.6843814176063, 'W_1KI': 209.89130434782606, 'W_D': 41.04475, 'J_D': 579.1645127553344, 'W_D_1KI': 148.7128623188406, 'J_D_1KI': 538.8147185465239}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..4bef977
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..37a66db
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.001.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '0.001', '-c', '16']
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+tensor(indices=tensor([[88148, 50636, 12278,  ..., 44395, 13251, 88413],
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..ede5e39
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 412.5398921966553, "TIME_S_1KI": 4125.398921966553, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 24198.670872001647, "W": 53.279999999999994, "J_1KI": 241986.70872001647, "W_1KI": 532.8, "W_D": 36.422999999999995, "J_D": 16542.571118072745, "W_D_1KI": 364.22999999999996, "J_D_1KI": 3642.2999999999997}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..a0dbec0
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_0.01.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '0.01', '-c', '16']
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+                       [74739, 76364, 52147,  ...,  6055, 76752, 19403]]),
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..8b20250
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2194, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.215775489807129, "TIME_S_1KI": 4.656233131179184, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1111.5062367582323, "W": 81.18, "J_1KI": 506.6117760976446, "W_1KI": 37.00091157702826, "W_D": 64.4195, "J_D": 882.0236021045446, "W_D_1KI": 29.36166818596171, "J_D_1KI": 13.382711114841255}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..a236b77
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '1e-05', '-c', '16']
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+       values=tensor([0.3522, 0.6796, 0.7423,  ..., 0.3629, 0.1983, 0.1916]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_coo)
+tensor([0.4184, 0.8527, 0.3195,  ..., 0.6891, 0.6136, 0.5192])
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+Shape: torch.Size([100000, 100000])
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..33e4c22
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 538, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.497490882873535, "TIME_S_1KI": 19.512064838054897, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 869.9857006072998, "W": 61.94, "J_1KI": 1617.0737929503714, "W_1KI": 115.13011152416357, "W_D": 45.07325, "J_D": 633.0817400693894, "W_D_1KI": 83.77927509293681, "J_D_1KI": 155.72355965229892}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..8e04606
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '5e-05', '-c', '16']
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..3612954
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.0001.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 29372, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.55020022392273, "TIME_S_1KI": 0.35919243578655624, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 759.2383503341675, "W": 54.24, "J_1KI": 25.84905182943509, "W_1KI": 1.846656679831132, "W_D": 37.11625, "J_D": 519.544255541861, "W_D_1KI": 1.263660969630941, "J_D_1KI": 0.04302263957615896}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..63fd63a
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.0001.output
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..341fad9
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.001.json
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..c48a950
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.001.output
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..bd2079d
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.01.json
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..921bf5b
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.01.output
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..6867b72
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.05.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 17.546292066574097, "TIME_S_1KI": 175.46292066574097, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1213.4392498111724, "W": 54.29, "J_1KI": 12134.392498111725, "W_1KI": 542.9, "W_D": 37.43899999999999, "J_D": 836.801474925041, "W_D_1KI": 374.38999999999993, "J_D_1KI": 3743.899999999999}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..12cc0f8
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 17.546292066574097}
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+                       [5895, 6102, 1680,  ..., 1426,  873, 1922]]),
+       values=tensor([0.7534, 0.5438, 0.6082,  ..., 0.2470, 0.5768, 0.1059]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_coo)
+tensor([0.0697, 0.4570, 0.5878,  ..., 0.8004, 0.4211, 0.6165])
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
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+tensor(indices=tensor([[7712, 8176, 2733,  ..., 9900, 3552,  719],
+                       [5895, 6102, 1680,  ..., 1426,  873, 1922]]),
+       values=tensor([0.7534, 0.5438, 0.6082,  ..., 0.2470, 0.5768, 0.1059]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_coo)
+tensor([0.0697, 0.4570, 0.5878,  ..., 0.8004, 0.4211, 0.6165])
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+Size: 100000000
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..1a1ffe0
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 35.104506969451904, "TIME_S_1KI": 351.04506969451904, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2223.327211351395, "W": 54.02, "J_1KI": 22233.272113513947, "W_1KI": 540.2, "W_D": 37.23700000000001, "J_D": 1532.5811804718976, "W_D_1KI": 372.3700000000001, "J_D_1KI": 3723.700000000001}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..81ebf24
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 35.104506969451904}
+
+tensor(indices=tensor([[5965, 1810, 9626,  ..., 4502, 3774, 7838],
+                       [1310, 6428, 4724,  ..., 7910, 1197, 4169]]),
+       values=tensor([0.7596, 0.3928, 0.9709,  ..., 0.9446, 0.5601, 0.7829]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.9776, 0.9172, 0.8687,  ..., 0.7797, 0.1327, 0.9713])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
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+Density: 0.1
+Time: 35.104506969451904 seconds
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+tensor(indices=tensor([[5965, 1810, 9626,  ..., 4502, 3774, 7838],
+                       [1310, 6428, 4724,  ..., 7910, 1197, 4169]]),
+       values=tensor([0.7596, 0.3928, 0.9709,  ..., 0.9446, 0.5601, 0.7829]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.9776, 0.9172, 0.8687,  ..., 0.7797, 0.1327, 0.9713])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
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+Time: 35.104506969451904 seconds
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..a4de997
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 70.16714549064636, "TIME_S_1KI": 701.6714549064636, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4225.564891979694, "W": 53.83, "J_1KI": 42255.64891979694, "W_1KI": 538.3, "W_D": 36.723749999999995, "J_D": 2882.7529017618294, "W_D_1KI": 367.23749999999995, "J_D_1KI": 3672.3749999999995}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..1afd81e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 70.16714549064636}
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+                       [2703, 1006, 6246,  ..., 5702, 7129, 7896]]),
+       values=tensor([0.2544, 0.6040, 0.8647,  ..., 0.6605, 0.8051, 0.9149]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_coo)
+tensor([0.7925, 0.4684, 0.4705,  ..., 0.0795, 0.6524, 0.8229])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 70.16714549064636 seconds
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+tensor(indices=tensor([[9700, 8094, 3775,  ..., 8917, 6161, 6776],
+                       [2703, 1006, 6246,  ..., 5702, 7129, 7896]]),
+       values=tensor([0.2544, 0.6040, 0.8647,  ..., 0.6605, 0.8051, 0.9149]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_coo)
+tensor([0.7925, 0.4684, 0.4705,  ..., 0.0795, 0.6524, 0.8229])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
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+Time: 70.16714549064636 seconds
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..4e52d36
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 105.21965885162354, "TIME_S_1KI": 1052.1965885162354, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 6222.783360099792, "W": 53.8, "J_1KI": 62227.83360099792, "W_1KI": 537.9999999999999, "W_D": 36.875, "J_D": 4265.151234269142, "W_D_1KI": 368.75, "J_D_1KI": 3687.5}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..4bb698f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 105.21965885162354}
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+                       [4914, 4898, 6139,  ..., 2861, 8664, 3517]]),
+       values=tensor([0.6261, 0.7581, 0.0102,  ..., 0.7531, 0.9323, 0.1407]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_coo)
+tensor([0.2937, 0.3731, 0.4040,  ..., 0.6645, 0.2192, 0.9273])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
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+NNZ: 30000000
+Density: 0.3
+Time: 105.21965885162354 seconds
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+tensor(indices=tensor([[5407, 5787, 9186,  ..., 8033, 8571, 4724],
+                       [4914, 4898, 6139,  ..., 2861, 8664, 3517]]),
+       values=tensor([0.6261, 0.7581, 0.0102,  ..., 0.7531, 0.9323, 0.1407]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_coo)
+tensor([0.2937, 0.3731, 0.4040,  ..., 0.6645, 0.2192, 0.9273])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
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+Time: 105.21965885162354 seconds
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..62ec3b8
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 140.21517753601074, "TIME_S_1KI": 1402.1517753601074, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 8232.081338682176, "W": 53.82000000000001, "J_1KI": 82320.81338682176, "W_1KI": 538.2000000000002, "W_D": 36.60050000000001, "J_D": 5598.258882133724, "W_D_1KI": 366.0050000000001, "J_D_1KI": 3660.050000000001}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..c569fce
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.4.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.4', '-c', '16']
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+                       [7948, 8550, 3543,  ..., 8398, 3778, 5745]]),
+       values=tensor([0.9606, 0.0989, 0.4394,  ..., 0.2887, 0.8749, 0.1670]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_coo)
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+                       [7948, 8550, 3543,  ..., 8398, 3778, 5745]]),
+       values=tensor([0.9606, 0.0989, 0.4394,  ..., 0.2887, 0.8749, 0.1670]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_coo)
+tensor([0.7317, 0.4476, 0.2901,  ..., 0.4493, 0.7492, 0.0926])
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..6c3cd55
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 175.4700062274933, "TIME_S_1KI": 1754.7000622749329, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 10255.436193523406, "W": 53.68, "J_1KI": 102554.36193523408, "W_1KI": 536.8, "W_D": 26.626000000000005, "J_D": 5086.833906273366, "W_D_1KI": 266.26000000000005, "J_D_1KI": 2662.6000000000004}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..82676b2
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_0.5.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.5', '-c', '16']
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+                       [6072, 3357, 8597,  ..., 2792, 9679, 7987]]),
+       values=tensor([0.3334, 0.3262, 0.6931,  ..., 0.2841, 0.1700, 0.7507]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_coo)
+tensor([0.2846, 0.5884, 0.8203,  ..., 0.0667, 0.5552, 0.4961])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 175.4700062274933 seconds
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+tensor(indices=tensor([[5161, 1288, 9022,  ..., 8146, 2109, 6157],
+                       [6072, 3357, 8597,  ..., 2792, 9679, 7987]]),
+       values=tensor([0.3334, 0.3262, 0.6931,  ..., 0.2841, 0.1700, 0.7507]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_coo)
+tensor([0.2846, 0.5884, 0.8203,  ..., 0.0667, 0.5552, 0.4961])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
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+Density: 0.5
+Time: 175.4700062274933 seconds
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..1e3ff7b
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 245084, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.43880581855774, "TIME_S_1KI": 0.04259276745343531, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 754.0787694597244, "W": 54.17, "J_1KI": 3.076817619508921, "W_1KI": 0.22102626038419482, "W_D": 37.293000000000006, "J_D": 519.1408445534707, "W_D_1KI": 0.15216415596285357, "J_D_1KI": 0.0006208653194939432}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..459b6c1
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,962 @@
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..eed56bf
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 57307, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.488039493560791, "TIME_S_1KI": 0.18301498060552446, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 752.8469358444214, "W": 54.18, "J_1KI": 13.137085100326686, "W_1KI": 0.945434240145183, "W_D": 37.037000000000006, "J_D": 514.6399402523042, "W_D_1KI": 0.6462910290191426, "J_D_1KI": 0.01127769782084462}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..cd4ab9c
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,73 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.030483007431030273}
+
+tensor(indices=tensor([[9082, 5330, 1512,  ..., 3284, 6824, 2716],
+                       [4275, 6555, 5954,  ...,  101, 7859, 2785]]),
+       values=tensor([0.6080, 0.6826, 0.1706,  ..., 0.1752, 0.3853, 0.5537]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_coo)
+tensor([0.0332, 0.8842, 0.7703,  ..., 0.1727, 0.8378, 0.3817])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.030483007431030273 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '34445', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.311120271682739}
+
+tensor(indices=tensor([[9468, 3923, 6376,  ..., 4943, 3935, 3556],
+                       [3509, 9230, 3239,  ..., 3460, 1571, 2853]]),
+       values=tensor([0.2308, 0.6081, 0.0151,  ..., 0.5369, 0.1924, 0.6647]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_coo)
+tensor([0.8348, 0.9189, 0.6141,  ..., 0.2864, 0.8825, 0.6909])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 6.311120271682739 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '57307', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.488039493560791}
+
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+                       [ 698, 8560, 1501,  ..., 4840, 7181, 4483]]),
+       values=tensor([0.8865, 0.1975, 0.1253,  ..., 0.2308, 0.7279, 0.8573]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_coo)
+tensor([0.0766, 0.7259, 0.7366,  ..., 0.7003, 0.3554, 0.0669])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.488039493560791 seconds
+
+tensor(indices=tensor([[1357, 4226, 9041,  ..., 7211, 2903, 1512],
+                       [ 698, 8560, 1501,  ..., 4840, 7181, 4483]]),
+       values=tensor([0.8865, 0.1975, 0.1253,  ..., 0.2308, 0.7279, 0.8573]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_coo)
+tensor([0.0766, 0.7259, 0.7366,  ..., 0.7003, 0.3554, 0.0669])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
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+Density: 5e-05
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..4581c49
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_0.0001.json
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..5813911
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_0.0001.output
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..99b9838
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_1e-05.json
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..b874877
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_1e-05.output
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..0a002a9
--- /dev/null
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..ab75b94
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+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_500000_5e-05.output
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..8734f45
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.0001.json
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..64306f3
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.0001.output
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..10b0ac1
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.001.json
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..1ad5d61
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.001.output
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..88e1cd6
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.01.json
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..d1b3ab0
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.01.output
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.05.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.05.json
new file mode 100644
index 0000000..f18df01
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.05.json
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.05.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.05.output
new file mode 100644
index 0000000..a30ee4f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_0.05.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '50000', '-sd', '0.05', '-c', '16']
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..ffd2a22
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_1e-05.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 9390, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.600325584411621, "TIME_S_1KI": 1.1288951634091182, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1132.2721235394476, "W": 81.35, "J_1KI": 120.58276076032456, "W_1KI": 8.663471778487752, "W_D": 55.831999999999994, "J_D": 777.0991665821075, "W_D_1KI": 5.945899893503727, "J_D_1KI": 0.6332161760919837}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..d98ed48
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_1e-05.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '50000', '-sd', '1e-05', '-c', '16']
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+                       [10562, 38430, 30967,  ...,  5071, 20662,   261]]),
+       values=tensor([0.8725, 0.3150, 0.6120,  ..., 0.7598, 0.1667, 0.8221]),
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+tensor([0.4073, 0.4312, 0.0425,  ..., 0.0130, 0.8266, 0.4352])
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..0c3aed5
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_5e-05.json
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..8f57ac2
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+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_50000_5e-05.output
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index 0000000..ed09269
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.0001.json
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.0001.output
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index 0000000..c21d2db
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+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.0001.output
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..5b7a026
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..dae494e
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+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.001.output
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..de2c625
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.01.json
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..eac492b
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.01.output
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new file mode 100644
index 0000000..a8877f7
--- /dev/null
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..39d5cc5
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+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.05.output
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..7e731ad
--- /dev/null
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..4debb38
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.1.output
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..bed1137
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.2.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 17.56717610359192, "TIME_S_1KI": 175.6717610359192, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1204.4688155555725, "W": 54.160000000000004, "J_1KI": 12044.688155555725, "W_1KI": 541.6000000000001, "W_D": 37.317750000000004, "J_D": 829.9125949353577, "W_D_1KI": 373.17750000000007, "J_D_1KI": 3731.7750000000005}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..9ed476e
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.2.output
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..d3fe69a
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.3.json
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..4255392
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.3.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '0.3', '-c', '16']
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..2d0da51
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.4.json
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..347098c
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.4.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '0.4', '-c', '16']
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..3458276
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.5.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 43.865360498428345, "TIME_S_1KI": 438.65360498428345, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2714.299004650116, "W": 54.05, "J_1KI": 27142.990046501156, "W_1KI": 540.5, "W_D": 37.03825, "J_D": 1859.9978743567465, "W_D_1KI": 370.3825, "J_D_1KI": 3703.8250000000003}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..71de153
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '0.5', '-c', '16']
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..029137a
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 647175, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.114959955215454, "TIME_S_1KI": 0.015629404651316035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 733.7914706420898, "W": 54.08, "J_1KI": 1.1338377882985127, "W_1KI": 0.08356317842932746, "W_D": 36.693999999999996, "J_D": 497.8872822437286, "W_D_1KI": 0.05669872909182214, "J_D_1KI": 8.760957869482311e-05}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..a00aeb7
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,429 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '1e-05', '-c', '16']
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..97e24d5
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 206108, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.516620397567749, "TIME_S_1KI": 0.051024804459641304, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 755.539121131897, "W": 54.27, "J_1KI": 3.6657437903036123, "W_1KI": 0.26330855667902264, "W_D": 37.44975000000001, "J_D": 521.3700239839555, "W_D_1KI": 0.18169964290566115, "J_D_1KI": 0.0008815749165760725}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..ad34e24
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_coo_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,73 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.01703357696533203}
+
+tensor(indices=tensor([[ 475, 3738, 3288,  ..., 4069,  887, 1359],
+                       [1010, 4738, 4553,  ..., 4388,  225, 2926]]),
+       values=tensor([0.2526, 0.0196, 0.5767,  ..., 0.5177, 0.2605, 0.1150]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.0360, 0.3764, 0.3903,  ..., 0.6391, 0.0436, 0.4518])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.01703357696533203 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '61642', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.140289306640625}
+
+tensor(indices=tensor([[ 167, 1713, 4757,  ...,   62, 1670, 2419],
+                       [ 610, 1517, 4376,  ..., 3286, 2079, 2105]]),
+       values=tensor([0.4242, 0.0107, 0.6695,  ..., 0.4660, 0.2421, 0.5912]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.7928, 0.1057, 0.9545,  ..., 0.4896, 0.0992, 0.9309])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 3.140289306640625 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '206108', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.516620397567749}
+
+tensor(indices=tensor([[2695, 3871,  432,  ..., 1583, 3020, 2131],
+                       [2211,  835,  658,  ..., 3988, 3418, 3891]]),
+       values=tensor([0.1995, 0.5778, 0.7653,  ..., 0.2548, 0.7811, 0.9660]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.4693, 0.9791, 0.1618,  ..., 0.4783, 0.1670, 0.5419])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.516620397567749 seconds
+
+tensor(indices=tensor([[2695, 3871,  432,  ..., 1583, 3020, 2131],
+                       [2211,  835,  658,  ..., 3988, 3418, 3891]]),
+       values=tensor([0.1995, 0.5778, 0.7653,  ..., 0.2548, 0.7811, 0.9660]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.4693, 0.9791, 0.1618,  ..., 0.4783, 0.1670, 0.5419])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.516620397567749 seconds
+
+[19.2, 18.48, 18.65, 18.58, 19.01, 18.56, 18.65, 18.53, 18.98, 18.52]
+[54.27]
+13.921855926513672
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 206108, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.516620397567749, 'TIME_S_1KI': 0.051024804459641304, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 755.539121131897, 'W': 54.27}
+[19.2, 18.48, 18.65, 18.58, 19.01, 18.56, 18.65, 18.53, 18.98, 18.52, 19.01, 18.42, 18.89, 18.41, 18.81, 18.5, 19.05, 18.68, 18.47, 18.74]
+336.405
+16.820249999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 206108, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.516620397567749, 'TIME_S_1KI': 0.051024804459641304, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 755.539121131897, 'W': 54.27, 'J_1KI': 3.6657437903036123, 'W_1KI': 0.26330855667902264, 'W_D': 37.44975000000001, 'J_D': 521.3700239839555, 'W_D_1KI': 0.18169964290566115, 'J_D_1KI': 0.0008815749165760725}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json
index c7b5a2b..ffb2e2a 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 33105, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.270436525344849, "TIME_S_1KI": 0.31023822761953934, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1243.494291753769, "W": 88.28, "J_1KI": 37.56212933858236, "W_1KI": 2.6666666666666665, "W_D": 71.58725, "J_D": 1008.3635788100362, "W_D_1KI": 2.1624301465035494, "J_D_1KI": 0.0653203487842788}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 30731, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.384181499481201, "TIME_S_1KI": 0.3379057466233185, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1280.0619927191735, "W": 90.17, "J_1KI": 41.65376957206643, "W_1KI": 2.934170707103576, "W_D": 73.17975, "J_D": 1038.866769565165, "W_D_1KI": 2.3813006410465003, "J_D_1KI": 0.07748855035783087}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.output
index 31fde2c..3ceb939 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.047393798828125}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.046231746673583984}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      11,      25,  ...,  999973,
-                             999987, 1000000]),
-       col_indices=tensor([ 2538, 10020, 11588,  ..., 84720, 92719, 95287]),
-       values=tensor([0.8172, 0.5815, 0.2513,  ..., 0.2819, 0.8178, 0.1271]),
+tensor(crow_indices=tensor([      0,      12,      30,  ...,  999983,
+                             999990, 1000000]),
+       col_indices=tensor([ 1195,  1262,  6291,  ..., 68939, 88156, 98049]),
+       values=tensor([0.1288, 0.6989, 0.0067,  ..., 0.2774, 0.2181, 0.0994]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9323, 0.3660, 0.2073,  ..., 0.7127, 0.8566, 0.0523])
+tensor([0.9227, 0.2479, 0.1465,  ..., 0.1264, 0.7704, 0.2389])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,39 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 0.047393798828125 seconds
+Time: 0.046231746673583984 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22154', '-ss', '100000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.0265889167785645}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22711', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.759751558303833}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      11,      20,  ...,  999976,
-                             999985, 1000000]),
-       col_indices=tensor([10428, 14843, 15503,  ..., 86013, 91025, 96391]),
-       values=tensor([0.0148, 0.3731, 0.6426,  ..., 0.4125, 0.5086, 0.3848]),
-       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0982, 0.0147, 0.0440,  ..., 0.9267, 0.0489, 0.6248])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 1000000
-Density: 0.0001
-Time: 7.0265889167785645 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33105', '-ss', '100000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.270436525344849}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      12,      18,  ...,  999974,
+tensor(crow_indices=tensor([      0,      13,      26,  ...,  999975,
                              999988, 1000000]),
-       col_indices=tensor([  697, 33076, 59577,  ..., 88840, 91058, 94574]),
-       values=tensor([0.8969, 0.3012, 0.5025,  ..., 0.5812, 0.6517, 0.5598]),
+       col_indices=tensor([   54,  3894,  8242,  ..., 85217, 87443, 91730]),
+       values=tensor([0.7440, 0.9626, 0.1209,  ..., 0.0767, 0.1943, 0.4883]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0089, 0.5150, 0.8606,  ..., 0.9603, 0.9290, 0.1786])
+tensor([0.9335, 0.7866, 0.9277,  ..., 0.2681, 0.0485, 0.1026])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.270436525344849 seconds
+Time: 7.759751558303833 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '30731', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.384181499481201}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      12,      18,  ...,  999974,
-                             999988, 1000000]),
-       col_indices=tensor([  697, 33076, 59577,  ..., 88840, 91058, 94574]),
-       values=tensor([0.8969, 0.3012, 0.5025,  ..., 0.5812, 0.6517, 0.5598]),
+tensor(crow_indices=tensor([      0,       6,      15,  ...,  999978,
+                             999990, 1000000]),
+       col_indices=tensor([ 5042, 44146, 46975,  ..., 74653, 86841, 94052]),
+       values=tensor([0.0034, 0.2057, 0.9151,  ..., 0.0568, 0.4580, 0.2811]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0089, 0.5150, 0.8606,  ..., 0.9603, 0.9290, 0.1786])
+tensor([0.0913, 0.1633, 0.2108,  ..., 0.2844, 0.2437, 0.6726])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.270436525344849 seconds
+Time: 10.384181499481201 seconds
 
-[18.44, 17.49, 17.8, 17.92, 17.7, 20.94, 18.06, 17.75, 21.44, 18.07]
-[88.28]
-14.085798501968384
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33105, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.270436525344849, 'TIME_S_1KI': 0.31023822761953934, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1243.494291753769, 'W': 88.28}
-[18.44, 17.49, 17.8, 17.92, 17.7, 20.94, 18.06, 17.75, 21.44, 18.07, 18.18, 18.15, 17.95, 18.02, 21.88, 18.26, 18.15, 17.92, 17.92, 18.32]
-333.855
-16.69275
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33105, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.270436525344849, 'TIME_S_1KI': 0.31023822761953934, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1243.494291753769, 'W': 88.28, 'J_1KI': 37.56212933858236, 'W_1KI': 2.6666666666666665, 'W_D': 71.58725, 'J_D': 1008.3635788100362, 'W_D_1KI': 2.1624301465035494, 'J_D_1KI': 0.0653203487842788}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      15,  ...,  999978,
+                             999990, 1000000]),
+       col_indices=tensor([ 5042, 44146, 46975,  ..., 74653, 86841, 94052]),
+       values=tensor([0.0034, 0.2057, 0.9151,  ..., 0.0568, 0.4580, 0.2811]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0913, 0.1633, 0.2108,  ..., 0.2844, 0.2437, 0.6726])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.384181499481201 seconds
+
+[19.26, 18.61, 18.82, 18.38, 18.86, 18.56, 18.72, 18.5, 18.64, 18.52]
+[90.17]
+14.196096181869507
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 30731, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.384181499481201, 'TIME_S_1KI': 0.3379057466233185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1280.0619927191735, 'W': 90.17}
+[19.26, 18.61, 18.82, 18.38, 18.86, 18.56, 18.72, 18.5, 18.64, 18.52, 20.22, 18.46, 18.71, 18.42, 18.71, 18.56, 18.79, 20.64, 20.22, 18.41]
+339.805
+16.99025
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 30731, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.384181499481201, 'TIME_S_1KI': 0.3379057466233185, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1280.0619927191735, 'W': 90.17, 'J_1KI': 41.65376957206643, 'W_1KI': 2.934170707103576, 'W_D': 73.17975, 'J_D': 1038.866769565165, 'W_D_1KI': 2.3813006410465003, 'J_D_1KI': 0.07748855035783087}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.json
index 8d42109..d019bd1 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2748, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.477163553237915, "TIME_S_1KI": 3.8126504924446563, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1424.6626942634582, "W": 81.1, "J_1KI": 518.4362060638495, "W_1KI": 29.512372634643373, "W_D": 64.67649999999999, "J_D": 1136.1553236193656, "W_D_1KI": 23.535844250363898, "J_D_1KI": 8.564717703916994}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2634, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.097931861877441, "TIME_S_1KI": 3.83368711536729, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1385.399602217674, "W": 83.66, "J_1KI": 525.9679583210608, "W_1KI": 31.761579347000758, "W_D": 66.5715, "J_D": 1102.4160843776465, "W_D_1KI": 25.273917995444194, "J_D_1KI": 9.595261197966664}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.output
index 280d994..d785c4e 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.4098339080810547}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.3985879421234131}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      105,      217,  ...,  9999774,
-                             9999878, 10000000]),
-       col_indices=tensor([ 2925,  3045,  3251,  ..., 98848, 99298, 99703]),
-       values=tensor([0.4813, 0.4380, 0.0490,  ..., 0.5758, 0.0326, 0.9259]),
+tensor(crow_indices=tensor([       0,       87,      172,  ...,  9999773,
+                             9999880, 10000000]),
+       col_indices=tensor([ 1874,  1886,  2437,  ..., 99368, 99870, 99904]),
+       values=tensor([0.7346, 0.5677, 0.7870,  ..., 0.6229, 0.4001, 0.9653]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.0648, 0.1204, 0.6207,  ..., 0.1724, 0.6764, 0.4459])
+tensor([0.5593, 0.9092, 0.2146,  ..., 0.7858, 0.8030, 0.2816])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,39 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 0.4098339080810547 seconds
+Time: 0.3985879421234131 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2562', '-ss', '100000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.787212610244751}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2634', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.097931861877441}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       90,      201,  ...,  9999780,
-                             9999895, 10000000]),
-       col_indices=tensor([ 1242,  4056,  4707,  ..., 96589, 97728, 98727]),
-       values=tensor([0.8897, 0.2716, 0.4760,  ..., 0.6356, 0.3047, 0.7796]),
-       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4134, 0.7359, 0.5031,  ..., 0.9568, 0.9528, 0.4063])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 10000000
-Density: 0.001
-Time: 9.787212610244751 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2748', '-ss', '100000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.477163553237915}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       82,      171,  ...,  9999801,
+tensor(crow_indices=tensor([       0,       97,      205,  ...,  9999773,
                              9999897, 10000000]),
-       col_indices=tensor([ 1661,  2279,  2856,  ..., 99449, 99691, 99739]),
-       values=tensor([0.1663, 0.0376, 0.1009,  ..., 0.7118, 0.9261, 0.1836]),
+       col_indices=tensor([ 2815,  3659,  3819,  ..., 99680, 99902, 99908]),
+       values=tensor([0.9090, 0.6219, 0.8306,  ..., 0.7078, 0.9813, 0.3499]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.7724, 0.0559, 0.5235,  ..., 0.7708, 0.2517, 0.0642])
+tensor([0.4504, 0.4366, 0.8174,  ..., 0.8474, 0.0095, 0.3536])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 10.477163553237915 seconds
+Time: 10.097931861877441 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       82,      171,  ...,  9999801,
+tensor(crow_indices=tensor([       0,       97,      205,  ...,  9999773,
                              9999897, 10000000]),
-       col_indices=tensor([ 1661,  2279,  2856,  ..., 99449, 99691, 99739]),
-       values=tensor([0.1663, 0.0376, 0.1009,  ..., 0.7118, 0.9261, 0.1836]),
+       col_indices=tensor([ 2815,  3659,  3819,  ..., 99680, 99902, 99908]),
+       values=tensor([0.9090, 0.6219, 0.8306,  ..., 0.7078, 0.9813, 0.3499]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.7724, 0.0559, 0.5235,  ..., 0.7708, 0.2517, 0.0642])
+tensor([0.4504, 0.4366, 0.8174,  ..., 0.8474, 0.0095, 0.3536])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 10.477163553237915 seconds
+Time: 10.097931861877441 seconds
 
-[18.37, 17.8, 17.96, 17.86, 22.53, 18.03, 17.75, 18.1, 18.23, 17.82]
-[81.1]
-17.56674098968506
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2748, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.477163553237915, 'TIME_S_1KI': 3.8126504924446563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1424.6626942634582, 'W': 81.1}
-[18.37, 17.8, 17.96, 17.86, 22.53, 18.03, 17.75, 18.1, 18.23, 17.82, 18.37, 17.83, 17.94, 18.39, 17.8, 17.92, 17.99, 17.94, 18.19, 17.86]
-328.47
-16.4235
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2748, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.477163553237915, 'TIME_S_1KI': 3.8126504924446563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1424.6626942634582, 'W': 81.1, 'J_1KI': 518.4362060638495, 'W_1KI': 29.512372634643373, 'W_D': 64.67649999999999, 'J_D': 1136.1553236193656, 'W_D_1KI': 23.535844250363898, 'J_D_1KI': 8.564717703916994}
+[19.1, 18.42, 18.45, 18.83, 18.64, 19.33, 22.53, 19.65, 18.77, 18.43]
+[83.66]
+16.55988049507141
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2634, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.097931861877441, 'TIME_S_1KI': 3.83368711536729, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1385.399602217674, 'W': 83.66}
+[19.1, 18.42, 18.45, 18.83, 18.64, 19.33, 22.53, 19.65, 18.77, 18.43, 18.95, 18.64, 18.76, 18.76, 18.59, 18.84, 18.6, 18.55, 18.85, 18.64]
+341.77
+17.0885
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2634, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.097931861877441, 'TIME_S_1KI': 3.83368711536729, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1385.399602217674, 'W': 83.66, 'J_1KI': 525.9679583210608, 'W_1KI': 31.761579347000758, 'W_D': 66.5715, 'J_D': 1102.4160843776465, 'W_D_1KI': 25.273917995444194, 'J_D_1KI': 9.595261197966664}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..2bf9ffc
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 305, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.454807043075562, "TIME_S_1KI": 34.278055878936264, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2894.4997478580476, "W": 65.14, "J_1KI": 9490.163107731303, "W_1KI": 213.5737704918033, "W_D": 47.742999999999995, "J_D": 2121.4630252070424, "W_D_1KI": 156.53442622950817, "J_D_1KI": 513.227626981994}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..535d52d
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.442110300064087}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       998,      1943,  ...,
+                             99998007,  99998988, 100000000]),
+       col_indices=tensor([   14,    86,   254,  ..., 99837, 99963, 99972]),
+       values=tensor([0.4084, 0.5337, 0.1918,  ..., 0.2591, 0.7499, 0.7582]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.0487, 0.8713, 0.6713,  ..., 0.4052, 0.6741, 0.8037])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 3.442110300064087 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '305', '-ss', '100000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.454807043075562}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       936,      2004,  ...,
+                             99997969,  99998972, 100000000]),
+       col_indices=tensor([  114,   337,   399,  ..., 99809, 99864, 99916]),
+       values=tensor([0.1247, 0.0736, 0.4797,  ..., 0.5713, 0.1398, 0.9744]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.2448, 0.2958, 0.9514,  ..., 0.8807, 0.6919, 0.0958])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 10.454807043075562 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       936,      2004,  ...,
+                             99997969,  99998972, 100000000]),
+       col_indices=tensor([  114,   337,   399,  ..., 99809, 99864, 99916]),
+       values=tensor([0.1247, 0.0736, 0.4797,  ..., 0.5713, 0.1398, 0.9744]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.2448, 0.2958, 0.9514,  ..., 0.8807, 0.6919, 0.0958])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 10.454807043075562 seconds
+
+[18.95, 18.95, 18.47, 18.47, 18.56, 18.56, 22.56, 20.46, 19.09, 19.2]
+[65.14]
+44.43505907058716
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 305, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.454807043075562, 'TIME_S_1KI': 34.278055878936264, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2894.4997478580476, 'W': 65.14}
+[18.95, 18.95, 18.47, 18.47, 18.56, 18.56, 22.56, 20.46, 19.09, 19.2, 18.7, 18.78, 23.07, 19.93, 18.61, 18.97, 18.63, 18.45, 18.49, 18.93]
+347.94000000000005
+17.397000000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 305, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.454807043075562, 'TIME_S_1KI': 34.278055878936264, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2894.4997478580476, 'W': 65.14, 'J_1KI': 9490.163107731303, 'W_1KI': 213.5737704918033, 'W_D': 47.742999999999995, 'J_D': 2121.4630252070424, 'W_D_1KI': 156.53442622950817, 'J_D_1KI': 513.227626981994}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.json
index d39bedd..62c8a18 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 65044, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.857811450958252, "TIME_S_1KI": 0.1669302541503944, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1160.4457891416548, "W": 82.65999999999998, "J_1KI": 17.840935199890147, "W_1KI": 1.2708320521493142, "W_D": 66.37149999999998, "J_D": 931.7750749336478, "W_D_1KI": 1.0204092614230365, "J_D_1KI": 0.015687984463179334}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 63380, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.448274612426758, "TIME_S_1KI": 0.16485128766845625, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1245.8409766912462, "W": 89.9, "J_1KI": 19.656689439748284, "W_1KI": 1.418428526349006, "W_D": 73.0515, "J_D": 1012.353193645835, "W_D_1KI": 1.1525954559798044, "J_D_1KI": 0.018185475796462677}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.output
index a139e8c..634073e 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.03411436080932617}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.03290224075317383}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      1,  ...,  99997,  99998,
+tensor(crow_indices=tensor([     0,      1,      2,  ...,  99998,  99998,
                             100000]),
-       col_indices=tensor([61814, 31861, 93735,  ..., 37976, 26709, 88923]),
-       values=tensor([0.4964, 0.9275, 0.0463,  ..., 0.6388, 0.5613, 0.1901]),
+       col_indices=tensor([54942, 27722, 96194,  ..., 71284,  7275, 46774]),
+       values=tensor([0.2018, 0.2814, 0.6673,  ..., 0.3211, 0.5324, 0.9423]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.9466, 0.9805, 0.4146,  ..., 0.4981, 0.9805, 0.0095])
+tensor([0.2274, 0.1599, 0.8719,  ..., 0.7214, 0.9707, 0.2763])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 0.03411436080932617 seconds
+Time: 0.03290224075317383 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '30778', '-ss', '100000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.968464136123657}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '31912', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.286698579788208}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      2,  ...,  99993,  99997,
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99998, 100000,
                             100000]),
-       col_indices=tensor([86302, 87189, 44148,  ...,  4090, 44893, 91495]),
-       values=tensor([0.5947, 0.5243, 0.4325,  ..., 0.8552, 0.8488, 0.7980]),
+       col_indices=tensor([93916, 16703, 19935,  ..., 93618,  1127, 15891]),
+       values=tensor([0.6597, 0.5386, 0.9885,  ..., 0.0840, 0.8294, 0.9651]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2800, 0.7786, 0.6115,  ..., 0.5946, 0.9897, 0.5537])
+tensor([0.8297, 0.7040, 0.7892,  ..., 0.5694, 0.6960, 0.4166])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 4.968464136123657 seconds
+Time: 5.286698579788208 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '65044', '-ss', '100000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.857811450958252}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '63380', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.448274612426758}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      2,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99995,  99998,
                             100000]),
-       col_indices=tensor([27172, 43192, 23755,  ..., 52370, 88374,  3897]),
-       values=tensor([0.0211, 0.7600, 0.3262,  ..., 0.1220, 0.7210, 0.9662]),
+       col_indices=tensor([52857, 69814, 79622,  ..., 57272,  6879, 75944]),
+       values=tensor([0.8320, 0.0214, 0.5046,  ..., 0.3100, 0.5363, 0.2559]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8254, 0.5550, 0.3634,  ..., 0.5298, 0.8710, 0.0274])
+tensor([0.4864, 0.2626, 0.9920,  ..., 0.6643, 0.2369, 0.7561])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.857811450958252 seconds
+Time: 10.448274612426758 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      2,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99995,  99998,
                             100000]),
-       col_indices=tensor([27172, 43192, 23755,  ..., 52370, 88374,  3897]),
-       values=tensor([0.0211, 0.7600, 0.3262,  ..., 0.1220, 0.7210, 0.9662]),
+       col_indices=tensor([52857, 69814, 79622,  ..., 57272,  6879, 75944]),
+       values=tensor([0.8320, 0.0214, 0.5046,  ..., 0.3100, 0.5363, 0.2559]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8254, 0.5550, 0.3634,  ..., 0.5298, 0.8710, 0.0274])
+tensor([0.4864, 0.2626, 0.9920,  ..., 0.6643, 0.2369, 0.7561])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.857811450958252 seconds
+Time: 10.448274612426758 seconds
 
-[18.23, 17.9, 18.0, 17.99, 18.15, 18.02, 18.22, 19.12, 18.05, 18.53]
-[82.66]
-14.038782835006714
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 65044, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.857811450958252, 'TIME_S_1KI': 0.1669302541503944, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1160.4457891416548, 'W': 82.65999999999998}
-[18.23, 17.9, 18.0, 17.99, 18.15, 18.02, 18.22, 19.12, 18.05, 18.53, 18.28, 17.9, 18.17, 17.79, 18.21, 17.83, 17.84, 17.79, 18.22, 18.1]
-325.77
-16.2885
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 65044, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.857811450958252, 'TIME_S_1KI': 0.1669302541503944, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1160.4457891416548, 'W': 82.65999999999998, 'J_1KI': 17.840935199890147, 'W_1KI': 1.2708320521493142, 'W_D': 66.37149999999998, 'J_D': 931.7750749336478, 'W_D_1KI': 1.0204092614230365, 'J_D_1KI': 0.015687984463179334}
+[19.19, 18.69, 18.45, 18.47, 18.61, 18.61, 18.61, 19.04, 18.85, 18.85]
+[89.9]
+13.858075380325317
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 63380, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.448274612426758, 'TIME_S_1KI': 0.16485128766845625, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1245.8409766912462, 'W': 89.9}
+[19.19, 18.69, 18.45, 18.47, 18.61, 18.61, 18.61, 19.04, 18.85, 18.85, 19.75, 18.58, 18.57, 18.93, 18.64, 18.51, 18.65, 18.72, 18.83, 18.63]
+336.96999999999997
+16.848499999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 63380, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.448274612426758, 'TIME_S_1KI': 0.16485128766845625, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1245.8409766912462, 'W': 89.9, 'J_1KI': 19.656689439748284, 'W_1KI': 1.418428526349006, 'W_D': 73.0515, 'J_D': 1012.353193645835, 'W_D_1KI': 1.1525954559798044, 'J_D_1KI': 0.018185475796462677}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.json
index 4c80d3b..fe215d7 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 45510, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.42077898979187, "TIME_S_1KI": 0.22897778487787013, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1214.5695349788666, "W": 87.39, "J_1KI": 26.687970445591443, "W_1KI": 1.920237310481213, "W_D": 71.04925, "J_D": 987.4614318926334, "W_D_1KI": 1.561178861788618, "J_D_1KI": 0.03430408397689778}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 44511, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.551278114318848, "TIME_S_1KI": 0.2370487770285738, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1257.1718712615966, "W": 90.27, "J_1KI": 28.244071606155703, "W_1KI": 2.0280380130754194, "W_D": 73.12225, "J_D": 1018.3586558475494, "W_D_1KI": 1.6427905461571295, "J_D_1KI": 0.03690751827991125}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.output
index d883b1c..13147a5 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.039800167083740234}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.03855562210083008}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,     10,  ..., 499993, 499997,
+tensor(crow_indices=tensor([     0,      5,     15,  ..., 499991, 499992,
                             500000]),
-       col_indices=tensor([ 4828, 48889, 52503,  ..., 31911, 36084, 76746]),
-       values=tensor([0.4793, 0.0828, 0.1169,  ..., 0.5530, 0.3033, 0.0718]),
+       col_indices=tensor([ 4960, 13928, 21214,  ..., 73878, 78273, 81286]),
+       values=tensor([0.1301, 0.5462, 0.1608,  ..., 0.3095, 0.2509, 0.1525]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.5059, 0.6298, 0.1664,  ..., 0.1879, 0.5431, 0.8952])
+tensor([0.0485, 0.4431, 0.3633,  ..., 0.2811, 0.8933, 0.3293])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 0.039800167083740234 seconds
+Time: 0.03855562210083008 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '26381', '-ss', '100000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.086489200592041}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '27233', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.424152374267578}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      7,  ..., 499989, 499994,
+tensor(crow_indices=tensor([     0,      8,     17,  ..., 499990, 499995,
                             500000]),
-       col_indices=tensor([14519, 22983, 80951,  ..., 84187, 95762, 97051]),
-       values=tensor([0.1542, 0.8524, 0.3039,  ..., 0.4189, 0.6409, 0.4295]),
+       col_indices=tensor([   37, 25573, 30814,  ..., 36843, 53237, 94063]),
+       values=tensor([0.0210, 0.1202, 0.2131,  ..., 0.0650, 0.9267, 0.3226]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.0352, 0.2725, 0.4170,  ..., 0.1491, 0.4370, 0.8032])
+tensor([0.5660, 0.0682, 0.3989,  ..., 0.3041, 0.2596, 0.1166])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 6.086489200592041 seconds
+Time: 6.424152374267578 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '45510', '-ss', '100000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.42077898979187}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '44511', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.551278114318848}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     19,  ..., 499990, 499995,
+tensor(crow_indices=tensor([     0,      5,      8,  ..., 499992, 499997,
                             500000]),
-       col_indices=tensor([14786, 31808, 59751,  ..., 39791, 89593, 95677]),
-       values=tensor([0.6756, 0.3891, 0.0863,  ..., 0.6881, 0.4209, 0.2818]),
+       col_indices=tensor([   63, 39760, 47695,  ..., 39492, 46376, 76986]),
+       values=tensor([0.5378, 0.5224, 0.7674,  ..., 0.6916, 0.3031, 0.4245]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.4948, 0.2234, 0.2049,  ..., 0.0447, 0.7948, 0.3022])
+tensor([0.4779, 0.0684, 0.1618,  ..., 0.9444, 0.2606, 0.1176])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.42077898979187 seconds
+Time: 10.551278114318848 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     19,  ..., 499990, 499995,
+tensor(crow_indices=tensor([     0,      5,      8,  ..., 499992, 499997,
                             500000]),
-       col_indices=tensor([14786, 31808, 59751,  ..., 39791, 89593, 95677]),
-       values=tensor([0.6756, 0.3891, 0.0863,  ..., 0.6881, 0.4209, 0.2818]),
+       col_indices=tensor([   63, 39760, 47695,  ..., 39492, 46376, 76986]),
+       values=tensor([0.5378, 0.5224, 0.7674,  ..., 0.6916, 0.3031, 0.4245]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.4948, 0.2234, 0.2049,  ..., 0.0447, 0.7948, 0.3022])
+tensor([0.4779, 0.0684, 0.1618,  ..., 0.9444, 0.2606, 0.1176])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.42077898979187 seconds
+Time: 10.551278114318848 seconds
 
-[19.52, 19.27, 18.28, 17.93, 17.92, 18.18, 17.97, 18.17, 18.27, 18.17]
-[87.39]
-13.898266792297363
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 45510, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.42077898979187, 'TIME_S_1KI': 0.22897778487787013, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1214.5695349788666, 'W': 87.39}
-[19.52, 19.27, 18.28, 17.93, 17.92, 18.18, 17.97, 18.17, 18.27, 18.17, 18.18, 17.95, 17.9, 18.1, 18.19, 18.03, 17.88, 17.96, 18.05, 17.66]
-326.815
-16.34075
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 45510, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.42077898979187, 'TIME_S_1KI': 0.22897778487787013, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1214.5695349788666, 'W': 87.39, 'J_1KI': 26.687970445591443, 'W_1KI': 1.920237310481213, 'W_D': 71.04925, 'J_D': 987.4614318926334, 'W_D_1KI': 1.561178861788618, 'J_D_1KI': 0.03430408397689778}
+[19.01, 18.3, 18.41, 18.76, 20.61, 20.75, 19.07, 18.89, 18.69, 18.25]
+[90.27]
+13.926795959472656
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 44511, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.551278114318848, 'TIME_S_1KI': 0.2370487770285738, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1257.1718712615966, 'W': 90.27}
+[19.01, 18.3, 18.41, 18.76, 20.61, 20.75, 19.07, 18.89, 18.69, 18.25, 20.63, 20.31, 18.94, 19.02, 18.94, 18.38, 18.66, 18.47, 18.45, 18.72]
+342.95500000000004
+17.147750000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 44511, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.551278114318848, 'TIME_S_1KI': 0.2370487770285738, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1257.1718712615966, 'W': 90.27, 'J_1KI': 28.244071606155703, 'W_1KI': 2.0280380130754194, 'W_D': 73.12225, 'J_D': 1018.3586558475494, 'W_D_1KI': 1.6427905461571295, 'J_D_1KI': 0.03690751827991125}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.json
index df907c2..d8ae514 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 251263, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.831458568572998, "TIME_S_1KI": 0.04310805239359952, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1035.0438996100424, "W": 74.32999999999998, "J_1KI": 4.119364568639403, "W_1KI": 0.2958254896264073, "W_D": 58.06174999999998, "J_D": 808.5088139134047, "W_D_1KI": 0.2310795859318721, "J_D_1KI": 0.0009196721599752933}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 253116, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.375891208648682, "TIME_S_1KI": 0.04099263266110669, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1177.9051132917405, "W": 82.7, "J_1KI": 4.653617761389009, "W_1KI": 0.32672766636640904, "W_D": 65.55350000000001, "J_D": 933.6856450322869, "W_D_1KI": 0.25898599851451515, "J_D_1KI": 0.0010231909421550402}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.output
index bb6d734..55827ff 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.019912242889404297}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.02077317237854004}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ...,  9999,  9999, 10000]),
-       col_indices=tensor([1836, 4826, 4334,  ..., 9720, 1658, 3253]),
-       values=tensor([0.6220, 0.1290, 0.9015,  ..., 0.3260, 0.3650, 0.7979]),
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([7897, 2161, 4159,  ..., 8292, 1674, 8120]),
+       values=tensor([0.6051, 0.7025, 0.1799,  ..., 0.7244, 0.2043, 0.9406]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.5720, 0.7293, 0.6280,  ..., 0.0388, 0.6575, 0.1842])
+tensor([0.0769, 0.9771, 0.4968,  ..., 0.7484, 0.1210, 0.2610])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 0.019912242889404297 seconds
+Time: 0.02077317237854004 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52731', '-ss', '10000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.203561544418335}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '50545', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.0967555046081543}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     5,  ...,  9999,  9999, 10000]),
-       col_indices=tensor([1066, 3027, 5018,  ...,  516, 4404, 8191]),
-       values=tensor([0.9408, 0.5840, 0.0232,  ..., 0.8231, 0.8506, 0.7636]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([2369,  543, 2531,  ..., 9604, 4226, 7578]),
+       values=tensor([0.6904, 0.7524, 0.6457,  ..., 0.1985, 0.4076, 0.1702]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.2181, 0.5672, 0.6634,  ..., 0.2110, 0.3174, 0.6218])
+tensor([0.3802, 0.7278, 0.1089,  ..., 0.9979, 0.0210, 0.2529])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 2.203561544418335 seconds
+Time: 2.0967555046081543 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '251263', '-ss', '10000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.831458568572998}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '253116', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.375891208648682}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ...,  9997,  9999, 10000]),
-       col_indices=tensor([6180, 2035, 3071,  ...,  490, 6496, 2315]),
-       values=tensor([0.2073, 0.5439, 0.2551,  ..., 0.7953, 0.4550, 0.0057]),
+tensor(crow_indices=tensor([    0,     2,     3,  ...,  9998, 10000, 10000]),
+       col_indices=tensor([3651, 7635,  677,  ..., 4204, 2003, 9081]),
+       values=tensor([0.0091, 0.3889, 0.0620,  ..., 0.0091, 0.2713, 0.4199]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.4967, 0.8117, 0.4603,  ..., 0.8210, 0.9832, 0.0501])
+tensor([0.5126, 0.2678, 0.0619,  ..., 0.9520, 0.1902, 0.4690])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.831458568572998 seconds
+Time: 10.375891208648682 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ...,  9997,  9999, 10000]),
-       col_indices=tensor([6180, 2035, 3071,  ...,  490, 6496, 2315]),
-       values=tensor([0.2073, 0.5439, 0.2551,  ..., 0.7953, 0.4550, 0.0057]),
+tensor(crow_indices=tensor([    0,     2,     3,  ...,  9998, 10000, 10000]),
+       col_indices=tensor([3651, 7635,  677,  ..., 4204, 2003, 9081]),
+       values=tensor([0.0091, 0.3889, 0.0620,  ..., 0.0091, 0.2713, 0.4199]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.4967, 0.8117, 0.4603,  ..., 0.8210, 0.9832, 0.0501])
+tensor([0.5126, 0.2678, 0.0619,  ..., 0.9520, 0.1902, 0.4690])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.831458568572998 seconds
+Time: 10.375891208648682 seconds
 
-[18.25, 18.21, 18.06, 17.61, 18.2, 17.92, 18.19, 17.85, 18.22, 17.78]
-[74.33]
-13.924981832504272
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 251263, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.831458568572998, 'TIME_S_1KI': 0.04310805239359952, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1035.0438996100424, 'W': 74.32999999999998}
-[18.25, 18.21, 18.06, 17.61, 18.2, 17.92, 18.19, 17.85, 18.22, 17.78, 19.64, 17.79, 18.1, 18.15, 18.23, 17.78, 18.02, 18.14, 18.11, 17.9]
-325.365
-16.268250000000002
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 251263, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.831458568572998, 'TIME_S_1KI': 0.04310805239359952, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1035.0438996100424, 'W': 74.32999999999998, 'J_1KI': 4.119364568639403, 'W_1KI': 0.2958254896264073, 'W_D': 58.06174999999998, 'J_D': 808.5088139134047, 'W_D_1KI': 0.2310795859318721, 'J_D_1KI': 0.0009196721599752933}
+[19.24, 18.44, 19.49, 18.43, 18.88, 18.47, 18.56, 18.63, 18.72, 20.67]
+[82.7]
+14.243108987808228
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253116, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.375891208648682, 'TIME_S_1KI': 0.04099263266110669, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1177.9051132917405, 'W': 82.7}
+[19.24, 18.44, 19.49, 18.43, 18.88, 18.47, 18.56, 18.63, 18.72, 20.67, 18.97, 18.57, 18.9, 18.61, 18.67, 22.43, 19.29, 18.63, 19.39, 18.76]
+342.92999999999995
+17.146499999999996
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253116, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.375891208648682, 'TIME_S_1KI': 0.04099263266110669, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1177.9051132917405, 'W': 82.7, 'J_1KI': 4.653617761389009, 'W_1KI': 0.32672766636640904, 'W_D': 65.55350000000001, 'J_D': 933.6856450322869, 'W_D_1KI': 0.25898599851451515, 'J_D_1KI': 0.0010231909421550402}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.json
index 610ea48..76a952a 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 195195, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.530660390853882, "TIME_S_1KI": 0.05394943718258092, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1115.5897302937508, "W": 79.81, "J_1KI": 5.7152577181472415, "W_1KI": 0.40887317810394735, "W_D": 63.323, "J_D": 885.1332977244854, "W_D_1KI": 0.3244089244089244, "J_D_1KI": 0.0016619735362531027}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 192145, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.452706813812256, "TIME_S_1KI": 0.05440009791465954, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1239.9078391647338, "W": 88.07, "J_1KI": 6.452979984723692, "W_1KI": 0.45835176559369223, "W_D": 71.1265, "J_D": 1001.3660147876739, "W_D_1KI": 0.37017096463608207, "J_D_1KI": 0.00192651885105562}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.output
index d29559f..4a3271a 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.02070331573486328}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.021983623504638672}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     12,     18,  ...,  99984,  99988,
+tensor(crow_indices=tensor([     0,      7,     15,  ...,  99984,  99994,
                             100000]),
-       col_indices=tensor([ 729,  732,  881,  ..., 6002, 8211, 9107]),
-       values=tensor([0.1473, 0.0535, 0.1985,  ..., 0.7529, 0.2592, 0.5040]),
+       col_indices=tensor([ 435, 4137, 4610,  ..., 2205, 2567, 7007]),
+       values=tensor([0.9001, 0.7437, 0.7565,  ..., 0.0290, 0.0508, 0.5447]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7970, 0.9066, 0.2901,  ..., 0.5249, 0.8444, 0.0204])
+tensor([0.8078, 0.4866, 0.3722,  ..., 0.5482, 0.6717, 0.7643])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 0.02070331573486328 seconds
+Time: 0.021983623504638672 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '50716', '-ss', '10000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.728132724761963}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '47762', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.6100127696990967}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     15,     22,  ...,  99985,  99992,
+tensor(crow_indices=tensor([     0,      6,     13,  ...,  99978,  99986,
                             100000]),
-       col_indices=tensor([ 992, 2241, 2699,  ..., 7485, 9702, 9755]),
-       values=tensor([0.1587, 0.3354, 0.8907,  ..., 0.7458, 0.3952, 0.4445]),
+       col_indices=tensor([ 692, 1536, 1925,  ..., 6926, 7604, 9271]),
+       values=tensor([0.2408, 0.0654, 0.4638,  ..., 0.3016, 0.2922, 0.8002]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7531, 0.7793, 0.1410,  ..., 0.7186, 0.3031, 0.2892])
+tensor([0.4827, 0.2535, 0.7045,  ..., 0.0246, 0.7771, 0.7807])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 2.728132724761963 seconds
+Time: 2.6100127696990967 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '195195', '-ss', '10000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.530660390853882}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '192145', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.452706813812256}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     11,  ...,  99979,  99991,
+tensor(crow_indices=tensor([     0,     14,     27,  ...,  99983,  99991,
                             100000]),
-       col_indices=tensor([  74,  913, 1678,  ..., 8042, 8094, 8596]),
-       values=tensor([0.3009, 0.6152, 0.9919,  ..., 0.0065, 0.7111, 0.2350]),
+       col_indices=tensor([ 156,  519,  905,  ..., 8338, 9210, 9603]),
+       values=tensor([0.5955, 0.0542, 0.7725,  ..., 0.9735, 0.5428, 0.7094]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1186, 0.1112, 0.0471,  ..., 0.5653, 0.6270, 0.7376])
+tensor([0.3824, 0.6115, 0.2638,  ..., 0.8702, 0.5681, 0.8528])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.530660390853882 seconds
+Time: 10.452706813812256 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     11,  ...,  99979,  99991,
+tensor(crow_indices=tensor([     0,     14,     27,  ...,  99983,  99991,
                             100000]),
-       col_indices=tensor([  74,  913, 1678,  ..., 8042, 8094, 8596]),
-       values=tensor([0.3009, 0.6152, 0.9919,  ..., 0.0065, 0.7111, 0.2350]),
+       col_indices=tensor([ 156,  519,  905,  ..., 8338, 9210, 9603]),
+       values=tensor([0.5955, 0.0542, 0.7725,  ..., 0.9735, 0.5428, 0.7094]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1186, 0.1112, 0.0471,  ..., 0.5653, 0.6270, 0.7376])
+tensor([0.3824, 0.6115, 0.2638,  ..., 0.8702, 0.5681, 0.8528])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.530660390853882 seconds
+Time: 10.452706813812256 seconds
 
-[18.85, 21.66, 18.61, 18.04, 18.26, 18.19, 18.11, 17.83, 18.59, 17.86]
-[79.81]
-13.978069543838501
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 195195, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.530660390853882, 'TIME_S_1KI': 0.05394943718258092, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1115.5897302937508, 'W': 79.81}
-[18.85, 21.66, 18.61, 18.04, 18.26, 18.19, 18.11, 17.83, 18.59, 17.86, 18.27, 17.86, 18.11, 17.94, 18.2, 17.78, 18.07, 17.82, 17.85, 18.66]
-329.74
-16.487000000000002
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 195195, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.530660390853882, 'TIME_S_1KI': 0.05394943718258092, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1115.5897302937508, 'W': 79.81, 'J_1KI': 5.7152577181472415, 'W_1KI': 0.40887317810394735, 'W_D': 63.323, 'J_D': 885.1332977244854, 'W_D_1KI': 0.3244089244089244, 'J_D_1KI': 0.0016619735362531027}
+[18.87, 18.51, 19.31, 18.76, 18.76, 18.65, 19.3, 18.75, 18.8, 18.73]
+[88.07]
+14.078662872314453
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 192145, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.452706813812256, 'TIME_S_1KI': 0.05440009791465954, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1239.9078391647338, 'W': 88.07}
+[18.87, 18.51, 19.31, 18.76, 18.76, 18.65, 19.3, 18.75, 18.8, 18.73, 19.62, 18.55, 18.88, 18.66, 18.93, 18.62, 18.78, 18.63, 19.05, 18.64]
+338.87
+16.9435
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 192145, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.452706813812256, 'TIME_S_1KI': 0.05440009791465954, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1239.9078391647338, 'W': 88.07, 'J_1KI': 6.452979984723692, 'W_1KI': 0.45835176559369223, 'W_D': 71.1265, 'J_D': 1001.3660147876739, 'W_D_1KI': 0.37017096463608207, 'J_D_1KI': 0.00192651885105562}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.json
index 9a2b891..3ad103d 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 58200, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.524534702301025, "TIME_S_1KI": 0.18083392959280112, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1230.7857535743713, "W": 87.63, "J_1KI": 21.147521539078543, "W_1KI": 1.5056701030927835, "W_D": 71.18625, "J_D": 999.8290807986259, "W_D_1KI": 1.223131443298969, "J_D_1KI": 0.02101600418039466}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 57161, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.708652973175049, "TIME_S_1KI": 0.1873419459627202, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1288.4431428408623, "W": 90.87, "J_1KI": 22.540598359735874, "W_1KI": 1.5897202638162384, "W_D": 62.09775, "J_D": 880.4822292653918, "W_D_1KI": 1.086365703889015, "J_D_1KI": 0.01900536561447517}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.output
index b0a27e2..c398e44 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.output
@@ -1,14 +1,34 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.032953500747680664}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.034076690673828125}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      87,     187,  ...,  999811,
+tensor(crow_indices=tensor([      0,     107,     208,  ...,  999803,
+                             999917, 1000000]),
+       col_indices=tensor([  77,  189,  287,  ..., 9483, 9488, 9787]),
+       values=tensor([0.8729, 0.2143, 0.4098,  ..., 0.2067, 0.2304, 0.4743]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8489, 0.8735, 0.2882,  ..., 0.7921, 0.8211, 0.7022])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.034076690673828125 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '30812', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 5.659840822219849}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      89,     189,  ...,  999802,
                              999895, 1000000]),
-       col_indices=tensor([  60,  162,  170,  ..., 9440, 9828, 9931]),
-       values=tensor([0.9691, 0.2545, 0.9233,  ..., 0.5616, 0.3084, 0.5234]),
+       col_indices=tensor([  64,   86,  225,  ..., 9919, 9935, 9940]),
+       values=tensor([0.6271, 0.3388, 0.3681,  ..., 0.0346, 0.0873, 0.5226]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0498, 0.1923, 0.6628,  ..., 0.9993, 0.6267, 0.7810])
+tensor([0.1570, 0.5305, 0.5278,  ..., 0.7249, 0.7100, 0.4427])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,39 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 0.032953500747680664 seconds
+Time: 5.659840822219849 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '31863', '-ss', '10000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 5.748403549194336}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '57161', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.708652973175049}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     110,     213,  ...,  999801,
-                             999901, 1000000]),
-       col_indices=tensor([  77,  119,  129,  ..., 9737, 9950, 9990]),
-       values=tensor([0.4475, 0.9311, 0.1906,  ..., 0.1630, 0.9417, 0.6731]),
-       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2219, 0.3377, 0.8817,  ..., 0.6372, 0.3631, 0.6898])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000000
-Density: 0.01
-Time: 5.748403549194336 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '58200', '-ss', '10000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.524534702301025}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     116,     213,  ...,  999804,
+tensor(crow_indices=tensor([      0,     110,     202,  ...,  999807,
                              999907, 1000000]),
-       col_indices=tensor([  96,  100,  135,  ..., 9713, 9783, 9969]),
-       values=tensor([0.2374, 0.5111, 0.2281,  ..., 0.8006, 0.1634, 0.0785]),
+       col_indices=tensor([  52,   75,   82,  ..., 9616, 9679, 9992]),
+       values=tensor([0.7610, 0.2228, 0.0334,  ..., 0.9210, 0.2713, 0.2170]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4187, 0.6286, 0.6485,  ..., 0.1996, 0.5955, 0.8769])
+tensor([0.2927, 0.9778, 0.1836,  ..., 0.2052, 0.7640, 0.7077])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.524534702301025 seconds
+Time: 10.708652973175049 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     116,     213,  ...,  999804,
+tensor(crow_indices=tensor([      0,     110,     202,  ...,  999807,
                              999907, 1000000]),
-       col_indices=tensor([  96,  100,  135,  ..., 9713, 9783, 9969]),
-       values=tensor([0.2374, 0.5111, 0.2281,  ..., 0.8006, 0.1634, 0.0785]),
+       col_indices=tensor([  52,   75,   82,  ..., 9616, 9679, 9992]),
+       values=tensor([0.7610, 0.2228, 0.0334,  ..., 0.9210, 0.2713, 0.2170]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4187, 0.6286, 0.6485,  ..., 0.1996, 0.5955, 0.8769])
+tensor([0.2927, 0.9778, 0.1836,  ..., 0.2052, 0.7640, 0.7077])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.524534702301025 seconds
+Time: 10.708652973175049 seconds
 
-[18.15, 17.92, 18.14, 18.3, 18.24, 17.98, 17.94, 18.28, 17.96, 17.99]
-[87.63]
-14.045255661010742
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58200, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.524534702301025, 'TIME_S_1KI': 0.18083392959280112, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1230.7857535743713, 'W': 87.63}
-[18.15, 17.92, 18.14, 18.3, 18.24, 17.98, 17.94, 18.28, 17.96, 17.99, 18.28, 18.07, 17.88, 17.88, 17.9, 18.02, 21.74, 18.02, 18.41, 17.97]
-328.875
-16.44375
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58200, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.524534702301025, 'TIME_S_1KI': 0.18083392959280112, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1230.7857535743713, 'W': 87.63, 'J_1KI': 21.147521539078543, 'W_1KI': 1.5056701030927835, 'W_D': 71.18625, 'J_D': 999.8290807986259, 'W_D_1KI': 1.223131443298969, 'J_D_1KI': 0.02101600418039466}
+[50.63, 43.53, 43.67, 43.68, 44.99, 46.29, 46.23, 45.2, 45.28, 44.72]
+[90.87]
+14.178971529006958
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 57161, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.708652973175049, 'TIME_S_1KI': 0.1873419459627202, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1288.4431428408623, 'W': 90.87}
+[50.63, 43.53, 43.67, 43.68, 44.99, 46.29, 46.23, 45.2, 45.28, 44.72, 18.99, 18.86, 18.72, 18.73, 18.8, 18.58, 18.67, 18.89, 18.72, 18.87]
+575.4450000000002
+28.772250000000007
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 57161, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.708652973175049, 'TIME_S_1KI': 0.1873419459627202, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1288.4431428408623, 'W': 90.87, 'J_1KI': 22.540598359735874, 'W_1KI': 1.5897202638162384, 'W_D': 62.09775, 'J_D': 880.4822292653918, 'W_D_1KI': 1.086365703889015, 'J_D_1KI': 0.01900536561447517}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.json
index 6f3f4c5..5df6b87 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8756, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.44620156288147, "TIME_S_1KI": 1.193033527053617, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1333.577045211792, "W": 83.47, "J_1KI": 152.30436788622566, "W_1KI": 9.532891731384193, "W_D": 66.97425, "J_D": 1070.0290214481354, "W_D_1KI": 7.648955002284148, "J_D_1KI": 0.8735672684198433}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8729, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.783016443252563, "TIME_S_1KI": 1.2353094791216135, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1352.3832885718346, "W": 87.53, "J_1KI": 154.9299219351397, "W_1KI": 10.027494558368657, "W_D": 70.42325, "J_D": 1088.075247651279, "W_D_1KI": 8.067733990147781, "J_D_1KI": 0.924244929562124}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.output
index e9fd20d..23aad01 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.14447855949401855}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.13656830787658691}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     507,    1042,  ..., 4998981,
-                            4999514, 5000000]),
-       col_indices=tensor([   8,   38,   72,  ..., 9951, 9971, 9980]),
-       values=tensor([0.6058, 0.5976, 0.8000,  ..., 0.1658, 0.6430, 0.8003]),
+tensor(crow_indices=tensor([      0,     509,    1006,  ..., 4999003,
+                            4999502, 5000000]),
+       col_indices=tensor([   3,    5,   16,  ..., 9942, 9959, 9980]),
+       values=tensor([0.0344, 0.2328, 0.6267,  ..., 0.4030, 0.4110, 0.2937]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.9071, 0.4156, 0.9536,  ..., 0.8291, 0.1377, 0.0392])
+tensor([0.6327, 0.2020, 0.0705,  ..., 0.9187, 0.0315, 0.1283])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 0.14447855949401855 seconds
+Time: 0.13656830787658691 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7267', '-ss', '10000', '-sd', '0.05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 8.714413404464722}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7688', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.24746060371399}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     473,     945,  ..., 4999048,
-                            4999511, 5000000]),
-       col_indices=tensor([   4,   25,   47,  ..., 9937, 9967, 9993]),
-       values=tensor([0.2180, 0.8351, 0.6646,  ..., 0.1409, 0.2302, 0.7325]),
+tensor(crow_indices=tensor([      0,     516,    1004,  ..., 4998937,
+                            4999450, 5000000]),
+       col_indices=tensor([   6,   10,   16,  ..., 9961, 9963, 9994]),
+       values=tensor([0.1497, 0.2572, 0.1920,  ..., 0.0397, 0.6799, 0.4086]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.9797, 0.6380, 0.6196,  ..., 0.0914, 0.5364, 0.9534])
+tensor([0.8056, 0.3224, 0.7213,  ..., 0.7847, 0.3449, 0.8828])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 8.714413404464722 seconds
+Time: 9.24746060371399 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8756', '-ss', '10000', '-sd', '0.05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.44620156288147}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8729', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.783016443252563}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     484,    1014,  ..., 4998952,
-                            4999472, 5000000]),
-       col_indices=tensor([  28,   62,   89,  ..., 9928, 9935, 9940]),
-       values=tensor([0.3908, 0.2484, 0.4500,  ..., 0.3668, 0.3711, 0.8718]),
+tensor(crow_indices=tensor([      0,     513,    1011,  ..., 4999049,
+                            4999518, 5000000]),
+       col_indices=tensor([  19,   50,   54,  ..., 9882, 9895, 9931]),
+       values=tensor([0.6614, 0.3405, 0.4062,  ..., 0.2762, 0.6907, 0.8766]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.8601, 0.7620, 0.0732,  ..., 0.0545, 0.3750, 0.1934])
+tensor([0.3093, 0.3070, 0.7107,  ..., 0.6329, 0.2063, 0.9404])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.44620156288147 seconds
+Time: 10.783016443252563 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     484,    1014,  ..., 4998952,
-                            4999472, 5000000]),
-       col_indices=tensor([  28,   62,   89,  ..., 9928, 9935, 9940]),
-       values=tensor([0.3908, 0.2484, 0.4500,  ..., 0.3668, 0.3711, 0.8718]),
+tensor(crow_indices=tensor([      0,     513,    1011,  ..., 4999049,
+                            4999518, 5000000]),
+       col_indices=tensor([  19,   50,   54,  ..., 9882, 9895, 9931]),
+       values=tensor([0.6614, 0.3405, 0.4062,  ..., 0.2762, 0.6907, 0.8766]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.8601, 0.7620, 0.0732,  ..., 0.0545, 0.3750, 0.1934])
+tensor([0.3093, 0.3070, 0.7107,  ..., 0.6329, 0.2063, 0.9404])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.44620156288147 seconds
+Time: 10.783016443252563 seconds
 
-[18.13, 18.04, 18.48, 17.86, 18.0, 18.3, 18.04, 17.84, 17.94, 18.14]
-[83.47]
-15.976722717285156
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8756, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.44620156288147, 'TIME_S_1KI': 1.193033527053617, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1333.577045211792, 'W': 83.47}
-[18.13, 18.04, 18.48, 17.86, 18.0, 18.3, 18.04, 17.84, 17.94, 18.14, 18.29, 18.21, 17.99, 18.15, 21.9, 18.49, 18.31, 17.94, 18.08, 18.13]
-329.91499999999996
-16.495749999999997
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8756, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.44620156288147, 'TIME_S_1KI': 1.193033527053617, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1333.577045211792, 'W': 83.47, 'J_1KI': 152.30436788622566, 'W_1KI': 9.532891731384193, 'W_D': 66.97425, 'J_D': 1070.0290214481354, 'W_D_1KI': 7.648955002284148, 'J_D_1KI': 0.8735672684198433}
+[19.4, 22.3, 19.22, 18.54, 19.29, 18.66, 18.82, 18.66, 18.68, 18.54]
+[87.53]
+15.450511693954468
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8729, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.783016443252563, 'TIME_S_1KI': 1.2353094791216135, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1352.3832885718346, 'W': 87.53}
+[19.4, 22.3, 19.22, 18.54, 19.29, 18.66, 18.82, 18.66, 18.68, 18.54, 19.2, 18.66, 18.77, 18.65, 18.7, 18.7, 18.9, 18.67, 19.02, 18.65]
+342.13500000000005
+17.10675
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8729, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.783016443252563, 'TIME_S_1KI': 1.2353094791216135, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1352.3832885718346, 'W': 87.53, 'J_1KI': 154.9299219351397, 'W_1KI': 10.027494558368657, 'W_D': 70.42325, 'J_D': 1088.075247651279, 'W_D_1KI': 8.067733990147781, 'J_D_1KI': 0.924244929562124}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json
index 646ac12..20e810f 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2958, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.525683641433716, "TIME_S_1KI": 3.558378512993143, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1502.5020074129104, "W": 79.35, "J_1KI": 507.94523577177495, "W_1KI": 26.825557809330626, "W_D": 62.803999999999995, "J_D": 1189.2014628047941, "W_D_1KI": 21.231913455037187, "J_D_1KI": 7.177793595347258}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2874, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.399726152420044, "TIME_S_1KI": 3.9665017927696744, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1398.2346516990663, "W": 84.76, "J_1KI": 486.51170901150533, "W_1KI": 29.491997216423105, "W_D": 67.9355, "J_D": 1120.6910120399, "W_D_1KI": 23.637961029923453, "J_D_1KI": 8.224760274851583}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output
index cd10b80..79c2bf9 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.38048672676086426}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.40368223190307617}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      978,     2001,  ...,  9997917,
-                             9998993, 10000000]),
-       col_indices=tensor([  10,   12,   31,  ..., 9968, 9976, 9993]),
-       values=tensor([0.1521, 0.7718, 0.5784,  ..., 0.3138, 0.0420, 0.0283]),
+tensor(crow_indices=tensor([       0,      987,     1967,  ...,  9997992,
+                             9999008, 10000000]),
+       col_indices=tensor([   7,   16,   26,  ..., 9960, 9968, 9979]),
+       values=tensor([0.6123, 0.2290, 0.8484,  ..., 0.2566, 0.4522, 0.9117]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8445, 0.5776, 0.6277,  ..., 0.5230, 0.9454, 0.0151])
+tensor([0.9379, 0.7537, 0.6942,  ..., 0.2092, 0.6278, 0.4526])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 0.38048672676086426 seconds
+Time: 0.40368223190307617 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2759', '-ss', '10000', '-sd', '0.1', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.790999174118042}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2601', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.502490997314453}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      961,     1957,  ...,  9997943,
-                             9998964, 10000000]),
-       col_indices=tensor([   0,    2,   15,  ..., 9987, 9990, 9997]),
-       values=tensor([0.8878, 0.8149, 0.0468,  ..., 0.0944, 0.2051, 0.2941]),
+tensor(crow_indices=tensor([       0,     1026,     2004,  ...,  9997979,
+                             9999022, 10000000]),
+       col_indices=tensor([   4,   15,   19,  ..., 9953, 9954, 9967]),
+       values=tensor([0.9619, 0.8012, 0.0780,  ..., 0.6886, 0.8242, 0.5458]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8720, 0.1434, 0.3774,  ..., 0.9472, 0.6076, 0.2537])
+tensor([0.2108, 0.1126, 0.9693,  ..., 0.1000, 0.3114, 0.1665])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 9.790999174118042 seconds
+Time: 9.502490997314453 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2958', '-ss', '10000', '-sd', '0.1', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.525683641433716}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2874', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.399726152420044}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1002,     2060,  ...,  9998101,
-                             9999084, 10000000]),
-       col_indices=tensor([   6,   12,   22,  ..., 9993, 9996, 9999]),
-       values=tensor([0.4647, 0.6377, 0.7581,  ..., 0.1422, 0.4549, 0.7257]),
+tensor(crow_indices=tensor([       0,      993,     2025,  ...,  9997969,
+                             9999011, 10000000]),
+       col_indices=tensor([   6,   13,   15,  ..., 9973, 9981, 9991]),
+       values=tensor([0.2056, 0.1415, 0.5151,  ..., 0.4464, 0.8622, 0.1462]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.2726, 0.9874, 0.6365,  ..., 0.6635, 0.0461, 0.2273])
+tensor([0.0942, 0.3792, 0.4904,  ..., 0.5152, 0.5684, 0.8787])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 10.525683641433716 seconds
+Time: 11.399726152420044 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1002,     2060,  ...,  9998101,
-                             9999084, 10000000]),
-       col_indices=tensor([   6,   12,   22,  ..., 9993, 9996, 9999]),
-       values=tensor([0.4647, 0.6377, 0.7581,  ..., 0.1422, 0.4549, 0.7257]),
+tensor(crow_indices=tensor([       0,      993,     2025,  ...,  9997969,
+                             9999011, 10000000]),
+       col_indices=tensor([   6,   13,   15,  ..., 9973, 9981, 9991]),
+       values=tensor([0.2056, 0.1415, 0.5151,  ..., 0.4464, 0.8622, 0.1462]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.2726, 0.9874, 0.6365,  ..., 0.6635, 0.0461, 0.2273])
+tensor([0.0942, 0.3792, 0.4904,  ..., 0.5152, 0.5684, 0.8787])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 10.525683641433716 seconds
+Time: 11.399726152420044 seconds
 
-[18.46, 22.29, 18.08, 17.85, 18.08, 18.14, 17.95, 17.97, 18.17, 18.1]
-[79.35]
-18.935122966766357
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2958, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.525683641433716, 'TIME_S_1KI': 3.558378512993143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.5020074129104, 'W': 79.35}
-[18.46, 22.29, 18.08, 17.85, 18.08, 18.14, 17.95, 17.97, 18.17, 18.1, 18.4, 17.86, 18.25, 18.05, 17.97, 17.82, 20.01, 18.07, 17.96, 17.84]
-330.91999999999996
-16.546
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2958, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.525683641433716, 'TIME_S_1KI': 3.558378512993143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.5020074129104, 'W': 79.35, 'J_1KI': 507.94523577177495, 'W_1KI': 26.825557809330626, 'W_D': 62.803999999999995, 'J_D': 1189.2014628047941, 'W_D_1KI': 21.231913455037187, 'J_D_1KI': 7.177793595347258}
+[19.05, 18.99, 18.84, 18.46, 18.61, 18.71, 18.6, 18.73, 18.61, 19.17]
+[84.76]
+16.496397495269775
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2874, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.399726152420044, 'TIME_S_1KI': 3.9665017927696744, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1398.2346516990663, 'W': 84.76}
+[19.05, 18.99, 18.84, 18.46, 18.61, 18.71, 18.6, 18.73, 18.61, 19.17, 19.07, 18.75, 18.82, 18.42, 19.03, 18.69, 18.45, 18.24, 18.54, 18.71]
+336.48999999999995
+16.824499999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2874, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.399726152420044, 'TIME_S_1KI': 3.9665017927696744, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1398.2346516990663, 'W': 84.76, 'J_1KI': 486.51170901150533, 'W_1KI': 29.491997216423105, 'W_D': 67.9355, 'J_D': 1120.6910120399, 'W_D_1KI': 23.637961029923453, 'J_D_1KI': 8.224760274851583}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json
index 600b3de..201d3d0 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1431, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.125213623046875, "TIME_S_1KI": 7.07562098046602, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1986.3526240086555, "W": 63.01, "J_1KI": 1388.087088755175, "W_1KI": 44.032145352900066, "W_D": 46.759, "J_D": 1474.0495531823635, "W_D_1KI": 32.67575122292103, "J_D_1KI": 22.83420770294971}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1479, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.389569997787476, "TIME_S_1KI": 7.024726164832641, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1529.2858192181588, "W": 80.81, "J_1KI": 1033.999877767518, "W_1KI": 54.63826910074375, "W_D": 63.61325000000001, "J_D": 1203.846567743838, "W_D_1KI": 43.01098715348209, "J_D_1KI": 29.081127216688362}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output
index b3d4b08..19322b1 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.7335808277130127}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.709862232208252}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1938,     3929,  ..., 19996007,
-                            19998018, 20000000]),
-       col_indices=tensor([  10,   23,   25,  ..., 9992, 9994, 9995]),
-       values=tensor([0.0730, 0.5628, 0.7699,  ..., 0.2806, 0.9097, 0.3889]),
+tensor(crow_indices=tensor([       0,     1995,     3996,  ..., 19995943,
+                            19997984, 20000000]),
+       col_indices=tensor([   1,   15,   31,  ..., 9973, 9977, 9994]),
+       values=tensor([0.9351, 0.4585, 0.8626,  ..., 0.1490, 0.3791, 0.5352]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.2309, 0.6404, 0.8370,  ..., 0.6670, 0.0943, 0.6898])
+tensor([0.1274, 0.7816, 0.0225,  ..., 0.9691, 0.3129, 0.2104])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 0.7335808277130127 seconds
+Time: 0.709862232208252 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1431', '-ss', '10000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.125213623046875}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1479', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.389569997787476}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2012,     4019,  ..., 19995967,
-                            19998000, 20000000]),
-       col_indices=tensor([   4,    7,    8,  ..., 9979, 9980, 9988]),
-       values=tensor([0.3444, 0.9651, 0.7506,  ..., 0.6074, 0.5252, 0.1862]),
+tensor(crow_indices=tensor([       0,     2030,     3992,  ..., 19996023,
+                            19997977, 20000000]),
+       col_indices=tensor([   0,    4,   14,  ..., 9988, 9997, 9999]),
+       values=tensor([0.5125, 0.9592, 0.8688,  ..., 0.5338, 0.8764, 0.3338]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.7493, 0.9404, 0.6976,  ..., 0.3307, 0.3774, 0.7329])
+tensor([0.9961, 0.7654, 0.4620,  ..., 0.1667, 0.2329, 0.0326])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 10.125213623046875 seconds
+Time: 10.389569997787476 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2012,     4019,  ..., 19995967,
-                            19998000, 20000000]),
-       col_indices=tensor([   4,    7,    8,  ..., 9979, 9980, 9988]),
-       values=tensor([0.3444, 0.9651, 0.7506,  ..., 0.6074, 0.5252, 0.1862]),
+tensor(crow_indices=tensor([       0,     2030,     3992,  ..., 19996023,
+                            19997977, 20000000]),
+       col_indices=tensor([   0,    4,   14,  ..., 9988, 9997, 9999]),
+       values=tensor([0.5125, 0.9592, 0.8688,  ..., 0.5338, 0.8764, 0.3338]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.7493, 0.9404, 0.6976,  ..., 0.3307, 0.3774, 0.7329])
+tensor([0.9961, 0.7654, 0.4620,  ..., 0.1667, 0.2329, 0.0326])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 10.125213623046875 seconds
+Time: 10.389569997787476 seconds
 
-[18.03, 17.87, 18.44, 17.79, 18.02, 17.71, 17.95, 17.85, 17.83, 17.74]
-[63.01]
-31.524402856826782
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1431, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.125213623046875, 'TIME_S_1KI': 7.07562098046602, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1986.3526240086555, 'W': 63.01}
-[18.03, 17.87, 18.44, 17.79, 18.02, 17.71, 17.95, 17.85, 17.83, 17.74, 18.68, 17.68, 18.59, 19.66, 17.91, 17.67, 18.09, 18.04, 17.79, 17.81]
-325.02
-16.250999999999998
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1431, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.125213623046875, 'TIME_S_1KI': 7.07562098046602, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1986.3526240086555, 'W': 63.01, 'J_1KI': 1388.087088755175, 'W_1KI': 44.032145352900066, 'W_D': 46.759, 'J_D': 1474.0495531823635, 'W_D_1KI': 32.67575122292103, 'J_D_1KI': 22.83420770294971}
+[18.82, 18.73, 24.37, 19.34, 18.75, 19.29, 18.77, 18.45, 18.74, 18.59]
+[80.81]
+18.92446255683899
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1479, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.389569997787476, 'TIME_S_1KI': 7.024726164832641, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1529.2858192181588, 'W': 80.81}
+[18.82, 18.73, 24.37, 19.34, 18.75, 19.29, 18.77, 18.45, 18.74, 18.59, 18.95, 18.48, 19.06, 18.92, 18.82, 18.63, 19.11, 18.42, 18.59, 18.57]
+343.93499999999995
+17.196749999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1479, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.389569997787476, 'TIME_S_1KI': 7.024726164832641, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1529.2858192181588, 'W': 80.81, 'J_1KI': 1033.999877767518, 'W_1KI': 54.63826910074375, 'W_D': 63.61325000000001, 'J_D': 1203.846567743838, 'W_D_1KI': 43.01098715348209, 'J_D_1KI': 29.081127216688362}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json
index 913ab31..a29eca9 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 887, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.198672533035278, "TIME_S_1KI": 11.497939721573031, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3521.5318365383146, "W": 51.94, "J_1KI": 3970.1599059056534, "W_1KI": 58.55693348365276, "W_D": 35.649249999999995, "J_D": 2417.019037807345, "W_D_1KI": 40.19081172491544, "J_D_1KI": 45.31094895706363}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 998, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.352726221084595, "TIME_S_1KI": 10.373473167419434, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1634.5226301193238, "W": 77.59, "J_1KI": 1637.7982265724686, "W_1KI": 77.74549098196393, "W_D": 60.511500000000005, "J_D": 1274.7443759822847, "W_D_1KI": 60.63276553106213, "J_D_1KI": 60.754274079220565}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output
index 697edf6..74b4d68 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.9959039688110352}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.0511415004730225}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2964,     5964,  ..., 29994054,
-                            29997049, 30000000]),
-       col_indices=tensor([   0,    6,    7,  ..., 9989, 9993, 9996]),
-       values=tensor([0.3352, 0.3012, 0.1376,  ..., 0.4634, 0.9038, 0.2157]),
+tensor(crow_indices=tensor([       0,     3013,     6009,  ..., 29993988,
+                            29997010, 30000000]),
+       col_indices=tensor([   3,   10,   11,  ..., 9994, 9996, 9998]),
+       values=tensor([0.5882, 0.0748, 0.3485,  ..., 0.9795, 0.2531, 0.2438]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.3407, 0.2089, 0.1462,  ..., 0.7488, 0.0030, 0.5159])
+tensor([0.3271, 0.7082, 0.9239,  ..., 0.2840, 0.5244, 0.2057])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 1.9959039688110352 seconds
+Time: 1.0511415004730225 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '526', '-ss', '10000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 7.3105573654174805}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '998', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.352726221084595}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2980,     5935,  ..., 29993957,
-                            29996987, 30000000]),
-       col_indices=tensor([   2,    4,    5,  ..., 9985, 9987, 9990]),
-       values=tensor([0.7275, 0.2529, 0.2202,  ..., 0.8048, 0.1786, 0.5578]),
+tensor(crow_indices=tensor([       0,     2925,     5880,  ..., 29993881,
+                            29996945, 30000000]),
+       col_indices=tensor([   7,    9,   15,  ..., 9991, 9992, 9996]),
+       values=tensor([0.5335, 0.0122, 0.5796,  ..., 0.5217, 0.7536, 0.5371]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.3722, 0.8340, 0.1775,  ..., 0.2787, 0.3419, 0.3614])
+tensor([0.2204, 0.0258, 0.7235,  ..., 0.5781, 0.5536, 0.8900])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 7.3105573654174805 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '755', '-ss', '10000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 8.937042713165283}
+Time: 10.352726221084595 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     3082,     6089,  ..., 29994069,
-                            29996991, 30000000]),
-       col_indices=tensor([   3,    5,    7,  ..., 9989, 9990, 9999]),
-       values=tensor([0.1400, 0.4959, 0.3565,  ..., 0.5786, 0.8662, 0.8079]),
+tensor(crow_indices=tensor([       0,     2925,     5880,  ..., 29993881,
+                            29996945, 30000000]),
+       col_indices=tensor([   7,    9,   15,  ..., 9991, 9992, 9996]),
+       values=tensor([0.5335, 0.0122, 0.5796,  ..., 0.5217, 0.7536, 0.5371]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.3795, 0.0549, 0.6386,  ..., 0.9156, 0.7490, 0.5099])
+tensor([0.2204, 0.0258, 0.7235,  ..., 0.5781, 0.5536, 0.8900])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,50 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 8.937042713165283 seconds
+Time: 10.352726221084595 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '887', '-ss', '10000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.198672533035278}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     3035,     5994,  ..., 29994113,
-                            29997092, 30000000]),
-       col_indices=tensor([   5,   10,   14,  ..., 9987, 9996, 9999]),
-       values=tensor([0.8098, 0.4554, 0.6671,  ..., 0.4349, 0.8044, 0.2223]),
-       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.3423, 0.6279, 0.0055,  ..., 0.7400, 0.5417, 0.1422])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 30000000
-Density: 0.3
-Time: 10.198672533035278 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     3035,     5994,  ..., 29994113,
-                            29997092, 30000000]),
-       col_indices=tensor([   5,   10,   14,  ..., 9987, 9996, 9999]),
-       values=tensor([0.8098, 0.4554, 0.6671,  ..., 0.4349, 0.8044, 0.2223]),
-       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.3423, 0.6279, 0.0055,  ..., 0.7400, 0.5417, 0.1422])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 30000000
-Density: 0.3
-Time: 10.198672533035278 seconds
-
-[18.62, 17.81, 18.39, 18.03, 17.98, 18.26, 18.01, 18.0, 17.9, 17.68]
-[51.94]
-67.79999685287476
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 887, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.198672533035278, 'TIME_S_1KI': 11.497939721573031, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3521.5318365383146, 'W': 51.94}
-[18.62, 17.81, 18.39, 18.03, 17.98, 18.26, 18.01, 18.0, 17.9, 17.68, 18.52, 17.91, 18.3, 18.31, 18.44, 18.04, 17.96, 18.17, 17.99, 17.81]
-325.815
-16.29075
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 887, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.198672533035278, 'TIME_S_1KI': 11.497939721573031, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3521.5318365383146, 'W': 51.94, 'J_1KI': 3970.1599059056534, 'W_1KI': 58.55693348365276, 'W_D': 35.649249999999995, 'J_D': 2417.019037807345, 'W_D_1KI': 40.19081172491544, 'J_D_1KI': 45.31094895706363}
+[20.76, 18.85, 19.04, 18.56, 18.79, 18.54, 18.88, 18.6, 18.72, 18.53]
+[77.59]
+21.066150665283203
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 998, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.352726221084595, 'TIME_S_1KI': 10.373473167419434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1634.5226301193238, 'W': 77.59}
+[20.76, 18.85, 19.04, 18.56, 18.79, 18.54, 18.88, 18.6, 18.72, 18.53, 19.33, 18.53, 18.72, 18.46, 18.58, 18.72, 18.36, 18.5, 18.43, 27.96]
+341.57
+17.0785
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 998, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.352726221084595, 'TIME_S_1KI': 10.373473167419434, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1634.5226301193238, 'W': 77.59, 'J_1KI': 1637.7982265724686, 'W_1KI': 77.74549098196393, 'W_D': 60.511500000000005, 'J_D': 1274.7443759822847, 'W_D_1KI': 60.63276553106213, 'J_D_1KI': 60.754274079220565}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..c3c441f
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 785, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.66882610321045, "TIME_S_1KI": 13.590861277975096, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1798.7167282104492, "W": 75.63, "J_1KI": 2291.3588894400627, "W_1KI": 96.34394904458598, "W_D": 58.558, "J_D": 1392.6914474487305, "W_D_1KI": 74.59617834394905, "J_D_1KI": 95.0269787821007}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..670751b
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.4.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 1.3374521732330322}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3951,     7984,  ..., 39992020,
+                            39996017, 40000000]),
+       col_indices=tensor([   4,    5,   12,  ..., 9995, 9997, 9999]),
+       values=tensor([0.6637, 0.1161, 0.0076,  ..., 0.1828, 0.4930, 0.5063]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.4339, 0.0820, 0.1001,  ..., 0.8568, 0.2207, 0.1791])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 1.3374521732330322 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '785', '-ss', '10000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.66882610321045}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4024,     7985,  ..., 39991914,
+                            39996029, 40000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9994, 9995, 9996]),
+       values=tensor([0.8779, 0.6492, 0.8537,  ..., 0.0562, 0.0638, 0.0669]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.8660, 0.7163, 0.6180,  ..., 0.0766, 0.1126, 0.5921])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 10.66882610321045 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4024,     7985,  ..., 39991914,
+                            39996029, 40000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9994, 9995, 9996]),
+       values=tensor([0.8779, 0.6492, 0.8537,  ..., 0.0562, 0.0638, 0.0669]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.8660, 0.7163, 0.6180,  ..., 0.0766, 0.1126, 0.5921])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 10.66882610321045 seconds
+
+[18.79, 18.81, 18.54, 18.57, 18.41, 18.56, 18.57, 18.37, 18.57, 18.5]
+[75.63]
+23.783111572265625
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 785, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.66882610321045, 'TIME_S_1KI': 13.590861277975096, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1798.7167282104492, 'W': 75.63}
+[18.79, 18.81, 18.54, 18.57, 18.41, 18.56, 18.57, 18.37, 18.57, 18.5, 19.16, 18.66, 18.58, 18.42, 19.27, 18.85, 22.54, 20.35, 18.51, 19.27]
+341.43999999999994
+17.071999999999996
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 785, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.66882610321045, 'TIME_S_1KI': 13.590861277975096, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1798.7167282104492, 'W': 75.63, 'J_1KI': 2291.3588894400627, 'W_1KI': 96.34394904458598, 'W_D': 58.558, 'J_D': 1392.6914474487305, 'W_D_1KI': 74.59617834394905, 'J_D_1KI': 95.0269787821007}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..b0932eb
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 636, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.683557987213135, "TIME_S_1KI": 16.79804714970619, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1927.4814306020737, "W": 73.89, "J_1KI": 3030.6311801919396, "W_1KI": 116.17924528301887, "W_D": 56.59825, "J_D": 1476.4119079655409, "W_D_1KI": 88.99095911949685, "J_D_1KI": 139.9228916973221}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..39e266d
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_0.5.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 1.6492679119110107}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4994,    10028,  ..., 49990032,
+                            49995032, 50000000]),
+       col_indices=tensor([   0,    1,    4,  ..., 9995, 9998, 9999]),
+       values=tensor([0.1973, 0.5130, 0.6236,  ..., 0.3108, 0.1154, 0.1821]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.8813, 0.9082, 0.9703,  ..., 0.1753, 0.3020, 0.5225])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 1.6492679119110107 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '636', '-ss', '10000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.683557987213135}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     5002,    10029,  ..., 49990100,
+                            49995028, 50000000]),
+       col_indices=tensor([   6,    9,   11,  ..., 9997, 9998, 9999]),
+       values=tensor([0.8163, 0.5188, 0.6591,  ..., 0.6776, 0.3373, 0.8589]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.7220, 0.4730, 0.7069,  ..., 0.0189, 0.3508, 0.7316])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 10.683557987213135 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     5002,    10029,  ..., 49990100,
+                            49995028, 50000000]),
+       col_indices=tensor([   6,    9,   11,  ..., 9997, 9998, 9999]),
+       values=tensor([0.8163, 0.5188, 0.6591,  ..., 0.6776, 0.3373, 0.8589]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.7220, 0.4730, 0.7069,  ..., 0.0189, 0.3508, 0.7316])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 10.683557987213135 seconds
+
+[18.98, 18.64, 19.64, 25.32, 19.4, 19.01, 19.04, 18.8, 18.69, 18.52]
+[73.89]
+26.085822582244873
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 636, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.683557987213135, 'TIME_S_1KI': 16.79804714970619, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1927.4814306020737, 'W': 73.89}
+[18.98, 18.64, 19.64, 25.32, 19.4, 19.01, 19.04, 18.8, 18.69, 18.52, 19.25, 18.51, 18.6, 18.89, 18.62, 18.41, 18.58, 18.78, 18.72, 19.62]
+345.83500000000004
+17.29175
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 636, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.683557987213135, 'TIME_S_1KI': 16.79804714970619, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1927.4814306020737, 'W': 73.89, 'J_1KI': 3030.6311801919396, 'W_1KI': 116.17924528301887, 'W_D': 56.59825, 'J_D': 1476.4119079655409, 'W_D_1KI': 88.99095911949685, 'J_D_1KI': 139.9228916973221}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.json
index 4a5c029..63563c0 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 285101, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.263301372528076, "TIME_S_1KI": 0.03599882628446788, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1023.6102543663977, "W": 73.21, "J_1KI": 3.5903425605886956, "W_1KI": 0.2567861915601839, "W_D": 56.916999999999994, "J_D": 795.8041913368701, "W_D_1KI": 0.1996380230164047, "J_D_1KI": 0.0007002361374264022}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 283081, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.251019954681396, "TIME_S_1KI": 0.03621232069507101, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1131.9865839004517, "W": 81.68, "J_1KI": 3.9988080581192373, "W_1KI": 0.28853932266736376, "W_D": 64.571, "J_D": 894.8764166140556, "W_D_1KI": 0.2281007909396957, "J_D_1KI": 0.0008057792325860644}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.output
index aea26a1..caf5fe8 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.output
@@ -1,752 +1,373 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.019815444946289062}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 1000, 1000, 1000]),
-       col_indices=tensor([ 147,  628, 1125, 5287, 8823, 6934, 2121, 9045, 1741,
-                           1008,  777, 1781, 8765, 5338, 5590, 4011, 9135, 3712,
-                           8048, 6348, 8740, 9035,  822, 3133, 9984, 8122, 9554,
-                           3712, 3272, 5515, 8959, 6829,   83, 3899, 7199, 7801,
-                           5285, 8079, 5441, 7100, 7667, 5056, 6862, 2526, 7349,
-                           1728, 3499, 3354, 7526, 5044, 3630, 6886, 4310, 5869,
-                           2649, 6497, 3797, 8787, 1590, 3717,  600, 9128, 9514,
-                            219, 9480, 7496, 7514, 5942, 3564, 2833, 1156, 3271,
-                           9892, 8323, 4817, 8727, 4029, 1225,  635, 3756, 3854,
-                           1580, 9309, 2348, 2287, 6728, 2416, 8735, 1996, 9982,
-                           2260, 9743, 6241, 6259, 3363, 2128, 9721, 8593, 9287,
-                           6303, 6167, 7208, 4450, 1275, 2370, 2881, 3104, 7161,
-                           9133,  226, 5026, 8446, 4133, 9547, 9259, 5653, 4323,
-                           9414, 5933, 1536, 7990, 6794, 7612, 5173, 6568, 8498,
-                           2655, 2146, 8320, 4457, 6510,  276, 9447, 6969, 2617,
-                           7175, 1400, 1929,  524,  929,  472, 5246, 4656, 4620,
-                            489, 4946,  342,  261, 5266, 9154, 4961,  841, 6798,
-                           6887, 9276,  203, 2231, 9861, 5260, 5410, 4989, 3777,
-                           5518, 2156,  807, 5251, 7532, 8301, 8516, 2536, 7883,
-                           4202, 2101, 5422, 6588,  505, 5736, 1011, 9457,  514,
-                           9520,  284, 2054, 2592, 5274, 6220, 4951, 8010, 3966,
-                           4007, 1997, 1966, 7251, 3207, 5838, 9132, 4982, 4877,
-                           8655, 8284,  876, 5569,  757, 2680, 1245, 1459,   64,
-                            365, 4927, 7024, 7351,  643, 3222, 4840, 6222, 7194,
-                           1143, 9570, 6110,  514, 6176, 3515,  236,   14, 9751,
-                           3159, 5832, 1147, 6202, 9999, 7112, 2023, 1940, 2795,
-                           6251, 8906, 5208, 3243, 7604, 6854, 2354, 2890, 2740,
-                            454, 4997, 9488, 8203, 5802,   49, 4850, 8249, 7823,
-                           1150, 8721, 9200, 1431, 3413, 5962,  300, 7898, 4190,
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-tensor([0.7030, 0.4856, 0.8302,  ..., 0.1030, 0.0064, 0.5187])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 0.019815444946289062 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52988', '-ss', '10000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.9514954090118408}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.019530296325683594}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      6.1926e-01, 3.4844e-01, 3.6060e-01, 7.5940e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.6534, 0.1395, 0.5723,  ..., 0.2846, 0.6527, 0.4839])
+tensor([0.4174, 0.1958, 0.7347,  ..., 0.7767, 0.9832, 0.8001])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -754,271 +375,271 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 1.9514954090118408 seconds
+Time: 0.019530296325683594 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '285101', '-ss', '10000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.263301372528076}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53762', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.9941279888153076}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.0039, 0.4488, 0.2437, 0.2279, 0.0034, 0.0921, 0.1372,
+                      0.0296, 0.1398, 0.6599, 0.0791, 0.6720, 0.7434, 0.5575,
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+                      0.6632, 0.4549, 0.9847, 0.6801, 0.4013, 0.0164]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4905, 0.9858, 0.4305,  ..., 0.2183, 0.4525, 0.0992])
+tensor([0.8565, 0.5607, 0.9219,  ..., 0.4382, 0.2051, 0.0014])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1026,268 +647,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.263301372528076 seconds
+Time: 1.9941279888153076 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '283081', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.251019954681396}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      4.9249e-01, 3.1649e-01, 9.6072e-01, 7.6183e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4905, 0.9858, 0.4305,  ..., 0.2183, 0.4525, 0.0992])
+tensor([0.0260, 0.2662, 0.7797,  ..., 0.3158, 0.7779, 0.5618])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1295,13 +1026,389 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.263301372528076 seconds
+Time: 10.251019954681396 seconds
 
-[18.52, 17.73, 17.82, 17.87, 17.98, 18.03, 17.91, 19.05, 18.64, 18.02]
-[73.21]
-13.981836557388306
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 285101, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.263301372528076, 'TIME_S_1KI': 0.03599882628446788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1023.6102543663977, 'W': 73.21}
-[18.52, 17.73, 17.82, 17.87, 17.98, 18.03, 17.91, 19.05, 18.64, 18.02, 18.85, 18.33, 18.07, 17.99, 17.95, 17.9, 17.98, 18.04, 17.93, 17.89]
-325.86
-16.293
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 285101, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.263301372528076, 'TIME_S_1KI': 0.03599882628446788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1023.6102543663977, 'W': 73.21, 'J_1KI': 3.5903425605886956, 'W_1KI': 0.2567861915601839, 'W_D': 56.916999999999994, 'J_D': 795.8041913368701, 'W_D_1KI': 0.1996380230164047, 'J_D_1KI': 0.0007002361374264022}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ...,  999, 1000, 1000]),
+       col_indices=tensor([6337, 6253, 6887, 9242, 4768, 6015, 4529, 9548, 1595,
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+                      1.5830e-01, 7.5655e-01, 9.7480e-01, 4.0051e-01,
+                      5.7804e-03, 8.2709e-01, 4.2182e-01, 4.4408e-01,
+                      8.9222e-01, 3.6208e-01, 8.9519e-01, 2.5600e-01,
+                      1.8632e-01, 9.8365e-01, 8.1851e-02, 5.2123e-01,
+                      9.5804e-01, 5.6319e-03, 5.9916e-01, 1.5515e-01,
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+                      6.8297e-01, 5.4021e-01, 1.9099e-01, 5.1440e-01,
+                      8.1793e-01, 1.3168e-01, 1.8958e-01, 6.2631e-01,
+                      8.6577e-01, 4.4099e-01, 1.4760e-01, 1.3065e-01,
+                      7.5405e-01, 2.6639e-01, 4.7266e-01, 8.4939e-01,
+                      3.1579e-01, 9.1292e-02, 6.1113e-01, 9.2818e-02,
+                      2.0669e-01, 1.2783e-01, 9.3739e-01, 1.3247e-01,
+                      2.9160e-02, 6.9776e-01, 8.8792e-01, 7.3849e-01,
+                      3.1319e-01, 9.7556e-01, 4.3062e-01, 2.6838e-01,
+                      9.6352e-01, 4.5384e-01, 5.3614e-01, 7.6115e-01,
+                      7.8346e-02, 9.7936e-01, 2.0486e-01, 3.3608e-01,
+                      9.7405e-01, 6.3823e-01, 5.2497e-01, 7.9240e-01,
+                      5.6798e-01, 4.8818e-01, 9.9144e-01, 9.0264e-01,
+                      7.2435e-01, 1.8029e-01, 1.9755e-01, 6.8914e-01,
+                      6.9843e-01, 3.5573e-01, 5.2406e-01, 1.1539e-01,
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+                      6.8536e-01, 6.7350e-01, 5.9683e-01, 6.0014e-01,
+                      9.6642e-01, 1.8403e-01, 3.0091e-01, 5.1426e-02,
+                      9.1433e-01, 3.3716e-01, 5.0284e-01, 7.4283e-01,
+                      2.7395e-02, 1.2681e-01, 6.1057e-01, 9.2038e-01,
+                      1.7242e-01, 5.0040e-01, 1.4167e-01, 9.2800e-01,
+                      3.1623e-01, 6.6496e-01, 4.6361e-01, 4.2210e-01,
+                      5.4576e-01, 7.4592e-01, 6.7106e-01, 2.2625e-01,
+                      1.7871e-01, 2.0941e-01, 4.0171e-01, 5.2365e-02,
+                      9.3492e-02, 6.2121e-01, 5.6627e-01, 4.8088e-01,
+                      3.4543e-01, 6.5256e-01, 3.8111e-01, 5.0820e-01,
+                      6.4190e-01, 9.4368e-01, 9.4619e-01, 1.4001e-01,
+                      9.6033e-01, 2.7958e-01, 7.0197e-01, 6.1710e-01,
+                      2.7678e-01, 5.4832e-01, 7.9254e-01, 8.3342e-01,
+                      6.2200e-01, 8.3286e-01, 9.9807e-01, 6.2790e-02,
+                      8.0453e-01, 1.0971e-01, 8.1520e-01, 3.9444e-01,
+                      6.3046e-01, 4.7325e-01, 4.5247e-01, 6.8061e-02,
+                      1.5491e-01, 4.0141e-03, 7.3446e-01, 6.2330e-02,
+                      9.0610e-01, 8.6523e-01, 4.7703e-01, 7.2769e-01,
+                      5.6920e-02, 4.7380e-01, 6.2567e-01, 9.8501e-01,
+                      6.2170e-01, 1.8872e-01, 1.3379e-02, 3.4054e-01,
+                      7.6416e-01, 3.8986e-01, 5.3500e-01, 9.8245e-01,
+                      7.0761e-01, 2.2468e-01, 5.4399e-01, 3.9694e-02,
+                      7.6734e-01, 1.0932e-01, 1.2210e-01, 7.1353e-01,
+                      8.0572e-01, 2.3582e-01, 9.8215e-01, 1.1015e-01,
+                      8.9113e-03, 7.1177e-01, 1.7986e-01, 4.5091e-01,
+                      1.7810e-01, 7.7559e-01, 9.4792e-01, 3.8139e-01,
+                      1.1546e-01, 7.6492e-01, 7.9508e-01, 8.6260e-01,
+                      9.6169e-01, 8.0818e-02, 2.8676e-02, 7.0994e-01,
+                      5.3818e-01, 2.6338e-01, 1.4087e-01, 6.8694e-01,
+                      2.4939e-01, 7.6196e-01, 6.6186e-01, 8.5655e-01,
+                      2.6657e-01, 6.6963e-01, 1.3788e-01, 4.1556e-01,
+                      1.9527e-01, 3.4537e-01, 1.4042e-01, 6.6402e-01,
+                      3.1351e-01, 6.6071e-01, 2.9595e-01, 9.6000e-01,
+                      7.2292e-01, 5.6025e-01, 7.7751e-01, 1.0276e-01,
+                      6.5010e-01, 1.5068e-01, 5.8440e-01, 3.1344e-01,
+                      1.3546e-01, 2.9211e-01, 4.6813e-01, 2.4935e-02,
+                      9.6457e-02, 7.4214e-01, 4.3047e-02, 2.1731e-01,
+                      3.8465e-01, 9.4445e-01, 6.5976e-01, 8.8821e-01,
+                      5.0696e-01, 5.1792e-01, 4.7107e-01, 5.6703e-01,
+                      1.5516e-01, 8.0371e-01, 6.5735e-02, 9.3342e-01,
+                      7.5234e-01, 9.3810e-01, 8.0659e-02, 7.2725e-01,
+                      7.4287e-01, 8.0905e-01, 5.7371e-01, 8.0566e-01,
+                      8.6814e-01, 9.0878e-01, 8.1847e-02, 9.7577e-01,
+                      5.2302e-01, 6.0315e-01, 7.6834e-01, 9.3738e-01,
+                      5.8105e-01, 6.2760e-01, 7.9012e-01, 7.6416e-01,
+                      9.0308e-01, 7.2939e-01, 6.2535e-01, 8.1521e-01,
+                      4.6661e-01, 6.4054e-01, 6.4950e-01, 1.2072e-01,
+                      6.6340e-01, 6.0429e-01, 4.3164e-02, 8.3696e-02,
+                      8.7069e-01, 8.1124e-01, 5.2189e-01, 2.7111e-01,
+                      7.0551e-01, 6.3514e-02, 9.6973e-01, 6.3092e-01,
+                      5.8354e-01, 1.5335e-01, 7.9160e-01, 1.2117e-02,
+                      6.9520e-01, 6.4075e-01, 6.0235e-01, 6.8160e-02,
+                      2.6503e-01, 6.1870e-01, 6.3837e-01, 1.2293e-01,
+                      7.3640e-01, 2.3190e-01, 5.3413e-01, 4.5806e-01,
+                      2.4139e-01, 2.6287e-02, 3.6355e-01, 4.1787e-01,
+                      3.1233e-01, 4.8046e-01, 9.4417e-01, 6.1386e-01,
+                      4.9249e-01, 3.1649e-01, 9.6072e-01, 7.6183e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0260, 0.2662, 0.7797,  ..., 0.3158, 0.7779, 0.5618])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.251019954681396 seconds
+
+[19.15, 18.55, 18.85, 18.7, 18.83, 18.64, 18.66, 18.71, 18.71, 18.69]
+[81.68]
+13.858797550201416
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 283081, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.251019954681396, 'TIME_S_1KI': 0.03621232069507101, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1131.9865839004517, 'W': 81.68}
+[19.15, 18.55, 18.85, 18.7, 18.83, 18.64, 18.66, 18.71, 18.71, 18.69, 19.67, 19.87, 19.02, 18.63, 19.42, 18.75, 18.68, 18.46, 19.14, 23.61]
+342.18
+17.109
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 283081, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.251019954681396, 'TIME_S_1KI': 0.03621232069507101, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1131.9865839004517, 'W': 81.68, 'J_1KI': 3.9988080581192373, 'W_1KI': 0.28853932266736376, 'W_D': 64.571, 'J_D': 894.8764166140556, 'W_D_1KI': 0.2281007909396957, 'J_D_1KI': 0.0008057792325860644}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.json
index 0ec114e..b5ab784 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 259324, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.257395267486572, "TIME_S_1KI": 0.03955436159972302, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1007.3763615226745, "W": 73.52, "J_1KI": 3.8846244910716883, "W_1KI": 0.28350634727213836, "W_D": 56.727999999999994, "J_D": 777.2911620845794, "W_D_1KI": 0.21875337415742466, "J_D_1KI": 0.000843552367530289}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 260903, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.26060175895691, "TIME_S_1KI": 0.03932726629803762, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1139.2804224228857, "W": 81.91, "J_1KI": 4.366681956217007, "W_1KI": 0.3139480956524072, "W_D": 65.07875, "J_D": 905.1757513216138, "W_D_1KI": 0.24943657221266138, "J_D_1KI": 0.0009560509929462728}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.output
index 838ccfc..c8b3970 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.021072864532470703}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.022187471389770508}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 4999, 4999, 5000]),
-       col_indices=tensor([6834, 1931, 4346,  ..., 6725, 2972, 1681]),
-       values=tensor([0.7465, 0.7749, 0.3553,  ..., 0.3449, 0.2710, 0.4644]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([1918, 5095, 6627,  ..., 7262, 3668, 8808]),
+       values=tensor([0.5666, 0.3017, 0.3210,  ..., 0.1526, 0.0926, 0.2620]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.4472, 0.0239, 0.4773,  ..., 0.7523, 0.1836, 0.2389])
+tensor([0.7773, 0.7111, 0.8277,  ..., 0.4499, 0.1514, 0.4790])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 0.021072864532470703 seconds
+Time: 0.022187471389770508 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '49827', '-ss', '10000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.0174834728240967}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '47324', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.9045445919036865}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    1,  ..., 4999, 5000, 5000]),
-       col_indices=tensor([6306, 9296, 8522,  ..., 5641, 7164, 5943]),
-       values=tensor([0.9605, 0.0866, 0.1892,  ..., 0.4816, 0.2836, 0.0365]),
+       col_indices=tensor([5402,    7,  882,  ..., 1424, 1990, 6900]),
+       values=tensor([0.9801, 0.1240, 0.1724,  ..., 0.6076, 0.6381, 0.9238]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.7120, 0.8205, 0.0862,  ..., 0.5109, 0.7192, 0.7608])
+tensor([0.8469, 0.8145, 0.0221,  ..., 0.4733, 0.3926, 0.1930])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 2.0174834728240967 seconds
+Time: 1.9045445919036865 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '259324', '-ss', '10000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.257395267486572}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '260903', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.26060175895691}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 5000, 5000]),
-       col_indices=tensor([2130, 3883, 5256,  ..., 7000, 8219, 5132]),
-       values=tensor([0.8393, 0.8650, 0.4056,  ..., 0.4895, 0.0562, 0.7603]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 4997, 4998, 5000]),
+       col_indices=tensor([1861,  320, 3544,  ..., 2680, 2737, 8798]),
+       values=tensor([0.9330, 0.5695, 0.5821,  ..., 0.6374, 0.5841, 0.7944]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.9495, 0.1374, 0.1837,  ..., 0.6231, 0.7099, 0.0387])
+tensor([0.6764, 0.7170, 0.1559,  ..., 0.5275, 0.9424, 0.9186])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.257395267486572 seconds
+Time: 10.26060175895691 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 5000, 5000]),
-       col_indices=tensor([2130, 3883, 5256,  ..., 7000, 8219, 5132]),
-       values=tensor([0.8393, 0.8650, 0.4056,  ..., 0.4895, 0.0562, 0.7603]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 4997, 4998, 5000]),
+       col_indices=tensor([1861,  320, 3544,  ..., 2680, 2737, 8798]),
+       values=tensor([0.9330, 0.5695, 0.5821,  ..., 0.6374, 0.5841, 0.7944]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.9495, 0.1374, 0.1837,  ..., 0.6231, 0.7099, 0.0387])
+tensor([0.6764, 0.7170, 0.1559,  ..., 0.5275, 0.9424, 0.9186])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.257395267486572 seconds
+Time: 10.26060175895691 seconds
 
-[18.17, 18.06, 17.92, 18.22, 18.12, 18.42, 22.11, 19.11, 18.46, 18.0]
-[73.52]
-13.702072381973267
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 259324, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.257395267486572, 'TIME_S_1KI': 0.03955436159972302, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1007.3763615226745, 'W': 73.52}
-[18.17, 18.06, 17.92, 18.22, 18.12, 18.42, 22.11, 19.11, 18.46, 18.0, 18.1, 18.11, 18.16, 22.77, 18.06, 18.3, 17.88, 17.93, 18.0, 18.15]
-335.84000000000003
-16.792
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 259324, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.257395267486572, 'TIME_S_1KI': 0.03955436159972302, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1007.3763615226745, 'W': 73.52, 'J_1KI': 3.8846244910716883, 'W_1KI': 0.28350634727213836, 'W_D': 56.727999999999994, 'J_D': 777.2911620845794, 'W_D_1KI': 0.21875337415742466, 'J_D_1KI': 0.000843552367530289}
+[19.13, 18.94, 18.64, 18.55, 18.48, 18.76, 18.52, 18.73, 18.63, 18.84]
+[81.91]
+13.908929586410522
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 260903, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.26060175895691, 'TIME_S_1KI': 0.03932726629803762, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1139.2804224228857, 'W': 81.91}
+[19.13, 18.94, 18.64, 18.55, 18.48, 18.76, 18.52, 18.73, 18.63, 18.84, 19.04, 18.5, 18.67, 18.84, 18.66, 18.62, 18.96, 18.69, 18.74, 18.38]
+336.625
+16.83125
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 260903, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.26060175895691, 'TIME_S_1KI': 0.03932726629803762, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1139.2804224228857, 'W': 81.91, 'J_1KI': 4.366681956217007, 'W_1KI': 0.3139480956524072, 'W_D': 65.07875, 'J_D': 905.1757513216138, 'W_D_1KI': 0.24943657221266138, 'J_D_1KI': 0.0009560509929462728}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json
index da0856a..8565a48 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 665, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.126577615737915, "TIME_S_1KI": 15.22793626426754, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3144.6024104833605, "W": 46.7, "J_1KI": 4728.725429298286, "W_1KI": 70.22556390977444, "W_D": 20.655250000000002, "J_D": 1390.846872358382, "W_D_1KI": 31.060526315789478, "J_D_1KI": 46.707558369608236}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 743, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.847487449645996, "TIME_S_1KI": 14.599579340034987, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1707.3248128795624, "W": 78.28, "J_1KI": 2297.879963498738, "W_1KI": 105.35666218034993, "W_D": 61.384750000000004, "J_D": 1338.8312060220837, "W_D_1KI": 82.61742934051145, "J_D_1KI": 111.19438673016346}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output
index 4beed36..ef0fdc6 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output
@@ -1,36 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.0304152965545654}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.4131314754486084}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       59,       94,  ..., 24999900,
-                            24999951, 25000000]),
-       col_indices=tensor([  1276,  15885,  34398,  ..., 446460, 484343,
-                           488114]),
-       values=tensor([0.3408, 0.7505, 0.4683,  ..., 0.6426, 0.2990, 0.1628]),
-       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.4984, 0.2348, 0.7546,  ..., 0.4897, 0.9555, 0.5266])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 25000000
-Density: 0.0001
-Time: 2.0304152965545654 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '517', '-ss', '500000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.159608364105225}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       46,       98,  ..., 24999895,
+tensor(crow_indices=tensor([       0,       45,       94,  ..., 24999909,
                             24999952, 25000000]),
-       col_indices=tensor([  1859,  10480,  11583,  ..., 471819, 483100,
-                           486034]),
-       values=tensor([0.5566, 0.4872, 0.1210,  ..., 0.2476, 0.9480, 0.3070]),
+       col_indices=tensor([   333,    411,   6379,  ..., 461667, 473356,
+                           487603]),
+       values=tensor([0.3560, 0.8516, 0.0241,  ..., 0.9709, 0.9869, 0.6984]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.1091, 0.0853, 0.9295,  ..., 0.2076, 0.8766, 0.1664])
+tensor([0.0647, 0.7295, 0.8675,  ..., 0.7068, 0.2858, 0.5210])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 8.159608364105225 seconds
+Time: 1.4131314754486084 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '665', '-ss', '500000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.126577615737915}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '743', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.847487449645996}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       66,      119,  ..., 24999902,
-                            24999949, 25000000]),
-       col_indices=tensor([  3829,  12709,  24306,  ..., 491038, 494248,
-                           495364]),
-       values=tensor([0.8354, 0.9747, 0.5569,  ..., 0.5257, 0.7884, 0.2877]),
+tensor(crow_indices=tensor([       0,       52,      106,  ..., 24999903,
+                            24999952, 25000000]),
+       col_indices=tensor([   253,   4284,   6146,  ..., 481164, 481548,
+                           482908]),
+       values=tensor([0.9604, 0.9584, 0.2027,  ..., 0.8146, 0.5069, 0.5308]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.8209, 0.7651, 0.4978,  ..., 0.6892, 0.4643, 0.4864])
+tensor([0.2685, 0.5529, 0.3924,  ..., 0.7041, 0.0047, 0.4106])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -59,17 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 10.126577615737915 seconds
+Time: 10.847487449645996 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       66,      119,  ..., 24999902,
-                            24999949, 25000000]),
-       col_indices=tensor([  3829,  12709,  24306,  ..., 491038, 494248,
-                           495364]),
-       values=tensor([0.8354, 0.9747, 0.5569,  ..., 0.5257, 0.7884, 0.2877]),
+tensor(crow_indices=tensor([       0,       52,      106,  ..., 24999903,
+                            24999952, 25000000]),
+       col_indices=tensor([   253,   4284,   6146,  ..., 481164, 481548,
+                           482908]),
+       values=tensor([0.9604, 0.9584, 0.2027,  ..., 0.8146, 0.5069, 0.5308]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.8209, 0.7651, 0.4978,  ..., 0.6892, 0.4643, 0.4864])
+tensor([0.2685, 0.5529, 0.3924,  ..., 0.7041, 0.0047, 0.4106])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -77,13 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 10.126577615737915 seconds
+Time: 10.847487449645996 seconds
 
-[18.36, 18.09, 18.07, 17.97, 17.94, 18.13, 18.13, 17.77, 17.92, 18.01]
-[46.7]
-67.33624005317688
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 665, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.126577615737915, 'TIME_S_1KI': 15.22793626426754, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3144.6024104833605, 'W': 46.7}
-[18.36, 18.09, 18.07, 17.97, 17.94, 18.13, 18.13, 17.77, 17.92, 18.01, 39.86, 40.69, 39.54, 40.28, 39.05, 39.85, 39.62, 39.36, 40.17, 40.4]
-520.895
-26.04475
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 665, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.126577615737915, 'TIME_S_1KI': 15.22793626426754, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3144.6024104833605, 'W': 46.7, 'J_1KI': 4728.725429298286, 'W_1KI': 70.22556390977444, 'W_D': 20.655250000000002, 'J_D': 1390.846872358382, 'W_D_1KI': 31.060526315789478, 'J_D_1KI': 46.707558369608236}
+[19.4, 18.5, 18.39, 18.48, 18.93, 18.52, 18.42, 18.25, 18.93, 18.28]
+[78.28]
+21.81048560142517
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 743, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.847487449645996, 'TIME_S_1KI': 14.599579340034987, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1707.3248128795624, 'W': 78.28}
+[19.4, 18.5, 18.39, 18.48, 18.93, 18.52, 18.42, 18.25, 18.93, 18.28, 18.83, 18.79, 18.7, 18.45, 18.42, 21.07, 19.15, 18.58, 18.85, 18.44]
+337.905
+16.895249999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 743, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.847487449645996, 'TIME_S_1KI': 14.599579340034987, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1707.3248128795624, 'W': 78.28, 'J_1KI': 2297.879963498738, 'W_1KI': 105.35666218034993, 'W_D': 61.384750000000004, 'J_D': 1338.8312060220837, 'W_D_1KI': 82.61742934051145, 'J_D_1KI': 111.19438673016346}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.json
index ac8e083..9cdb73b 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8088, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.521591186523438, "TIME_S_1KI": 1.30088911801724, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1272.5733924508095, "W": 87.25, "J_1KI": 157.34092389352244, "W_1KI": 10.787586547972305, "W_D": 70.764, "J_D": 1032.1190090932846, "W_D_1KI": 8.749258160237389, "J_D_1KI": 1.0817579327692124}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 7744, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.456795454025269, "TIME_S_1KI": 1.350309330323511, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1300.6653230977058, "W": 89.23, "J_1KI": 167.95781548265828, "W_1KI": 11.522469008264462, "W_D": 72.1055, "J_D": 1051.0492374159098, "W_D_1KI": 9.311144111570249, "J_D_1KI": 1.2023688160602077}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.output
index a65a620..b742134 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,57 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.14249539375305176}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.15149736404418945}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       5,       6,  ..., 2499988,
-                            2499994, 2500000]),
-       col_indices=tensor([159074, 199303, 338786,  ..., 336877, 376694,
-                           404714]),
-       values=tensor([0.7251, 0.9700, 0.9965,  ..., 0.4798, 0.8363, 0.5285]),
-       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5063, 0.7490, 0.8579,  ..., 0.3117, 0.7674, 0.7165])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 2500000
-Density: 1e-05
-Time: 0.14249539375305176 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7368', '-ss', '500000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.564131021499634}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       7,      11,  ..., 2499988,
-                            2499994, 2500000]),
-       col_indices=tensor([ 45977,  46883, 132654,  ..., 283974, 337716,
-                           438050]),
-       values=tensor([0.6941, 0.4659, 0.2903,  ..., 0.1328, 0.8033, 0.9427]),
-       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8274, 0.6187, 0.7071,  ..., 0.9433, 0.5745, 0.3570])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 2500000
-Density: 1e-05
-Time: 9.564131021499634 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8088', '-ss', '500000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.521591186523438}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       6,      11,  ..., 2499988,
+tensor(crow_indices=tensor([      0,       6,       9,  ..., 2499992,
                             2499995, 2500000]),
-       col_indices=tensor([ 55151,  55855, 262240,  ..., 129037, 280325,
-                           497898]),
-       values=tensor([0.5548, 0.3291, 0.4545,  ..., 0.6191, 0.2200, 0.6842]),
+       col_indices=tensor([ 74157, 220860, 221973,  ..., 150255, 261218,
+                           336444]),
+       values=tensor([0.4232, 0.0404, 0.7971,  ..., 0.3800, 0.9766, 0.9632]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8740, 0.8697, 0.8262,  ..., 0.4420, 0.1114, 0.8177])
+tensor([0.7557, 0.5265, 0.2382,  ..., 0.3943, 0.8940, 0.5498])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -59,17 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.521591186523438 seconds
+Time: 0.15149736404418945 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '6930', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.39523720741272}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       6,      11,  ..., 2499988,
+tensor(crow_indices=tensor([      0,       3,       9,  ..., 2499985,
                             2499995, 2500000]),
-       col_indices=tensor([ 55151,  55855, 262240,  ..., 129037, 280325,
-                           497898]),
-       values=tensor([0.5548, 0.3291, 0.4545,  ..., 0.6191, 0.2200, 0.6842]),
+       col_indices=tensor([204165, 362178, 474289,  ..., 280949, 390948,
+                           429692]),
+       values=tensor([0.7877, 0.4298, 0.2604,  ..., 0.9690, 0.2975, 0.5629]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8740, 0.8697, 0.8262,  ..., 0.4420, 0.1114, 0.8177])
+tensor([0.2673, 0.3483, 0.3518,  ..., 0.9196, 0.5251, 0.0617])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -77,13 +38,52 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.521591186523438 seconds
+Time: 9.39523720741272 seconds
 
-[19.45, 17.88, 18.22, 21.5, 18.64, 18.15, 18.16, 17.8, 17.97, 17.95]
-[87.25]
-14.585368394851685
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8088, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.521591186523438, 'TIME_S_1KI': 1.30088911801724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1272.5733924508095, 'W': 87.25}
-[19.45, 17.88, 18.22, 21.5, 18.64, 18.15, 18.16, 17.8, 17.97, 17.95, 18.5, 17.88, 17.95, 18.22, 18.0, 18.18, 18.21, 18.1, 18.01, 17.8]
-329.72
-16.486
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8088, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.521591186523438, 'TIME_S_1KI': 1.30088911801724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1272.5733924508095, 'W': 87.25, 'J_1KI': 157.34092389352244, 'W_1KI': 10.787586547972305, 'W_D': 70.764, 'J_D': 1032.1190090932846, 'W_D_1KI': 8.749258160237389, 'J_D_1KI': 1.0817579327692124}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7744', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.456795454025269}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       4,  ..., 2499990,
+                            2499993, 2500000]),
+       col_indices=tensor([ 84916, 193491, 208640,  ..., 274348, 297691,
+                           374693]),
+       values=tensor([0.3724, 0.0478, 0.9524,  ..., 0.8845, 0.7053, 0.9023]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0947, 0.8980, 0.4248,  ..., 0.5144, 0.1147, 0.5545])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.456795454025269 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       4,  ..., 2499990,
+                            2499993, 2500000]),
+       col_indices=tensor([ 84916, 193491, 208640,  ..., 274348, 297691,
+                           374693]),
+       values=tensor([0.3724, 0.0478, 0.9524,  ..., 0.8845, 0.7053, 0.9023]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0947, 0.8980, 0.4248,  ..., 0.5144, 0.1147, 0.5545])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.456795454025269 seconds
+
+[19.09, 18.64, 19.23, 18.57, 18.57, 18.65, 18.89, 18.58, 18.6, 21.3]
+[89.23]
+14.576547384262085
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 7744, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.456795454025269, 'TIME_S_1KI': 1.350309330323511, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.6653230977058, 'W': 89.23}
+[19.09, 18.64, 19.23, 18.57, 18.57, 18.65, 18.89, 18.58, 18.6, 21.3, 18.87, 18.69, 18.86, 18.42, 18.71, 22.08, 19.26, 18.57, 19.16, 18.76]
+342.49
+17.1245
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 7744, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.456795454025269, 'TIME_S_1KI': 1.350309330323511, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.6653230977058, 'W': 89.23, 'J_1KI': 167.95781548265828, 'W_1KI': 11.522469008264462, 'W_D': 72.1055, 'J_D': 1051.0492374159098, 'W_D_1KI': 9.311144111570249, 'J_D_1KI': 1.2023688160602077}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.json
index 17825c9..cd41306 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1356, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.575294256210327, "TIME_S_1KI": 7.798889569476643, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1502.6908141350748, "W": 76.43, "J_1KI": 1108.179066471294, "W_1KI": 56.36430678466077, "W_D": 60.11625000000001, "J_D": 1181.9460507032277, "W_D_1KI": 44.333517699115056, "J_D_1KI": 32.694334586368036}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1328, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.82228398323059, "TIME_S_1KI": 8.149310228336288, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1475.9286375498773, "W": 83.17, "J_1KI": 1111.3920463477991, "W_1KI": 62.628012048192765, "W_D": 66.243, "J_D": 1175.5433538200855, "W_D_1KI": 49.88177710843373, "J_D_1KI": 37.561579147916966}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.output
index b27b9d0..34a0ce8 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.output
@@ -1,15 +1,36 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.7741575241088867}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.790607213973999}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       27,       54,  ..., 12499941,
+tensor(crow_indices=tensor([       0,       21,       47,  ..., 12499944,
+                            12499967, 12500000]),
+       col_indices=tensor([  1494,  57305,  78344,  ..., 463720, 471221,
+                           471958]),
+       values=tensor([0.3917, 0.2908, 0.7809,  ..., 0.7032, 0.0658, 0.8610]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4834, 0.6730, 0.9333,  ..., 0.5341, 0.2922, 0.8114])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 0.790607213973999 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1328', '-ss', '500000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.82228398323059}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       19,       44,  ..., 12499949,
                             12499975, 12500000]),
-       col_indices=tensor([ 19879,  19996,  22547,  ..., 457855, 459779,
-                           462945]),
-       values=tensor([0.0262, 0.3741, 0.0922,  ..., 0.5524, 0.1014, 0.8276]),
+       col_indices=tensor([ 57604,  61315,  85606,  ..., 382989, 409177,
+                           486939]),
+       values=tensor([0.6373, 0.9438, 0.2505,  ..., 0.8754, 0.9247, 0.8731]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.7209, 0.9008, 0.7814,  ..., 0.2206, 0.4926, 0.1534])
+tensor([0.4492, 0.4704, 0.5551,  ..., 0.2025, 0.2950, 0.9971])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 0.7741575241088867 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1356', '-ss', '500000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.575294256210327}
+Time: 10.82228398323059 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       30,       59,  ..., 12499949,
-                            12499973, 12500000]),
-       col_indices=tensor([ 13892,  45461,  46784,  ..., 469557, 488276,
-                           489508]),
-       values=tensor([0.8469, 0.7554, 0.2394,  ..., 0.6309, 0.5261, 0.2516]),
+tensor(crow_indices=tensor([       0,       19,       44,  ..., 12499949,
+                            12499975, 12500000]),
+       col_indices=tensor([ 57604,  61315,  85606,  ..., 382989, 409177,
+                           486939]),
+       values=tensor([0.6373, 0.9438, 0.2505,  ..., 0.8754, 0.9247, 0.8731]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.9763, 0.3043, 0.0965,  ..., 0.1822, 0.5455, 0.4604])
+tensor([0.4492, 0.4704, 0.5551,  ..., 0.2025, 0.2950, 0.9971])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,31 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 10.575294256210327 seconds
+Time: 10.82228398323059 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       30,       59,  ..., 12499949,
-                            12499973, 12500000]),
-       col_indices=tensor([ 13892,  45461,  46784,  ..., 469557, 488276,
-                           489508]),
-       values=tensor([0.8469, 0.7554, 0.2394,  ..., 0.6309, 0.5261, 0.2516]),
-       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.9763, 0.3043, 0.0965,  ..., 0.1822, 0.5455, 0.4604])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 12500000
-Density: 5e-05
-Time: 10.575294256210327 seconds
-
-[18.28, 17.9, 18.0, 17.69, 18.19, 17.78, 17.99, 17.71, 17.86, 17.72]
-[76.43]
-19.66100764274597
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1356, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.575294256210327, 'TIME_S_1KI': 7.798889569476643, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.6908141350748, 'W': 76.43}
-[18.28, 17.9, 18.0, 17.69, 18.19, 17.78, 17.99, 17.71, 17.86, 17.72, 22.73, 18.23, 18.33, 18.05, 18.19, 17.82, 18.25, 18.16, 17.83, 17.86]
-326.275
-16.31375
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1356, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.575294256210327, 'TIME_S_1KI': 7.798889569476643, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.6908141350748, 'W': 76.43, 'J_1KI': 1108.179066471294, 'W_1KI': 56.36430678466077, 'W_D': 60.11625000000001, 'J_D': 1181.9460507032277, 'W_D_1KI': 44.333517699115056, 'J_D_1KI': 32.694334586368036}
+[22.8, 18.6, 18.68, 18.35, 18.69, 18.46, 18.87, 18.58, 18.66, 18.46]
+[83.17]
+17.745925664901733
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1328, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.82228398323059, 'TIME_S_1KI': 8.149310228336288, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1475.9286375498773, 'W': 83.17}
+[22.8, 18.6, 18.68, 18.35, 18.69, 18.46, 18.87, 18.58, 18.66, 18.46, 19.23, 18.49, 19.15, 18.46, 19.64, 18.46, 18.68, 18.56, 18.73, 18.47]
+338.54
+16.927
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1328, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.82228398323059, 'TIME_S_1KI': 8.149310228336288, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1475.9286375498773, 'W': 83.17, 'J_1KI': 1111.3920463477991, 'W_1KI': 62.628012048192765, 'W_D': 66.243, 'J_D': 1175.5433538200855, 'W_D_1KI': 49.88177710843373, 'J_D_1KI': 37.561579147916966}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.json
index 8609bc2..58e9731 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 80207, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.527606010437012, "TIME_S_1KI": 0.13125545164932004, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1181.3196375966072, "W": 83.35, "J_1KI": 14.728385771773127, "W_1KI": 1.039186105950852, "W_D": 67.15325, "J_D": 951.7630828246474, "W_D_1KI": 0.8372492425848118, "J_D_1KI": 0.010438605640216089}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 78502, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.604196548461914, "TIME_S_1KI": 0.13508186477366071, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1248.9033283519743, "W": 89.76999999999998, "J_1KI": 15.909191209803243, "W_1KI": 1.1435377442612924, "W_D": 72.76324999999999, "J_D": 1012.3010483090875, "W_D_1KI": 0.926896766961351, "J_D_1KI": 0.011807301303933034}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.output
index 381203d..143ee83 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.03568005561828613}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.029583454132080078}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      9,  ..., 249987, 249994,
+tensor(crow_indices=tensor([     0,      6,     14,  ..., 249987, 249994,
                             250000]),
-       col_indices=tensor([ 1312, 19953, 25282,  ..., 26652, 33001, 38879]),
-       values=tensor([0.3658, 0.9367, 0.4335,  ..., 0.7027, 0.8564, 0.9906]),
+       col_indices=tensor([ 9923, 19449, 22561,  ..., 21964, 36889, 39975]),
+       values=tensor([0.4210, 0.0559, 0.4971,  ..., 0.9048, 0.5499, 0.1509]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4942, 0.6881, 0.2872,  ..., 0.3001, 0.6556, 0.7300])
+tensor([0.0733, 0.6322, 0.8044,  ..., 0.8642, 0.7618, 0.4999])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 0.03568005561828613 seconds
+Time: 0.029583454132080078 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '29428', '-ss', '50000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 3.8524389266967773}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '35492', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 4.747204542160034}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,      8,  ..., 249998, 250000,
+tensor(crow_indices=tensor([     0,      8,     11,  ..., 249987, 249995,
                             250000]),
-       col_indices=tensor([ 4346,  9215, 13661,  ..., 37674, 16332, 22572]),
-       values=tensor([0.5552, 0.4398, 0.7001,  ..., 0.6234, 0.7005, 0.0878]),
+       col_indices=tensor([  252,  9477, 12548,  ..., 34072, 35023, 41292]),
+       values=tensor([0.7567, 0.0178, 0.4836,  ..., 0.7947, 0.4700, 0.9389]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5304, 0.8900, 0.0447,  ..., 0.3418, 0.1958, 0.2486])
+tensor([0.1654, 0.8334, 0.6400,  ..., 0.5149, 0.8662, 0.5728])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 3.8524389266967773 seconds
+Time: 4.747204542160034 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '80207', '-ss', '50000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.527606010437012}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '78502', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.604196548461914}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      9,     19,  ..., 249993, 249998,
+tensor(crow_indices=tensor([     0,      4,      8,  ..., 249989, 249995,
                             250000]),
-       col_indices=tensor([ 8045, 12111, 14477,  ..., 47402, 12160, 19361]),
-       values=tensor([0.9649, 0.3819, 0.5636,  ..., 0.2633, 0.1370, 0.0196]),
+       col_indices=tensor([ 1101, 25106, 38381,  ..., 17831, 22084, 28136]),
+       values=tensor([0.1529, 0.1331, 0.6574,  ..., 0.5721, 0.3973, 0.7056]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0927, 0.4142, 0.0895,  ..., 0.8219, 0.5339, 0.8064])
+tensor([0.8762, 0.0918, 0.6182,  ..., 0.1694, 0.9682, 0.4170])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.527606010437012 seconds
+Time: 10.604196548461914 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      9,     19,  ..., 249993, 249998,
+tensor(crow_indices=tensor([     0,      4,      8,  ..., 249989, 249995,
                             250000]),
-       col_indices=tensor([ 8045, 12111, 14477,  ..., 47402, 12160, 19361]),
-       values=tensor([0.9649, 0.3819, 0.5636,  ..., 0.2633, 0.1370, 0.0196]),
+       col_indices=tensor([ 1101, 25106, 38381,  ..., 17831, 22084, 28136]),
+       values=tensor([0.1529, 0.1331, 0.6574,  ..., 0.5721, 0.3973, 0.7056]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0927, 0.4142, 0.0895,  ..., 0.8219, 0.5339, 0.8064])
+tensor([0.8762, 0.0918, 0.6182,  ..., 0.1694, 0.9682, 0.4170])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.527606010437012 seconds
+Time: 10.604196548461914 seconds
 
-[18.32, 17.98, 17.86, 18.86, 18.68, 17.81, 17.68, 18.11, 17.76, 17.85]
-[83.35]
-14.173001050949097
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 80207, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.527606010437012, 'TIME_S_1KI': 0.13125545164932004, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1181.3196375966072, 'W': 83.35}
-[18.32, 17.98, 17.86, 18.86, 18.68, 17.81, 17.68, 18.11, 17.76, 17.85, 18.01, 18.02, 17.88, 17.81, 17.97, 17.93, 17.96, 17.74, 17.82, 17.95]
-323.93499999999995
-16.196749999999998
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 80207, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.527606010437012, 'TIME_S_1KI': 0.13125545164932004, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1181.3196375966072, 'W': 83.35, 'J_1KI': 14.728385771773127, 'W_1KI': 1.039186105950852, 'W_D': 67.15325, 'J_D': 951.7630828246474, 'W_D_1KI': 0.8372492425848118, 'J_D_1KI': 0.010438605640216089}
+[18.91, 18.66, 18.4, 18.85, 18.71, 18.62, 18.6, 18.6, 18.59, 18.36]
+[89.77]
+13.912257194519043
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 78502, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.604196548461914, 'TIME_S_1KI': 0.13508186477366071, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1248.9033283519743, 'W': 89.76999999999998}
+[18.91, 18.66, 18.4, 18.85, 18.71, 18.62, 18.6, 18.6, 18.59, 18.36, 19.02, 18.67, 23.43, 18.59, 18.63, 18.45, 18.84, 18.48, 18.52, 18.7]
+340.135
+17.00675
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 78502, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.604196548461914, 'TIME_S_1KI': 0.13508186477366071, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1248.9033283519743, 'W': 89.76999999999998, 'J_1KI': 15.909191209803243, 'W_1KI': 1.1435377442612924, 'W_D': 72.76324999999999, 'J_D': 1012.3010483090875, 'W_D_1KI': 0.926896766961351, 'J_D_1KI': 0.011807301303933034}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.json
index 1ef0389..fe8b0b3 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 17086, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.693793773651123, "TIME_S_1KI": 0.6258804737007564, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1274.9557392597198, "W": 87.4, "J_1KI": 74.61990748330328, "W_1KI": 5.1152990752663, "W_D": 70.92850000000001, "J_D": 1034.6761802297833, "W_D_1KI": 4.151264192906474, "J_D_1KI": 0.24296290488742092}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 16566, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.542575359344482, "TIME_S_1KI": 0.6363983677015865, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1301.1103634548188, "W": 89.24, "J_1KI": 78.5410095046975, "W_1KI": 5.38693710008451, "W_D": 72.376, "J_D": 1055.234913328171, "W_D_1KI": 4.368948448629724, "J_D_1KI": 0.26372983512191983}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.output
index 168fd00..b103663 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.output
@@ -1,15 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.07488131523132324}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.07738423347473145}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      64,     108,  ..., 2499910,
-                            2499955, 2500000]),
-       col_indices=tensor([  984,  1625,  1972,  ..., 46651, 48149, 48861]),
-       values=tensor([5.1121e-01, 5.6272e-01, 1.1145e-04,  ...,
-                      9.0355e-01, 4.1789e-01, 4.2355e-01]),
+tensor(crow_indices=tensor([      0,      42,      96,  ..., 2499904,
+                            2499957, 2500000]),
+       col_indices=tensor([ 1691,  2079,  2423,  ..., 45303, 47027, 48713]),
+       values=tensor([0.6144, 0.7881, 0.6854,  ..., 0.3269, 0.0689, 0.3794]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3839, 0.9792, 0.8841,  ..., 0.7211, 0.2437, 0.3590])
+tensor([0.1047, 0.4397, 0.6737,  ..., 0.5492, 0.2785, 0.2061])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -17,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 0.07488131523132324 seconds
+Time: 0.07738423347473145 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '14022', '-ss', '50000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.616644144058228}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '13568', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.599735021591187}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      54,     105,  ..., 2499907,
-                            2499960, 2500000]),
-       col_indices=tensor([  369,  1157,  3425,  ..., 45077, 46820, 49764]),
-       values=tensor([0.6429, 0.4063, 0.5775,  ..., 0.7664, 0.6925, 0.8507]),
+tensor(crow_indices=tensor([      0,      57,     103,  ..., 2499904,
+                            2499953, 2500000]),
+       col_indices=tensor([   50,   533,  1501,  ..., 46106, 47815, 49257]),
+       values=tensor([0.9408, 0.2538, 0.6981,  ..., 0.4395, 0.0590, 0.3961]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4381, 0.1550, 0.0791,  ..., 0.0177, 0.9903, 0.0608])
+tensor([0.1785, 0.5003, 0.9612,  ..., 0.8268, 0.6661, 0.8175])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -37,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 8.616644144058228 seconds
+Time: 8.599735021591187 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '17086', '-ss', '50000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.693793773651123}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '16566', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.542575359344482}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      52,      91,  ..., 2499883,
-                            2499936, 2500000]),
-       col_indices=tensor([  188,  2361,  2646,  ..., 48274, 48923, 49377]),
-       values=tensor([0.2734, 0.1056, 0.9298,  ..., 0.4005, 0.9270, 0.9473]),
+tensor(crow_indices=tensor([      0,      51,      94,  ..., 2499892,
+                            2499941, 2500000]),
+       col_indices=tensor([ 1586,  1774,  2808,  ..., 47697, 48009, 49307]),
+       values=tensor([0.9728, 0.9906, 0.5047,  ..., 0.0535, 0.3566, 0.5066]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1965, 0.7813, 0.8576,  ..., 0.6695, 0.1581, 0.1443])
+tensor([0.7723, 0.7483, 0.0194,  ..., 0.7269, 0.0365, 0.8768])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -57,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.693793773651123 seconds
+Time: 10.542575359344482 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      52,      91,  ..., 2499883,
-                            2499936, 2500000]),
-       col_indices=tensor([  188,  2361,  2646,  ..., 48274, 48923, 49377]),
-       values=tensor([0.2734, 0.1056, 0.9298,  ..., 0.4005, 0.9270, 0.9473]),
+tensor(crow_indices=tensor([      0,      51,      94,  ..., 2499892,
+                            2499941, 2500000]),
+       col_indices=tensor([ 1586,  1774,  2808,  ..., 47697, 48009, 49307]),
+       values=tensor([0.9728, 0.9906, 0.5047,  ..., 0.0535, 0.3566, 0.5066]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1965, 0.7813, 0.8576,  ..., 0.6695, 0.1581, 0.1443])
+tensor([0.7723, 0.7483, 0.0194,  ..., 0.7269, 0.0365, 0.8768])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -74,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.693793773651123 seconds
+Time: 10.542575359344482 seconds
 
-[18.43, 18.16, 17.96, 18.08, 18.33, 18.04, 18.36, 18.18, 18.22, 18.06]
-[87.4]
-14.587594270706177
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17086, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.693793773651123, 'TIME_S_1KI': 0.6258804737007564, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.9557392597198, 'W': 87.4}
-[18.43, 18.16, 17.96, 18.08, 18.33, 18.04, 18.36, 18.18, 18.22, 18.06, 18.48, 17.93, 19.19, 18.85, 18.16, 18.04, 18.63, 18.25, 18.43, 18.27]
-329.43
-16.4715
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17086, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.693793773651123, 'TIME_S_1KI': 0.6258804737007564, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.9557392597198, 'W': 87.4, 'J_1KI': 74.61990748330328, 'W_1KI': 5.1152990752663, 'W_D': 70.92850000000001, 'J_D': 1034.6761802297833, 'W_D_1KI': 4.151264192906474, 'J_D_1KI': 0.24296290488742092}
+[19.58, 18.68, 19.21, 18.55, 18.74, 18.46, 19.06, 18.57, 19.35, 18.59]
+[89.24]
+14.579900979995728
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 16566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.542575359344482, 'TIME_S_1KI': 0.6363983677015865, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.1103634548188, 'W': 89.24}
+[19.58, 18.68, 19.21, 18.55, 18.74, 18.46, 19.06, 18.57, 19.35, 18.59, 18.96, 18.5, 18.54, 18.83, 18.59, 18.61, 18.59, 18.56, 18.69, 18.37]
+337.28
+16.863999999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 16566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.542575359344482, 'TIME_S_1KI': 0.6363983677015865, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.1103634548188, 'W': 89.24, 'J_1KI': 78.5410095046975, 'W_1KI': 5.38693710008451, 'W_D': 72.376, 'J_D': 1055.234913328171, 'W_D_1KI': 4.368948448629724, 'J_D_1KI': 0.26372983512191983}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json
index 6f4838a..66c32ba 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1159, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.524485111236572, "TIME_S_1KI": 9.080660147745101, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2883.053437728882, "W": 54.72, "J_1KI": 2487.5353215952387, "W_1KI": 47.21311475409836, "W_D": 38.2975, "J_D": 2017.7949384397268, "W_D_1KI": 33.043572044866266, "J_D_1KI": 28.51041591446615}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1171, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.663051128387451, "TIME_S_1KI": 9.10593606181678, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1689.2026331734658, "W": 77.67, "J_1KI": 1442.5300027100477, "W_1KI": 66.32792485055508, "W_D": 60.839, "J_D": 1323.1543581774235, "W_D_1KI": 51.95473953885568, "J_D_1KI": 44.36783905965472}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output
index c966aa4..dc4d5d3 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 1.2965989112854004}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.8961431980133057}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      462,      963,  ..., 24999009,
-                            24999507, 25000000]),
-       col_indices=tensor([   19,    54,    59,  ..., 49770, 49789, 49840]),
-       values=tensor([0.0062, 0.3047, 0.0339,  ..., 0.6533, 0.8264, 0.4065]),
+tensor(crow_indices=tensor([       0,      519,      989,  ..., 24998978,
+                            24999454, 25000000]),
+       col_indices=tensor([   45,   174,   218,  ..., 49244, 49258, 49545]),
+       values=tensor([0.1125, 0.2142, 0.3311,  ..., 0.0504, 0.1981, 0.8104]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.0572, 0.3375, 0.5398,  ..., 0.2388, 0.0349, 0.7555])
+tensor([0.3205, 0.7140, 0.4617,  ..., 0.1872, 0.0736, 0.0707])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 1.2965989112854004 seconds
+Time: 0.8961431980133057 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '809', '-ss', '50000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.848743677139282}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1171', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.663051128387451}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      486,      980,  ..., 24998984,
-                            24999490, 25000000]),
-       col_indices=tensor([   15,   197,   386,  ..., 49782, 49793, 49889]),
-       values=tensor([0.3923, 0.3887, 0.8681,  ..., 0.2288, 0.1762, 0.4981]),
+tensor(crow_indices=tensor([       0,      544,     1055,  ..., 24999059,
+                            24999508, 25000000]),
+       col_indices=tensor([  351,   404,   563,  ..., 49538, 49715, 49727]),
+       values=tensor([0.9378, 0.6332, 0.6319,  ..., 0.7390, 0.2560, 0.4027]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.2965, 0.2935, 0.4053,  ..., 0.9117, 0.1428, 0.4127])
+tensor([0.0290, 0.6833, 0.3576,  ..., 0.6974, 0.2953, 0.9792])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 7.848743677139282 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1082', '-ss', '50000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.800410747528076}
+Time: 10.663051128387451 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      497,      982,  ..., 24999036,
-                            24999517, 25000000]),
-       col_indices=tensor([   46,   129,   426,  ..., 49653, 49766, 49830]),
-       values=tensor([0.6195, 0.6207, 0.9497,  ..., 0.4637, 0.0557, 0.5508]),
+tensor(crow_indices=tensor([       0,      544,     1055,  ..., 24999059,
+                            24999508, 25000000]),
+       col_indices=tensor([  351,   404,   563,  ..., 49538, 49715, 49727]),
+       values=tensor([0.9378, 0.6332, 0.6319,  ..., 0.7390, 0.2560, 0.4027]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.1589, 0.3084, 0.3696,  ..., 0.3780, 0.7461, 0.4084])
+tensor([0.0290, 0.6833, 0.3576,  ..., 0.6974, 0.2953, 0.9792])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,50 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 9.800410747528076 seconds
+Time: 10.663051128387451 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1159', '-ss', '50000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.524485111236572}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      513,     1013,  ..., 24998968,
-                            24999473, 25000000]),
-       col_indices=tensor([   48,   124,   131,  ..., 49410, 49843, 49893]),
-       values=tensor([0.3835, 0.3241, 0.0409,  ..., 0.5767, 0.7491, 0.8402]),
-       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.3332, 0.0589, 0.5895,  ..., 0.7200, 0.0490, 0.9504])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 25000000
-Density: 0.01
-Time: 10.524485111236572 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      513,     1013,  ..., 24998968,
-                            24999473, 25000000]),
-       col_indices=tensor([   48,   124,   131,  ..., 49410, 49843, 49893]),
-       values=tensor([0.3835, 0.3241, 0.0409,  ..., 0.5767, 0.7491, 0.8402]),
-       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.3332, 0.0589, 0.5895,  ..., 0.7200, 0.0490, 0.9504])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 25000000
-Density: 0.01
-Time: 10.524485111236572 seconds
-
-[18.54, 17.98, 18.07, 17.85, 18.22, 17.93, 18.48, 18.6, 18.31, 18.9]
-[54.72]
-52.68738007545471
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1159, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.524485111236572, 'TIME_S_1KI': 9.080660147745101, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2883.053437728882, 'W': 54.72}
-[18.54, 17.98, 18.07, 17.85, 18.22, 17.93, 18.48, 18.6, 18.31, 18.9, 18.48, 18.15, 18.06, 18.06, 17.98, 17.84, 18.37, 17.81, 17.96, 21.64]
-328.45
-16.4225
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1159, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.524485111236572, 'TIME_S_1KI': 9.080660147745101, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2883.053437728882, 'W': 54.72, 'J_1KI': 2487.5353215952387, 'W_1KI': 47.21311475409836, 'W_D': 38.2975, 'J_D': 2017.7949384397268, 'W_D_1KI': 33.043572044866266, 'J_D_1KI': 28.51041591446615}
+[19.29, 18.98, 18.82, 18.67, 18.7, 18.48, 18.8, 18.48, 18.69, 18.72]
+[77.67]
+21.748456716537476
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1171, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.663051128387451, 'TIME_S_1KI': 9.10593606181678, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1689.2026331734658, 'W': 77.67}
+[19.29, 18.98, 18.82, 18.67, 18.7, 18.48, 18.8, 18.48, 18.69, 18.72, 19.17, 18.51, 18.58, 18.49, 18.99, 18.48, 18.57, 18.54, 19.01, 18.48]
+336.62
+16.831
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1171, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.663051128387451, 'TIME_S_1KI': 9.10593606181678, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1689.2026331734658, 'W': 77.67, 'J_1KI': 1442.5300027100477, 'W_1KI': 66.32792485055508, 'W_D': 60.839, 'J_D': 1323.1543581774235, 'W_D_1KI': 51.95473953885568, 'J_D_1KI': 44.36783905965472}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.05.json
new file mode 100644
index 0000000..5d1cbb5
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 246, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.44187068939209, "TIME_S_1KI": 42.44662881866703, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3199.6955910372735, "W": 63.97, "J_1KI": 13006.892646492983, "W_1KI": 260.040650406504, "W_D": 47.1205, "J_D": 2356.90567605865, "W_D_1KI": 191.54674796747966, "J_D_1KI": 778.6453169409742}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.05.output
new file mode 100644
index 0000000..c913954
--- /dev/null
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_0.05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 4.265462636947632}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2506,      5031,  ...,
+                            124994966, 124997482, 125000000]),
+       col_indices=tensor([    0,    23,    38,  ..., 49921, 49929, 49974]),
+       values=tensor([0.8348, 0.2779, 0.9906,  ..., 0.7530, 0.5252, 0.2131]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.8800, 0.4802, 0.4436,  ..., 0.3999, 0.1613, 0.9688])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 4.265462636947632 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '246', '-ss', '50000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.44187068939209}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2491,      4960,  ...,
+                            124994953, 124997533, 125000000]),
+       col_indices=tensor([    7,    10,    21,  ..., 49960, 49969, 49999]),
+       values=tensor([0.3142, 0.3593, 0.3701,  ..., 0.0388, 0.8323, 0.4619]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.9622, 0.4731, 0.9509,  ..., 0.9547, 0.9339, 0.1027])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 10.44187068939209 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2491,      4960,  ...,
+                            124994953, 124997533, 125000000]),
+       col_indices=tensor([    7,    10,    21,  ..., 49960, 49969, 49999]),
+       values=tensor([0.3142, 0.3593, 0.3701,  ..., 0.0388, 0.8323, 0.4619]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.9622, 0.4731, 0.9509,  ..., 0.9547, 0.9339, 0.1027])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 10.44187068939209 seconds
+
+[18.92, 18.86, 18.6, 19.01, 18.68, 18.68, 19.09, 18.37, 18.68, 18.77]
+[63.97]
+50.01868987083435
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 246, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.44187068939209, 'TIME_S_1KI': 42.44662881866703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3199.6955910372735, 'W': 63.97}
+[18.92, 18.86, 18.6, 19.01, 18.68, 18.68, 19.09, 18.37, 18.68, 18.77, 18.88, 18.32, 18.42, 18.71, 18.57, 19.28, 18.78, 18.72, 18.64, 18.59]
+336.99
+16.8495
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 246, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.44187068939209, 'TIME_S_1KI': 42.44662881866703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3199.6955910372735, 'W': 63.97, 'J_1KI': 13006.892646492983, 'W_1KI': 260.040650406504, 'W_D': 47.1205, 'J_D': 2356.90567605865, 'W_D_1KI': 191.54674796747966, 'J_D_1KI': 778.6453169409742}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.json
index be4ba65..b1b48d4 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 113077, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.7483811378479, "TIME_S_1KI": 0.09505364608052831, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1073.5031987619402, "W": 76.43000000000002, "J_1KI": 9.49355924513332, "W_1KI": 0.6759111048223778, "W_D": 60.05525000000002, "J_D": 843.5104406312707, "W_D_1KI": 0.5311004890472866, "J_D_1KI": 0.004696803850891751}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 110042, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.494260787963867, "TIME_S_1KI": 0.09536595834284971, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1150.5043627023697, "W": 84.45, "J_1KI": 10.455138608007577, "W_1KI": 0.7674342523763654, "W_D": 67.43625, "J_D": 918.7175823479892, "W_D_1KI": 0.6128228312826012, "J_D_1KI": 0.0055689903062703435}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.output
index 8dbecc1..22265c6 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.02812027931213379}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.025803804397583008}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([42362, 37248, 40868,  ..., 37764, 10134, 17711]),
-       values=tensor([0.4763, 0.9715, 0.1475,  ..., 0.3126, 0.8815, 0.0115]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([19109,  3237, 16845,  ..., 27846,  2234, 15836]),
+       values=tensor([0.7799, 0.7161, 0.7964,  ..., 0.4451, 0.9674, 0.7259]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9973, 0.6491, 0.6388,  ..., 0.7622, 0.1974, 0.5505])
+tensor([0.5700, 0.8969, 0.4532,  ..., 0.0768, 0.3223, 0.3140])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,18 +15,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.02812027931213379 seconds
+Time: 0.025803804397583008 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '37339', '-ss', '50000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.4671623706817627}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '40691', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.8826541900634766}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ..., 24999, 24999, 25000]),
-       col_indices=tensor([  629, 39590,  3531,  ..., 29068, 27842, 31077]),
-       values=tensor([0.8879, 0.6863, 0.1252,  ..., 0.4874, 0.7763, 0.0925]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([22975, 26462, 32598,  ..., 46764, 26072, 23370]),
+       values=tensor([0.8868, 0.3719, 0.2452,  ..., 0.3502, 0.5196, 0.5481]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1798, 0.4085, 0.6464,  ..., 0.4512, 0.1603, 0.8018])
+tensor([0.5003, 0.6252, 0.5103,  ..., 0.7624, 0.8677, 0.3765])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,18 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 3.4671623706817627 seconds
+Time: 3.8826541900634766 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '113077', '-ss', '50000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.7483811378479}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '110042', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.494260787963867}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     3,  ..., 24998, 24999, 25000]),
-       col_indices=tensor([ 3755, 45041, 41651,  ..., 28239, 26624, 23506]),
-       values=tensor([0.7255, 0.9443, 0.1927,  ..., 0.4549, 0.6422, 0.3790]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([  968, 37612, 48250,  ..., 42979, 11526, 36099]),
+       values=tensor([0.0287, 0.5894, 0.3848,  ..., 0.9306, 0.7000, 0.7540]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1750, 0.3712, 0.8832,  ..., 0.2728, 0.8510, 0.3193])
+tensor([0.9632, 0.8456, 0.5500,  ..., 0.5594, 0.3118, 0.7403])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,15 +53,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.7483811378479 seconds
+Time: 10.494260787963867 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     3,  ..., 24998, 24999, 25000]),
-       col_indices=tensor([ 3755, 45041, 41651,  ..., 28239, 26624, 23506]),
-       values=tensor([0.7255, 0.9443, 0.1927,  ..., 0.4549, 0.6422, 0.3790]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([  968, 37612, 48250,  ..., 42979, 11526, 36099]),
+       values=tensor([0.0287, 0.5894, 0.3848,  ..., 0.9306, 0.7000, 0.7540]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1750, 0.3712, 0.8832,  ..., 0.2728, 0.8510, 0.3193])
+tensor([0.9632, 0.8456, 0.5500,  ..., 0.5594, 0.3118, 0.7403])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -69,13 +69,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.7483811378479 seconds
+Time: 10.494260787963867 seconds
 
-[18.23, 17.8, 18.68, 17.79, 18.26, 17.97, 18.22, 17.87, 18.18, 18.24]
-[76.43]
-14.045573711395264
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 113077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.7483811378479, 'TIME_S_1KI': 0.09505364608052831, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1073.5031987619402, 'W': 76.43000000000002}
-[18.23, 17.8, 18.68, 17.79, 18.26, 17.97, 18.22, 17.87, 18.18, 18.24, 18.73, 17.83, 18.03, 17.81, 18.07, 18.07, 17.89, 17.96, 20.53, 17.87]
-327.495
-16.37475
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 113077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.7483811378479, 'TIME_S_1KI': 0.09505364608052831, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1073.5031987619402, 'W': 76.43000000000002, 'J_1KI': 9.49355924513332, 'W_1KI': 0.6759111048223778, 'W_D': 60.05525000000002, 'J_D': 843.5104406312707, 'W_D_1KI': 0.5311004890472866, 'J_D_1KI': 0.004696803850891751}
+[19.13, 18.37, 21.18, 19.25, 18.77, 19.21, 18.57, 18.51, 18.4, 18.72]
+[84.45]
+13.623497486114502
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 110042, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.494260787963867, 'TIME_S_1KI': 0.09536595834284971, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1150.5043627023697, 'W': 84.45}
+[19.13, 18.37, 21.18, 19.25, 18.77, 19.21, 18.57, 18.51, 18.4, 18.72, 18.82, 19.16, 18.6, 18.62, 18.99, 18.66, 18.81, 18.6, 18.74, 19.0]
+340.27500000000003
+17.01375
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 110042, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.494260787963867, 'TIME_S_1KI': 0.09536595834284971, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1150.5043627023697, 'W': 84.45, 'J_1KI': 10.455138608007577, 'W_1KI': 0.7674342523763654, 'W_D': 67.43625, 'J_D': 918.7175823479892, 'W_D_1KI': 0.6128228312826012, 'J_D_1KI': 0.0055689903062703435}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.json
index e084c9d..b3c7367 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 88156, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.868364572525024, "TIME_S_1KI": 0.12328559113985461, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1132.9122091054917, "W": 79.86, "J_1KI": 12.851220666834834, "W_1KI": 0.9058940968283498, "W_D": 63.193749999999994, "J_D": 896.4809781387447, "W_D_1KI": 0.716840033576841, "J_D_1KI": 0.00813149455030674}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 87980, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.691504955291748, "TIME_S_1KI": 0.12152199312675321, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1230.567190117836, "W": 88.47, "J_1KI": 13.98689690972762, "W_1KI": 1.0055694476017276, "W_D": 71.44725, "J_D": 993.790456359744, "W_D_1KI": 0.8120851329847691, "J_D_1KI": 0.009230337951634112}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.output
index a0c02dc..b6f37ce 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.028945207595825195}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.028501510620117188}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      5,  ..., 124995, 124996,
+tensor(crow_indices=tensor([     0,      3,      5,  ..., 124997, 125000,
                             125000]),
-       col_indices=tensor([ 6853, 13029,  2913,  ..., 16543, 38720, 48018]),
-       values=tensor([0.0310, 0.8074, 0.8860,  ..., 0.7640, 0.7803, 0.3703]),
+       col_indices=tensor([  688, 22534, 35293,  ..., 27965, 30917, 45363]),
+       values=tensor([0.8089, 0.8662, 0.3715,  ..., 0.9018, 0.4368, 0.1391]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.5471, 0.4507, 0.3833,  ..., 0.1733, 0.0716, 0.3889])
+tensor([0.4275, 0.6873, 0.5905,  ..., 0.8775, 0.0153, 0.6240])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 0.028945207595825195 seconds
+Time: 0.028501510620117188 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '36275', '-ss', '50000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.320595741271973}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '36840', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.396669387817383}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      6,  ..., 124997, 124999,
+tensor(crow_indices=tensor([     0,      3,      7,  ..., 124995, 124997,
                             125000]),
-       col_indices=tensor([26872,  2155, 12844,  ..., 14460, 31839, 14088]),
-       values=tensor([0.4897, 0.3509, 0.0171,  ..., 0.2036, 0.0300, 0.0283]),
+       col_indices=tensor([10255, 14562, 30918,  ...,  6027, 29124, 40379]),
+       values=tensor([0.9249, 0.3245, 0.9331,  ..., 0.1161, 0.7409, 0.4879]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.1984, 0.6261, 0.3986,  ..., 0.2975, 0.8868, 0.2739])
+tensor([0.7659, 0.9381, 0.3858,  ..., 0.5909, 0.8665, 0.5688])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 4.320595741271973 seconds
+Time: 4.396669387817383 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '88156', '-ss', '50000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.868364572525024}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '87980', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.691504955291748}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      6,  ..., 124997, 124998,
+tensor(crow_indices=tensor([     0,      4,      6,  ..., 124996, 124998,
                             125000]),
-       col_indices=tensor([17427, 27540, 29335,  ..., 48451,  4975, 32778]),
-       values=tensor([0.3820, 0.7973, 0.1142,  ..., 0.5863, 0.3733, 0.5873]),
+       col_indices=tensor([ 9926, 12121, 27292,  ...,  4019, 43243, 48084]),
+       values=tensor([0.5351, 0.6475, 0.3719,  ..., 0.2513, 0.9989, 0.4768]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.7507, 0.0731, 0.4946,  ..., 0.5316, 0.6298, 0.9971])
+tensor([0.0373, 0.7318, 0.2383,  ..., 0.3868, 0.6719, 0.1788])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.868364572525024 seconds
+Time: 10.691504955291748 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      6,  ..., 124997, 124998,
+tensor(crow_indices=tensor([     0,      4,      6,  ..., 124996, 124998,
                             125000]),
-       col_indices=tensor([17427, 27540, 29335,  ..., 48451,  4975, 32778]),
-       values=tensor([0.3820, 0.7973, 0.1142,  ..., 0.5863, 0.3733, 0.5873]),
+       col_indices=tensor([ 9926, 12121, 27292,  ...,  4019, 43243, 48084]),
+       values=tensor([0.5351, 0.6475, 0.3719,  ..., 0.2513, 0.9989, 0.4768]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.7507, 0.0731, 0.4946,  ..., 0.5316, 0.6298, 0.9971])
+tensor([0.0373, 0.7318, 0.2383,  ..., 0.3868, 0.6719, 0.1788])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.868364572525024 seconds
+Time: 10.691504955291748 seconds
 
-[18.49, 17.88, 18.37, 17.92, 18.86, 17.84, 18.14, 20.87, 18.57, 18.13]
-[79.86]
-14.186228513717651
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 88156, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.868364572525024, 'TIME_S_1KI': 0.12328559113985461, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1132.9122091054917, 'W': 79.86}
-[18.49, 17.88, 18.37, 17.92, 18.86, 17.84, 18.14, 20.87, 18.57, 18.13, 18.45, 17.94, 18.01, 21.62, 18.32, 18.14, 18.03, 18.0, 18.36, 17.84]
-333.32500000000005
-16.66625
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 88156, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.868364572525024, 'TIME_S_1KI': 0.12328559113985461, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1132.9122091054917, 'W': 79.86, 'J_1KI': 12.851220666834834, 'W_1KI': 0.9058940968283498, 'W_D': 63.193749999999994, 'J_D': 896.4809781387447, 'W_D_1KI': 0.716840033576841, 'J_D_1KI': 0.00813149455030674}
+[19.17, 18.37, 18.62, 19.0, 18.76, 19.72, 18.55, 18.79, 18.81, 18.44]
+[88.47]
+13.90942907333374
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87980, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.691504955291748, 'TIME_S_1KI': 0.12152199312675321, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1230.567190117836, 'W': 88.47}
+[19.17, 18.37, 18.62, 19.0, 18.76, 19.72, 18.55, 18.79, 18.81, 18.44, 18.89, 18.83, 18.65, 18.38, 18.48, 18.74, 18.64, 18.42, 21.9, 19.09]
+340.45500000000004
+17.022750000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87980, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.691504955291748, 'TIME_S_1KI': 0.12152199312675321, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1230.567190117836, 'W': 88.47, 'J_1KI': 13.98689690972762, 'W_1KI': 1.0055694476017276, 'W_D': 71.44725, 'J_D': 993.790456359744, 'W_D_1KI': 0.8120851329847691, 'J_D_1KI': 0.009230337951634112}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.json
index 63a6b21..aacc2c9 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 339415, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.661308526992798, "TIME_S_1KI": 0.031410834898259646, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1042.263754928112, "W": 73.63, "J_1KI": 3.0707651545397576, "W_1KI": 0.2169320743043177, "W_D": 56.882, "J_D": 805.1887397503853, "W_D_1KI": 0.1675883505443189, "J_D_1KI": 0.0004937564649303033}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 340799, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.474717378616333, "TIME_S_1KI": 0.03073576324641895, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1184.1861459732056, "W": 82.2, "J_1KI": 3.474734802546972, "W_1KI": 0.24119789083888157, "W_D": 65.00150000000001, "J_D": 936.4218463196755, "W_D_1KI": 0.19073266060052993, "J_D_1KI": 0.0005596632049992222}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.output
index 4acadea..d0dfbed 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,14 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.02093958854675293}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.020232677459716797}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([ 145,  181,  299,  ..., 1340, 4416,  166]),
-       values=tensor([0.2713, 0.7441, 0.5681,  ..., 0.3863, 0.3329, 0.3299]),
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([2628, 3754,  592,  ..., 2458, 3315, 4944]),
+       values=tensor([0.4123, 0.7806, 0.8053,  ..., 0.5946, 0.1806, 0.3377]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([6.8436e-01, 7.7662e-01, 5.3826e-02,  ..., 2.4557e-01, 1.4818e-01,
-        6.5619e-04])
+tensor([0.7124, 0.1921, 0.3514,  ..., 0.3599, 0.5299, 0.1827])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.02093958854675293 seconds
+Time: 0.020232677459716797 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '50144', '-ss', '5000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.5512330532073975}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '51896', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.6946630477905273}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([ 261, 2124, 2825,  ..., 2342, 1684, 3815]),
-       values=tensor([0.1132, 0.9807, 0.3410,  ..., 0.0783, 0.3569, 0.0713]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([4256, 1296, 4417,  ..., 3148, 1518, 3293]),
+       values=tensor([0.6152, 0.7132, 0.3577,  ..., 0.6179, 0.4799, 0.9734]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.8182, 0.0907, 0.1359,  ..., 0.4059, 0.0754, 0.0727])
+tensor([0.2217, 0.2835, 0.9132,  ..., 0.3426, 0.7119, 0.8690])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -35,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 1.5512330532073975 seconds
+Time: 1.6946630477905273 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '339415', '-ss', '5000', '-sd', '0.0001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.661308526992798}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '321543', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.906718492507935}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2497, 2498, 2500]),
-       col_indices=tensor([  91, 2131, 2855,  ..., 2446,  470, 1581]),
-       values=tensor([0.9229, 0.3729, 0.6792,  ..., 0.1416, 0.2267, 0.3921]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2498, 2498, 2500]),
+       col_indices=tensor([ 523, 3699, 1839,  ..., 2785,  659, 2568]),
+       values=tensor([0.0225, 0.6703, 0.1062,  ..., 0.0367, 0.5194, 0.4406]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.5750, 0.0749, 0.6665,  ..., 0.8045, 0.0578, 0.3106])
+tensor([0.2271, 0.1701, 0.4937,  ..., 0.4807, 0.0704, 0.7192])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -54,15 +53,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.661308526992798 seconds
+Time: 9.906718492507935 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '340799', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.474717378616333}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2497, 2498, 2500]),
-       col_indices=tensor([  91, 2131, 2855,  ..., 2446,  470, 1581]),
-       values=tensor([0.9229, 0.3729, 0.6792,  ..., 0.1416, 0.2267, 0.3921]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([3046, 2614, 1442,  ..., 2131, 1160,  199]),
+       values=tensor([0.0087, 0.6144, 0.0411,  ..., 0.0390, 0.8578, 0.8978]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.5750, 0.0749, 0.6665,  ..., 0.8045, 0.0578, 0.3106])
+tensor([0.5522, 0.2768, 0.6866,  ..., 0.7223, 0.2595, 0.0285])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -70,13 +72,29 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.661308526992798 seconds
+Time: 10.474717378616333 seconds
 
-[18.68, 17.92, 18.02, 18.26, 18.14, 18.26, 18.3, 17.84, 18.15, 17.87]
-[73.63]
-14.155422449111938
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 339415, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.661308526992798, 'TIME_S_1KI': 0.031410834898259646, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1042.263754928112, 'W': 73.63}
-[18.68, 17.92, 18.02, 18.26, 18.14, 18.26, 18.3, 17.84, 18.15, 17.87, 18.33, 18.37, 18.02, 18.02, 22.34, 18.22, 18.25, 22.38, 17.9, 18.26]
-334.9599999999999
-16.747999999999998
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 339415, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.661308526992798, 'TIME_S_1KI': 0.031410834898259646, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1042.263754928112, 'W': 73.63, 'J_1KI': 3.0707651545397576, 'W_1KI': 0.2169320743043177, 'W_D': 56.882, 'J_D': 805.1887397503853, 'W_D_1KI': 0.1675883505443189, 'J_D_1KI': 0.0004937564649303033}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([3046, 2614, 1442,  ..., 2131, 1160,  199]),
+       values=tensor([0.0087, 0.6144, 0.0411,  ..., 0.0390, 0.8578, 0.8978]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.5522, 0.2768, 0.6866,  ..., 0.7223, 0.2595, 0.0285])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.474717378616333 seconds
+
+[19.26, 18.52, 18.76, 18.46, 18.71, 19.29, 18.61, 18.48, 18.54, 20.56]
+[82.2]
+14.406157493591309
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 340799, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.474717378616333, 'TIME_S_1KI': 0.03073576324641895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1184.1861459732056, 'W': 82.2}
+[19.26, 18.52, 18.76, 18.46, 18.71, 19.29, 18.61, 18.48, 18.54, 20.56, 19.09, 18.94, 18.73, 18.56, 18.9, 23.81, 19.07, 18.44, 19.46, 18.47]
+343.9699999999999
+17.198499999999996
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 340799, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.474717378616333, 'TIME_S_1KI': 0.03073576324641895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1184.1861459732056, 'W': 82.2, 'J_1KI': 3.474734802546972, 'W_1KI': 0.24119789083888157, 'W_D': 65.00150000000001, 'J_D': 936.4218463196755, 'W_D_1KI': 0.19073266060052993, 'J_D_1KI': 0.0005596632049992222}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.json
index 4f7c98c..25b8108 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 242735, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.6920804977417, "TIME_S_1KI": 0.044048367552028754, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1022.2163880014419, "W": 74.91, "J_1KI": 4.211244311703883, "W_1KI": 0.30860815292397054, "W_D": 58.500499999999995, "J_D": 798.2935496766567, "W_D_1KI": 0.241005623416483, "J_D_1KI": 0.000992875454369922}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 242501, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.588265895843506, "TIME_S_1KI": 0.04366277209513984, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1181.5985651540755, "W": 83.01999999999998, "J_1KI": 4.872551309702127, "W_1KI": 0.3423491037150362, "W_D": 66.15374999999997, "J_D": 941.5463271448012, "W_D_1KI": 0.2727978441326014, "J_D_1KI": 0.0011249349245265024}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.output
index 6818997..fe2e3ea 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.019707918167114258}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.021615982055664062}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     9,  ..., 24992, 24998, 25000]),
-       col_indices=tensor([2443, 3271, 4233,  ..., 3520, 3792, 4350]),
-       values=tensor([0.8426, 0.5824, 0.3389,  ..., 0.9840, 0.1147, 0.8239]),
+tensor(crow_indices=tensor([    0,     5,    11,  ..., 24992, 24995, 25000]),
+       col_indices=tensor([ 538, 1794, 1869,  ...,  620,  754, 2185]),
+       values=tensor([0.7958, 0.9810, 0.8441,  ..., 0.1848, 0.5789, 0.4718]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5471, 0.6226, 0.3278,  ..., 0.2451, 0.7959, 0.6112])
+tensor([0.9458, 0.8492, 0.0516,  ..., 0.5673, 0.4627, 0.7477])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.019707918167114258 seconds
+Time: 0.021615982055664062 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53278', '-ss', '5000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.304640293121338}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '48575', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.1032345294952393}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     9,    13,  ..., 24990, 24994, 25000]),
-       col_indices=tensor([ 876,  897, 2274,  ..., 2103, 3712, 4740]),
-       values=tensor([0.9508, 0.2626, 0.2379,  ..., 0.2341, 0.9066, 0.6182]),
+tensor(crow_indices=tensor([    0,     2,     7,  ..., 24994, 24997, 25000]),
+       col_indices=tensor([1341, 3194,   65,  ...,  424, 2435, 3817]),
+       values=tensor([0.3518, 0.9410, 0.4009,  ..., 0.7954, 0.6370, 0.0462]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8511, 0.0306, 0.7639,  ..., 0.5025, 0.8599, 0.9690])
+tensor([0.6184, 0.6272, 0.7963,  ..., 0.5192, 0.6796, 0.6431])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 2.304640293121338 seconds
+Time: 2.1032345294952393 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '242735', '-ss', '5000', '-sd', '0.001', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.6920804977417}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '242501', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.588265895843506}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     6,    10,  ..., 24991, 24997, 25000]),
-       col_indices=tensor([1152, 1177, 2929,  ..., 1264, 2609, 4571]),
-       values=tensor([0.4587, 0.9835, 0.0653,  ..., 0.8655, 0.2110, 0.7343]),
+tensor(crow_indices=tensor([    0,     4,     9,  ..., 24992, 24996, 25000]),
+       col_indices=tensor([1959, 1972, 3833,  ...,  874, 3224, 4993]),
+       values=tensor([0.1621, 0.3934, 0.1039,  ..., 0.7772, 0.8981, 0.7652]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5612, 0.6517, 0.4101,  ..., 0.1691, 0.2414, 0.3717])
+tensor([0.4878, 0.7321, 0.0138,  ..., 0.8024, 0.2204, 0.3168])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.6920804977417 seconds
+Time: 10.588265895843506 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     6,    10,  ..., 24991, 24997, 25000]),
-       col_indices=tensor([1152, 1177, 2929,  ..., 1264, 2609, 4571]),
-       values=tensor([0.4587, 0.9835, 0.0653,  ..., 0.8655, 0.2110, 0.7343]),
+tensor(crow_indices=tensor([    0,     4,     9,  ..., 24992, 24996, 25000]),
+       col_indices=tensor([1959, 1972, 3833,  ...,  874, 3224, 4993]),
+       values=tensor([0.1621, 0.3934, 0.1039,  ..., 0.7772, 0.8981, 0.7652]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5612, 0.6517, 0.4101,  ..., 0.1691, 0.2414, 0.3717])
+tensor([0.4878, 0.7321, 0.0138,  ..., 0.8024, 0.2204, 0.3168])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.6920804977417 seconds
+Time: 10.588265895843506 seconds
 
-[18.79, 17.83, 18.25, 17.86, 18.02, 18.39, 18.09, 17.82, 18.07, 17.75]
-[74.91]
-13.64592695236206
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 242735, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.6920804977417, 'TIME_S_1KI': 0.044048367552028754, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1022.2163880014419, 'W': 74.91}
-[18.79, 17.83, 18.25, 17.86, 18.02, 18.39, 18.09, 17.82, 18.07, 17.75, 18.54, 18.01, 18.05, 19.12, 19.51, 18.12, 18.41, 17.83, 18.29, 17.96]
-328.19
-16.4095
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 242735, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.6920804977417, 'TIME_S_1KI': 0.044048367552028754, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1022.2163880014419, 'W': 74.91, 'J_1KI': 4.211244311703883, 'W_1KI': 0.30860815292397054, 'W_D': 58.500499999999995, 'J_D': 798.2935496766567, 'W_D_1KI': 0.241005623416483, 'J_D_1KI': 0.000992875454369922}
+[19.01, 18.78, 19.12, 18.48, 18.82, 18.63, 18.62, 18.49, 18.85, 18.55]
+[83.02]
+14.23269772529602
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 242501, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.588265895843506, 'TIME_S_1KI': 0.04366277209513984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1181.5985651540755, 'W': 83.01999999999998}
+[19.01, 18.78, 19.12, 18.48, 18.82, 18.63, 18.62, 18.49, 18.85, 18.55, 19.01, 18.56, 19.17, 18.58, 18.61, 18.4, 18.91, 18.6, 19.13, 18.58]
+337.325
+16.86625
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 242501, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.588265895843506, 'TIME_S_1KI': 0.04366277209513984, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1181.5985651540755, 'W': 83.01999999999998, 'J_1KI': 4.872551309702127, 'W_1KI': 0.3423491037150362, 'W_D': 66.15374999999997, 'J_D': 941.5463271448012, 'W_D_1KI': 0.2727978441326014, 'J_D_1KI': 0.0011249349245265024}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.json
index 64ab37a..284b6b4 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 161950, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.493181943893433, "TIME_S_1KI": 0.06479272580360255, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1161.5640621185303, "W": 84.0, "J_1KI": 7.172362223640199, "W_1KI": 0.5186786045075641, "W_D": 67.18725, "J_D": 929.0749408639671, "W_D_1KI": 0.41486415560358136, "J_D_1KI": 0.0025616804915318393}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 160288, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.41415810585022, "TIME_S_1KI": 0.06497153939066069, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1300.6230839395523, "W": 90.31, "J_1KI": 8.114288555222801, "W_1KI": 0.5634233379916151, "W_D": 73.274, "J_D": 1055.2746744832993, "W_D_1KI": 0.45713964863246154, "J_D_1KI": 0.002851989223350853}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.output
index f4d043e..c1f07bf 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.022989749908447266}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.022222280502319336}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     53,    112,  ..., 249913, 249957,
+tensor(crow_indices=tensor([     0,     65,    108,  ..., 249912, 249955,
                             250000]),
-       col_indices=tensor([  48,   54,  234,  ..., 4368, 4853, 4864]),
-       values=tensor([0.3452, 0.1008, 0.0125,  ..., 0.4983, 0.8936, 0.7126]),
+       col_indices=tensor([  39,  109,  188,  ..., 4800, 4890, 4910]),
+       values=tensor([0.0753, 0.9561, 0.7366,  ..., 0.1284, 0.9854, 0.3908]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5619, 0.6285, 0.8433,  ..., 0.7149, 0.5039, 0.6932])
+tensor([0.1414, 0.2099, 0.8694,  ..., 0.2280, 0.9608, 0.7506])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 0.022989749908447266 seconds
+Time: 0.022222280502319336 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '45672', '-ss', '5000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.961124897003174}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '47249', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.095125675201416}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     53,     98,  ..., 249913, 249948,
+tensor(crow_indices=tensor([     0,     40,     94,  ..., 249926, 249968,
                             250000]),
-       col_indices=tensor([ 202,  295,  369,  ..., 4836, 4929, 4943]),
-       values=tensor([0.0950, 0.5576, 0.0327,  ..., 0.3192, 0.9052, 0.1110]),
+       col_indices=tensor([  87,  303,  372,  ..., 4332, 4413, 4812]),
+       values=tensor([0.0586, 0.1707, 0.3614,  ..., 0.6651, 0.1911, 0.3417]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.6070, 0.1874, 0.1819,  ..., 0.4756, 0.1999, 0.3064])
+tensor([0.0815, 0.5844, 0.5335,  ..., 0.2225, 0.7876, 0.4789])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 2.961124897003174 seconds
+Time: 3.095125675201416 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '161950', '-ss', '5000', '-sd', '0.01', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.493181943893433}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '160288', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.41415810585022}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     48,    111,  ..., 249911, 249964,
+tensor(crow_indices=tensor([     0,     33,     67,  ..., 249892, 249948,
                             250000]),
-       col_indices=tensor([  19,   43,  144,  ..., 4843, 4924, 4947]),
-       values=tensor([0.0652, 0.5238, 0.9360,  ..., 0.5118, 0.2782, 0.1343]),
+       col_indices=tensor([ 195,  263,  291,  ..., 4372, 4543, 4623]),
+       values=tensor([0.5403, 0.8262, 0.1866,  ..., 0.1892, 0.9511, 0.1533]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4213, 0.5109, 0.7737,  ..., 0.0670, 0.5810, 0.9899])
+tensor([0.0116, 0.2797, 0.4745,  ..., 0.5306, 0.6019, 0.8794])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.493181943893433 seconds
+Time: 10.41415810585022 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     48,    111,  ..., 249911, 249964,
+tensor(crow_indices=tensor([     0,     33,     67,  ..., 249892, 249948,
                             250000]),
-       col_indices=tensor([  19,   43,  144,  ..., 4843, 4924, 4947]),
-       values=tensor([0.0652, 0.5238, 0.9360,  ..., 0.5118, 0.2782, 0.1343]),
+       col_indices=tensor([ 195,  263,  291,  ..., 4372, 4543, 4623]),
+       values=tensor([0.5403, 0.8262, 0.1866,  ..., 0.1892, 0.9511, 0.1533]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4213, 0.5109, 0.7737,  ..., 0.0670, 0.5810, 0.9899])
+tensor([0.0116, 0.2797, 0.4745,  ..., 0.5306, 0.6019, 0.8794])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.493181943893433 seconds
+Time: 10.41415810585022 seconds
 
-[18.86, 18.02, 19.18, 18.0, 18.43, 21.89, 19.39, 17.96, 18.27, 17.81]
-[84.0]
-13.82814359664917
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 161950, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.493181943893433, 'TIME_S_1KI': 0.06479272580360255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1161.5640621185303, 'W': 84.0}
-[18.86, 18.02, 19.18, 18.0, 18.43, 21.89, 19.39, 17.96, 18.27, 17.81, 18.1, 20.23, 20.02, 18.19, 17.98, 17.88, 18.2, 17.87, 18.32, 18.08]
-336.255
-16.81275
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 161950, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.493181943893433, 'TIME_S_1KI': 0.06479272580360255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1161.5640621185303, 'W': 84.0, 'J_1KI': 7.172362223640199, 'W_1KI': 0.5186786045075641, 'W_D': 67.18725, 'J_D': 929.0749408639671, 'W_D_1KI': 0.41486415560358136, 'J_D_1KI': 0.0025616804915318393}
+[18.89, 18.49, 18.95, 21.32, 19.61, 18.65, 19.28, 18.61, 18.59, 18.51]
+[90.31]
+14.401761531829834
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 160288, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.41415810585022, 'TIME_S_1KI': 0.06497153939066069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.6230839395523, 'W': 90.31}
+[18.89, 18.49, 18.95, 21.32, 19.61, 18.65, 19.28, 18.61, 18.59, 18.51, 19.15, 18.78, 18.93, 18.53, 18.69, 18.51, 18.5, 18.5, 18.83, 19.35]
+340.71999999999997
+17.035999999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 160288, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.41415810585022, 'TIME_S_1KI': 0.06497153939066069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.6230839395523, 'W': 90.31, 'J_1KI': 8.114288555222801, 'W_1KI': 0.5634233379916151, 'W_D': 73.274, 'J_D': 1055.2746744832993, 'W_D_1KI': 0.45713964863246154, 'J_D_1KI': 0.002851989223350853}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json
index c0ca085..02d1c44 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 46969, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.723698854446411, "TIME_S_1KI": 0.22831439575989293, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1262.0827813720703, "W": 87.97, "J_1KI": 26.87054826315379, "W_1KI": 1.8729374693947072, "W_D": 71.6635, "J_D": 1028.1376537780761, "W_D_1KI": 1.5257616725925611, "J_D_1KI": 0.03248444021785776}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 43317, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.514539003372192, "TIME_S_1KI": 0.24273470008015774, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1282.2987394380568, "W": 89.82, "J_1KI": 29.602667300091348, "W_1KI": 2.073550799916892, "W_D": 72.69899999999998, "J_D": 1037.8739262793063, "W_D_1KI": 1.6783018214557792, "J_D_1KI": 0.03874464578469837}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.output
index 62668bd..21a66b8 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03811240196228027}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03870248794555664}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     252,     486,  ..., 1249499,
-                            1249768, 1250000]),
-       col_indices=tensor([  15,   65,   81,  ..., 4947, 4948, 4952]),
-       values=tensor([0.2497, 0.0794, 0.3182,  ..., 0.5399, 0.7483, 0.2341]),
+tensor(crow_indices=tensor([      0,     249,     456,  ..., 1249509,
+                            1249760, 1250000]),
+       col_indices=tensor([  11,   61,   68,  ..., 4983, 4987, 4999]),
+       values=tensor([0.8543, 0.3334, 0.8458,  ..., 0.8706, 0.3239, 0.8971]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.6929, 0.1024, 0.7145,  ..., 0.7803, 0.6014, 0.5585])
+tensor([0.1640, 0.6869, 0.3358,  ..., 0.6749, 0.0366, 0.9855])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 0.03811240196228027 seconds
+Time: 0.03870248794555664 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '27550', '-ss', '5000', '-sd', '0.05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 6.158770799636841}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '27130', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 6.576195955276489}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     250,     496,  ..., 1249504,
-                            1249751, 1250000]),
-       col_indices=tensor([  13,   88,   93,  ..., 4888, 4919, 4936]),
-       values=tensor([0.3293, 0.4307, 0.3782,  ..., 0.2045, 0.2965, 0.3765]),
+tensor(crow_indices=tensor([      0,     251,     503,  ..., 1249463,
+                            1249716, 1250000]),
+       col_indices=tensor([   0,   17,   18,  ..., 4944, 4963, 4970]),
+       values=tensor([0.0874, 0.9701, 0.9470,  ..., 0.5996, 0.4415, 0.6823]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.2742, 0.5184, 0.9225,  ..., 0.0830, 0.2762, 0.4744])
+tensor([0.2939, 0.4606, 0.6555,  ..., 0.6332, 0.8096, 0.8939])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 6.158770799636841 seconds
+Time: 6.576195955276489 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '46969', '-ss', '5000', '-sd', '0.05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.723698854446411}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '43317', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.514539003372192}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     267,     518,  ..., 1249533,
-                            1249757, 1250000]),
-       col_indices=tensor([   4,   12,   26,  ..., 4948, 4976, 4977]),
-       values=tensor([0.8262, 0.2304, 0.8718,  ..., 0.9371, 0.9418, 0.0811]),
+tensor(crow_indices=tensor([      0,     225,     465,  ..., 1249521,
+                            1249744, 1250000]),
+       col_indices=tensor([  10,   14,   60,  ..., 4938, 4947, 4964]),
+       values=tensor([0.1905, 0.3997, 0.9962,  ..., 0.5332, 0.4508, 0.1955]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.8290, 0.5732, 0.0178,  ..., 0.9134, 0.7238, 0.4621])
+tensor([0.0493, 0.7294, 0.8838,  ..., 0.4629, 0.2283, 0.0408])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.723698854446411 seconds
+Time: 10.514539003372192 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     267,     518,  ..., 1249533,
-                            1249757, 1250000]),
-       col_indices=tensor([   4,   12,   26,  ..., 4948, 4976, 4977]),
-       values=tensor([0.8262, 0.2304, 0.8718,  ..., 0.9371, 0.9418, 0.0811]),
+tensor(crow_indices=tensor([      0,     225,     465,  ..., 1249521,
+                            1249744, 1250000]),
+       col_indices=tensor([  10,   14,   60,  ..., 4938, 4947, 4964]),
+       values=tensor([0.1905, 0.3997, 0.9962,  ..., 0.5332, 0.4508, 0.1955]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.8290, 0.5732, 0.0178,  ..., 0.9134, 0.7238, 0.4621])
+tensor([0.0493, 0.7294, 0.8838,  ..., 0.4629, 0.2283, 0.0408])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.723698854446411 seconds
+Time: 10.514539003372192 seconds
 
-[18.22, 17.77, 18.1, 17.81, 18.08, 18.08, 18.29, 17.93, 18.14, 17.97]
-[87.97]
-14.34674072265625
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46969, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.723698854446411, 'TIME_S_1KI': 0.22831439575989293, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1262.0827813720703, 'W': 87.97}
-[18.22, 17.77, 18.1, 17.81, 18.08, 18.08, 18.29, 17.93, 18.14, 17.97, 18.49, 18.0, 18.1, 18.25, 18.1, 17.98, 17.87, 18.07, 17.92, 20.6]
-326.13
-16.3065
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46969, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.723698854446411, 'TIME_S_1KI': 0.22831439575989293, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1262.0827813720703, 'W': 87.97, 'J_1KI': 26.87054826315379, 'W_1KI': 1.8729374693947072, 'W_D': 71.6635, 'J_D': 1028.1376537780761, 'W_D_1KI': 1.5257616725925611, 'J_D_1KI': 0.03248444021785776}
+[18.88, 18.95, 18.56, 18.93, 18.61, 18.37, 18.57, 18.8, 18.58, 18.47]
+[89.82]
+14.276316404342651
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 43317, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.514539003372192, 'TIME_S_1KI': 0.24273470008015774, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.2987394380568, 'W': 89.82}
+[18.88, 18.95, 18.56, 18.93, 18.61, 18.37, 18.57, 18.8, 18.58, 18.47, 19.07, 18.87, 18.54, 18.68, 18.92, 18.59, 23.9, 19.06, 18.7, 19.16]
+342.42
+17.121000000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 43317, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.514539003372192, 'TIME_S_1KI': 0.24273470008015774, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1282.2987394380568, 'W': 89.82, 'J_1KI': 29.602667300091348, 'W_1KI': 2.073550799916892, 'W_D': 72.69899999999998, 'J_D': 1037.8739262793063, 'W_D_1KI': 1.6783018214557792, 'J_D_1KI': 0.03874464578469837}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json
index fe585f9..7a2dbef 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 19220, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.306657314300537, "TIME_S_1KI": 0.536246478371516, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1258.2980075454711, "W": 87.22999999999999, "J_1KI": 65.46815856115875, "W_1KI": 4.538501560874089, "W_D": 70.87349999999999, "J_D": 1022.354509202957, "W_D_1KI": 3.6874869927159204, "J_D_1KI": 0.19185676340873678}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 19195, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.674326419830322, "TIME_S_1KI": 0.5560993185637052, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1320.7079879951475, "W": 89.32, "J_1KI": 68.80479228940597, "W_1KI": 4.653295128939828, "W_D": 72.452, "J_D": 1071.2934969348908, "W_D_1KI": 3.7745246157853605, "J_D_1KI": 0.19664103234099298}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output
index 2db63b3..3982142 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.06937050819396973}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.06820058822631836}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     517,    1013,  ..., 2498965,
-                            2499518, 2500000]),
-       col_indices=tensor([  16,   31,   39,  ..., 4973, 4987, 4996]),
-       values=tensor([0.8092, 0.6907, 0.1859,  ..., 0.6156, 0.1820, 0.0827]),
+tensor(crow_indices=tensor([      0,     527,    1022,  ..., 2498961,
+                            2499470, 2500000]),
+       col_indices=tensor([   3,    9,   18,  ..., 4988, 4996, 4997]),
+       values=tensor([0.9931, 0.9342, 0.5454,  ..., 0.8792, 0.3313, 0.4412]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0341, 0.1297, 0.9553,  ..., 0.9560, 0.1422, 0.0438])
+tensor([0.4695, 0.7281, 0.8416,  ..., 0.2227, 0.0195, 0.5717])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 0.06937050819396973 seconds
+Time: 0.06820058822631836 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '15136', '-ss', '5000', '-sd', '0.1', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.268742084503174}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '15395', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.421234846115112}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     498,    1015,  ..., 2498995,
-                            2499510, 2500000]),
-       col_indices=tensor([   1,    5,   12,  ..., 4987, 4992, 4994]),
-       values=tensor([0.2706, 0.5291, 0.0606,  ..., 0.9998, 0.6766, 0.8077]),
+tensor(crow_indices=tensor([      0,     525,     988,  ..., 2498961,
+                            2499466, 2500000]),
+       col_indices=tensor([   0,    4,   11,  ..., 4982, 4991, 4997]),
+       values=tensor([0.8697, 0.9027, 0.4817,  ..., 0.5631, 0.4015, 0.3568]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5238, 0.3873, 0.9372,  ..., 0.7751, 0.3587, 0.1743])
+tensor([0.9397, 0.4161, 0.2766,  ..., 0.4206, 0.4046, 0.6689])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 8.268742084503174 seconds
+Time: 8.421234846115112 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '19220', '-ss', '5000', '-sd', '0.1', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.306657314300537}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '19195', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.674326419830322}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     478,     976,  ..., 2498986,
-                            2499483, 2500000]),
-       col_indices=tensor([   0,   15,   20,  ..., 4988, 4995, 4997]),
-       values=tensor([0.9696, 0.2544, 0.3304,  ..., 0.5139, 0.4686, 0.4850]),
+tensor(crow_indices=tensor([      0,     474,     982,  ..., 2498973,
+                            2499464, 2500000]),
+       col_indices=tensor([   0,    4,   10,  ..., 4986, 4995, 4998]),
+       values=tensor([0.6694, 0.2603, 0.0355,  ..., 0.0763, 0.4858, 0.2671]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8873, 0.6521, 0.3260,  ..., 0.9177, 0.3863, 0.5956])
+tensor([0.9454, 0.3117, 0.1592,  ..., 0.6761, 0.9412, 0.4124])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.306657314300537 seconds
+Time: 10.674326419830322 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     478,     976,  ..., 2498986,
-                            2499483, 2500000]),
-       col_indices=tensor([   0,   15,   20,  ..., 4988, 4995, 4997]),
-       values=tensor([0.9696, 0.2544, 0.3304,  ..., 0.5139, 0.4686, 0.4850]),
+tensor(crow_indices=tensor([      0,     474,     982,  ..., 2498973,
+                            2499464, 2500000]),
+       col_indices=tensor([   0,    4,   10,  ..., 4986, 4995, 4998]),
+       values=tensor([0.6694, 0.2603, 0.0355,  ..., 0.0763, 0.4858, 0.2671]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8873, 0.6521, 0.3260,  ..., 0.9177, 0.3863, 0.5956])
+tensor([0.9454, 0.3117, 0.1592,  ..., 0.6761, 0.9412, 0.4124])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.306657314300537 seconds
+Time: 10.674326419830322 seconds
 
-[18.67, 18.15, 17.98, 18.09, 18.08, 18.12, 18.09, 17.98, 17.86, 18.03]
-[87.23]
-14.425060272216797
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19220, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.306657314300537, 'TIME_S_1KI': 0.536246478371516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1258.2980075454711, 'W': 87.22999999999999}
-[18.67, 18.15, 17.98, 18.09, 18.08, 18.12, 18.09, 17.98, 17.86, 18.03, 18.76, 17.96, 18.22, 17.88, 18.31, 18.19, 18.22, 17.8, 18.27, 20.4]
-327.13
-16.3565
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19220, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.306657314300537, 'TIME_S_1KI': 0.536246478371516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1258.2980075454711, 'W': 87.22999999999999, 'J_1KI': 65.46815856115875, 'W_1KI': 4.538501560874089, 'W_D': 70.87349999999999, 'J_D': 1022.354509202957, 'W_D_1KI': 3.6874869927159204, 'J_D_1KI': 0.19185676340873678}
+[19.27, 18.44, 18.93, 18.6, 18.67, 18.58, 19.07, 19.64, 18.53, 18.58]
+[89.32]
+14.786251544952393
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19195, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.674326419830322, 'TIME_S_1KI': 0.5560993185637052, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1320.7079879951475, 'W': 89.32}
+[19.27, 18.44, 18.93, 18.6, 18.67, 18.58, 19.07, 19.64, 18.53, 18.58, 19.02, 18.67, 18.78, 18.82, 18.69, 18.42, 18.7, 18.66, 18.5, 18.45]
+337.36
+16.868000000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19195, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.674326419830322, 'TIME_S_1KI': 0.5560993185637052, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1320.7079879951475, 'W': 89.32, 'J_1KI': 68.80479228940597, 'W_1KI': 4.653295128939828, 'W_D': 72.452, 'J_D': 1071.2934969348908, 'W_D_1KI': 3.7745246157853605, 'J_D_1KI': 0.19664103234099298}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json
index 5da43df..4dac3eb 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 9074, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.487424612045288, "TIME_S_1KI": 1.1557664328901573, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1301.6191907930374, "W": 85.83, "J_1KI": 143.44491853571054, "W_1KI": 9.458893541988099, "W_D": 69.53375, "J_D": 1054.4851847583054, "W_D_1KI": 7.662965616045845, "J_D_1KI": 0.8444969821518454}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8923, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.545594930648804, "TIME_S_1KI": 1.1818441029529085, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1330.735052251816, "W": 87.79, "J_1KI": 149.13538633327533, "W_1KI": 9.838619298442229, "W_D": 70.932, "J_D": 1075.1987552833557, "W_D_1KI": 7.949344390899922, "J_D_1KI": 0.89088248244984}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output
index 4c63e35..4bc8723 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.12977051734924316}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.13272905349731445}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1037,    2033,  ..., 4997947,
-                            4998969, 5000000]),
-       col_indices=tensor([   0,    3,   11,  ..., 4985, 4988, 4990]),
-       values=tensor([0.1539, 0.2882, 0.0917,  ..., 0.8336, 0.9260, 0.3814]),
+tensor(crow_indices=tensor([      0,     997,    1995,  ..., 4998002,
+                            4998986, 5000000]),
+       col_indices=tensor([   7,    8,   17,  ..., 4982, 4983, 4992]),
+       values=tensor([0.4184, 0.9767, 0.6771,  ..., 0.6978, 0.0394, 0.2404]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.9778, 0.3097, 0.5480,  ..., 0.9590, 0.3024, 0.0294])
+tensor([0.1273, 0.8365, 0.7708,  ..., 0.6648, 0.0907, 0.2516])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 0.12977051734924316 seconds
+Time: 0.13272905349731445 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8091', '-ss', '5000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.36232042312622}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7910', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.307441234588623}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     972,    1989,  ..., 4997978,
-                            4998985, 5000000]),
-       col_indices=tensor([   0,    9,   19,  ..., 4989, 4992, 4995]),
-       values=tensor([0.2585, 0.0110, 0.4823,  ..., 0.4314, 0.8099, 0.9487]),
+tensor(crow_indices=tensor([      0,     974,    1954,  ..., 4998031,
+                            4999055, 5000000]),
+       col_indices=tensor([   7,   10,   12,  ..., 4985, 4990, 4995]),
+       values=tensor([0.9202, 0.4707, 0.8760,  ..., 0.2235, 0.0017, 0.5563]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.7513, 0.6352, 0.8184,  ..., 0.0273, 0.2479, 0.5631])
+tensor([0.0317, 0.9028, 0.8670,  ..., 0.2249, 0.4204, 0.9373])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 9.36232042312622 seconds
+Time: 9.307441234588623 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9074', '-ss', '5000', '-sd', '0.2', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.487424612045288}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8923', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.545594930648804}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1061,    2118,  ..., 4997993,
-                            4998997, 5000000]),
-       col_indices=tensor([   3,    6,   10,  ..., 4996, 4998, 4999]),
-       values=tensor([0.5066, 0.7039, 0.5374,  ..., 0.1064, 0.9581, 0.5937]),
+tensor(crow_indices=tensor([      0,     983,    1975,  ..., 4998032,
+                            4998999, 5000000]),
+       col_indices=tensor([   6,   16,   20,  ..., 4983, 4987, 4992]),
+       values=tensor([0.2859, 0.1301, 0.3895,  ..., 0.6829, 0.0353, 0.2028]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1982, 0.1257, 0.6934,  ..., 0.0401, 0.8872, 0.0311])
+tensor([0.4672, 0.7822, 0.1527,  ..., 0.8581, 0.0623, 0.3252])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.487424612045288 seconds
+Time: 10.545594930648804 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1061,    2118,  ..., 4997993,
-                            4998997, 5000000]),
-       col_indices=tensor([   3,    6,   10,  ..., 4996, 4998, 4999]),
-       values=tensor([0.5066, 0.7039, 0.5374,  ..., 0.1064, 0.9581, 0.5937]),
+tensor(crow_indices=tensor([      0,     983,    1975,  ..., 4998032,
+                            4998999, 5000000]),
+       col_indices=tensor([   6,   16,   20,  ..., 4983, 4987, 4992]),
+       values=tensor([0.2859, 0.1301, 0.3895,  ..., 0.6829, 0.0353, 0.2028]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1982, 0.1257, 0.6934,  ..., 0.0401, 0.8872, 0.0311])
+tensor([0.4672, 0.7822, 0.1527,  ..., 0.8581, 0.0623, 0.3252])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.487424612045288 seconds
+Time: 10.545594930648804 seconds
 
-[18.43, 18.11, 17.89, 18.52, 18.92, 18.0, 18.0, 17.79, 18.16, 17.76]
-[85.83]
-15.16508436203003
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9074, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.487424612045288, 'TIME_S_1KI': 1.1557664328901573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.6191907930374, 'W': 85.83}
-[18.43, 18.11, 17.89, 18.52, 18.92, 18.0, 18.0, 17.79, 18.16, 17.76, 18.23, 17.97, 18.42, 17.87, 17.92, 18.08, 18.11, 17.82, 17.87, 18.53]
-325.925
-16.29625
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9074, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.487424612045288, 'TIME_S_1KI': 1.1557664328901573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.6191907930374, 'W': 85.83, 'J_1KI': 143.44491853571054, 'W_1KI': 9.458893541988099, 'W_D': 69.53375, 'J_D': 1054.4851847583054, 'W_D_1KI': 7.662965616045845, 'J_D_1KI': 0.8444969821518454}
+[19.05, 18.78, 18.53, 18.46, 18.67, 18.5, 18.62, 18.45, 18.84, 18.85]
+[87.79]
+15.158162117004395
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8923, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.545594930648804, 'TIME_S_1KI': 1.1818441029529085, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1330.735052251816, 'W': 87.79}
+[19.05, 18.78, 18.53, 18.46, 18.67, 18.5, 18.62, 18.45, 18.84, 18.85, 18.85, 18.82, 18.67, 19.46, 18.69, 19.1, 18.66, 18.45, 18.69, 18.79]
+337.16
+16.858
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8923, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.545594930648804, 'TIME_S_1KI': 1.1818441029529085, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1330.735052251816, 'W': 87.79, 'J_1KI': 149.13538633327533, 'W_1KI': 9.838619298442229, 'W_D': 70.932, 'J_D': 1075.1987552833557, 'W_D_1KI': 7.949344390899922, 'J_D_1KI': 0.89088248244984}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json
index c0e5e12..6906142 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5607, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.746073484420776, "TIME_S_1KI": 1.9165460111326513, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1333.0484231472017, "W": 84.51, "J_1KI": 237.7471773046552, "W_1KI": 15.07223113964687, "W_D": 68.30000000000001, "J_D": 1077.3542456626894, "W_D_1KI": 12.18120206884252, "J_D_1KI": 2.1724990313612484}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5529, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.533730745315552, "TIME_S_1KI": 1.905178286365627, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1373.9562348842621, "W": 86.44, "J_1KI": 248.49995204996603, "W_1KI": 15.633930186290467, "W_D": 69.4085, "J_D": 1103.2420329588651, "W_D_1KI": 12.553535901609694, "J_D_1KI": 2.270489401629534}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output
index 4501c06..5a9a550 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.20109891891479492}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.20404601097106934}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1526,    3035,  ..., 7496941,
-                            7498453, 7500000]),
-       col_indices=tensor([   2,    6,   10,  ..., 4993, 4994, 4996]),
-       values=tensor([0.3579, 0.5981, 0.5931,  ..., 0.3837, 0.5123, 0.1240]),
+tensor(crow_indices=tensor([      0,    1447,    2938,  ..., 7497029,
+                            7498441, 7500000]),
+       col_indices=tensor([   3,    5,    7,  ..., 4987, 4996, 4997]),
+       values=tensor([0.2329, 0.7294, 0.8836,  ..., 0.5208, 0.0518, 0.9364]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.0391, 0.3406, 0.5361,  ..., 0.5039, 0.0205, 0.2852])
+tensor([0.5789, 0.0903, 0.7224,  ..., 0.8391, 0.9209, 0.7019])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 0.20109891891479492 seconds
+Time: 0.20404601097106934 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5221', '-ss', '5000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.77708387374878}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5145', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.769284009933472}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1505,    2964,  ..., 7496939,
-                            7498442, 7500000]),
-       col_indices=tensor([   2,    4,    5,  ..., 4996, 4997, 4999]),
-       values=tensor([0.1729, 0.9195, 0.5163,  ..., 0.0436, 0.1803, 0.9350]),
+tensor(crow_indices=tensor([      0,    1458,    3003,  ..., 7496989,
+                            7498497, 7500000]),
+       col_indices=tensor([   1,    4,   10,  ..., 4989, 4990, 4994]),
+       values=tensor([0.1464, 0.4974, 0.0225,  ..., 0.5683, 0.0440, 0.9269]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.3955, 0.7196, 0.4505,  ..., 0.8904, 0.4770, 0.0844])
+tensor([0.7014, 0.8503, 0.5401,  ..., 0.5553, 0.6399, 0.6983])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 9.77708387374878 seconds
+Time: 9.769284009933472 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5607', '-ss', '5000', '-sd', '0.3', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.746073484420776}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5529', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.533730745315552}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1451,    2905,  ..., 7497061,
-                            7498533, 7500000]),
-       col_indices=tensor([   4,    9,   12,  ..., 4994, 4998, 4999]),
-       values=tensor([0.3703, 0.3823, 0.6108,  ..., 0.0984, 0.8524, 0.0373]),
+tensor(crow_indices=tensor([      0,    1537,    3056,  ..., 7497003,
+                            7498499, 7500000]),
+       col_indices=tensor([   6,   10,   11,  ..., 4981, 4989, 4990]),
+       values=tensor([0.6097, 0.4352, 0.7201,  ..., 0.3526, 0.9200, 0.7753]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.6688, 0.5149, 0.3458,  ..., 0.1151, 0.9310, 0.8037])
+tensor([0.1460, 0.3019, 0.8185,  ..., 0.1969, 0.4512, 0.0832])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.746073484420776 seconds
+Time: 10.533730745315552 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1451,    2905,  ..., 7497061,
-                            7498533, 7500000]),
-       col_indices=tensor([   4,    9,   12,  ..., 4994, 4998, 4999]),
-       values=tensor([0.3703, 0.3823, 0.6108,  ..., 0.0984, 0.8524, 0.0373]),
+tensor(crow_indices=tensor([      0,    1537,    3056,  ..., 7497003,
+                            7498499, 7500000]),
+       col_indices=tensor([   6,   10,   11,  ..., 4981, 4989, 4990]),
+       values=tensor([0.6097, 0.4352, 0.7201,  ..., 0.3526, 0.9200, 0.7753]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.6688, 0.5149, 0.3458,  ..., 0.1151, 0.9310, 0.8037])
+tensor([0.1460, 0.3019, 0.8185,  ..., 0.1969, 0.4512, 0.0832])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.746073484420776 seconds
+Time: 10.533730745315552 seconds
 
-[18.36, 17.95, 18.08, 17.67, 18.17, 17.84, 18.12, 17.84, 18.09, 18.06]
-[84.51]
-15.77385425567627
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5607, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.746073484420776, 'TIME_S_1KI': 1.9165460111326513, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1333.0484231472017, 'W': 84.51}
-[18.36, 17.95, 18.08, 17.67, 18.17, 17.84, 18.12, 17.84, 18.09, 18.06, 18.35, 17.89, 17.87, 18.07, 18.23, 17.83, 18.12, 17.92, 18.19, 17.87]
-324.20000000000005
-16.21
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5607, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.746073484420776, 'TIME_S_1KI': 1.9165460111326513, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1333.0484231472017, 'W': 84.51, 'J_1KI': 237.7471773046552, 'W_1KI': 15.07223113964687, 'W_D': 68.30000000000001, 'J_D': 1077.3542456626894, 'W_D_1KI': 12.18120206884252, 'J_D_1KI': 2.1724990313612484}
+[19.34, 18.74, 18.52, 18.62, 18.6, 18.73, 18.65, 19.79, 18.5, 18.66]
+[86.44]
+15.894912481307983
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5529, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.533730745315552, 'TIME_S_1KI': 1.905178286365627, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.9562348842621, 'W': 86.44}
+[19.34, 18.74, 18.52, 18.62, 18.6, 18.73, 18.65, 19.79, 18.5, 18.66, 18.87, 18.59, 18.65, 18.49, 18.51, 18.61, 18.87, 18.52, 22.37, 18.87]
+340.63
+17.0315
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5529, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.533730745315552, 'TIME_S_1KI': 1.905178286365627, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1373.9562348842621, 'W': 86.44, 'J_1KI': 248.49995204996603, 'W_1KI': 15.633930186290467, 'W_D': 69.4085, 'J_D': 1103.2420329588651, 'W_D_1KI': 12.553535901609694, 'J_D_1KI': 2.270489401629534}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json
index 363edc0..ead5e1d 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2843, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.510854721069336, "TIME_S_1KI": 3.6970997963662806, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1362.9070807647706, "W": 82.31, "J_1KI": 479.39046104986653, "W_1KI": 28.951811466760464, "W_D": 66.04650000000001, "J_D": 1093.6124712638857, "W_D_1KI": 23.23126978543792, "J_D_1KI": 8.171392819359099}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2846, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.30638337135315, "TIME_S_1KI": 3.621357474122681, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1425.0354087996482, "W": 85.47, "J_1KI": 500.715182290811, "W_1KI": 30.031623330990865, "W_D": 68.37174999999999, "J_D": 1139.957467083156, "W_D_1KI": 24.02380534082923, "J_D_1KI": 8.441252755034865}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.output
index 26353c7..66e74c6 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.3692905902862549}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.39473772048950195}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1942,     3937,  ...,  9996021,
-                             9998014, 10000000]),
-       col_indices=tensor([   1,    6,   11,  ..., 4995, 4996, 4997]),
-       values=tensor([0.9006, 0.6515, 0.7177,  ..., 0.3221, 0.1090, 0.6573]),
+tensor(crow_indices=tensor([       0,     2002,     3985,  ...,  9995971,
+                             9998009, 10000000]),
+       col_indices=tensor([   0,    2,    5,  ..., 4991, 4992, 4996]),
+       values=tensor([0.1620, 0.9199, 0.6443,  ..., 0.6479, 0.2394, 0.7085]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3731, 0.3806, 0.5183,  ..., 0.1665, 0.0476, 0.3514])
+tensor([0.6678, 0.3987, 0.1276,  ..., 0.4805, 0.4392, 0.0605])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 0.3692905902862549 seconds
+Time: 0.39473772048950195 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2843', '-ss', '5000', '-sd', '0.4', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.510854721069336}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2659', '-ss', '5000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 9.807755947113037}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2016,     4009,  ...,  9996028,
-                             9998016, 10000000]),
-       col_indices=tensor([   0,    3,    5,  ..., 4991, 4992, 4997]),
-       values=tensor([0.1122, 0.1212, 0.4120,  ..., 0.9869, 0.5095, 0.1756]),
+tensor(crow_indices=tensor([       0,     2021,     4024,  ...,  9995985,
+                             9998035, 10000000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4985, 4994, 4995]),
+       values=tensor([0.7018, 0.1471, 0.5279,  ..., 0.3317, 0.2521, 0.8359]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1243, 0.0753, 0.2426,  ..., 0.5390, 0.1485, 0.6469])
+tensor([0.1015, 0.1457, 0.5192,  ..., 0.4135, 0.5810, 0.6590])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 10.510854721069336 seconds
+Time: 9.807755947113037 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2846', '-ss', '5000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.30638337135315}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2016,     4009,  ...,  9996028,
-                             9998016, 10000000]),
-       col_indices=tensor([   0,    3,    5,  ..., 4991, 4992, 4997]),
-       values=tensor([0.1122, 0.1212, 0.4120,  ..., 0.9869, 0.5095, 0.1756]),
+tensor(crow_indices=tensor([       0,     1985,     3951,  ...,  9995929,
+                             9997921, 10000000]),
+       col_indices=tensor([   1,    3,    5,  ..., 4995, 4997, 4998]),
+       values=tensor([0.2102, 0.3320, 0.2208,  ..., 0.4653, 0.1673, 0.9633]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1243, 0.0753, 0.2426,  ..., 0.5390, 0.1485, 0.6469])
+tensor([0.2836, 0.6428, 0.7336,  ..., 0.3764, 0.7686, 0.5592])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +56,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 10.510854721069336 seconds
+Time: 10.30638337135315 seconds
 
-[18.34, 18.07, 17.87, 17.95, 18.1, 18.25, 18.13, 17.86, 18.35, 17.99]
-[82.31]
-16.55821990966797
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2843, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.510854721069336, 'TIME_S_1KI': 3.6970997963662806, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1362.9070807647706, 'W': 82.31}
-[18.34, 18.07, 17.87, 17.95, 18.1, 18.25, 18.13, 17.86, 18.35, 17.99, 18.23, 18.01, 18.07, 17.94, 18.3, 17.93, 18.17, 17.9, 18.02, 18.14]
-325.2699999999999
-16.263499999999997
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2843, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.510854721069336, 'TIME_S_1KI': 3.6970997963662806, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1362.9070807647706, 'W': 82.31, 'J_1KI': 479.39046104986653, 'W_1KI': 28.951811466760464, 'W_D': 66.04650000000001, 'J_D': 1093.6124712638857, 'W_D_1KI': 23.23126978543792, 'J_D_1KI': 8.171392819359099}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1985,     3951,  ...,  9995929,
+                             9997921, 10000000]),
+       col_indices=tensor([   1,    3,    5,  ..., 4995, 4997, 4998]),
+       values=tensor([0.2102, 0.3320, 0.2208,  ..., 0.4653, 0.1673, 0.9633]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2836, 0.6428, 0.7336,  ..., 0.3764, 0.7686, 0.5592])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.30638337135315 seconds
+
+[19.22, 18.77, 18.64, 18.81, 18.58, 18.67, 18.62, 18.73, 18.7, 18.37]
+[85.47]
+16.67293095588684
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2846, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.30638337135315, 'TIME_S_1KI': 3.621357474122681, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1425.0354087996482, 'W': 85.47}
+[19.22, 18.77, 18.64, 18.81, 18.58, 18.67, 18.62, 18.73, 18.7, 18.37, 18.99, 18.55, 18.63, 22.5, 19.62, 18.58, 19.47, 18.45, 19.02, 18.67]
+341.96500000000003
+17.09825
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2846, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.30638337135315, 'TIME_S_1KI': 3.621357474122681, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1425.0354087996482, 'W': 85.47, 'J_1KI': 500.715182290811, 'W_1KI': 30.031623330990865, 'W_D': 68.37174999999999, 'J_D': 1139.957467083156, 'W_D_1KI': 24.02380534082923, 'J_D_1KI': 8.441252755034865}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json
index 5cc798d..94053c3 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2367, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.31517243385315, "TIME_S_1KI": 4.35790977349098, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1435.467025976181, "W": 77.42, "J_1KI": 606.4499476029494, "W_1KI": 32.708069286016055, "W_D": 60.97375, "J_D": 1130.5322600764036, "W_D_1KI": 25.7599281791297, "J_D_1KI": 10.882943886408832}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2370, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.843660354614258, "TIME_S_1KI": 4.575384115870995, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1460.4018950557709, "W": 84.26, "J_1KI": 616.2033312471608, "W_1KI": 35.552742616033754, "W_D": 67.05925, "J_D": 1162.2769496916533, "W_D_1KI": 28.29504219409283, "J_D_1KI": 11.93883636881554}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.output
index 9c08cbf..1bfc58f 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.4716074466705322}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.4699594974517822}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2514,     4962,  ..., 12495038,
-                            12497495, 12500000]),
-       col_indices=tensor([   0,    2,    3,  ..., 4994, 4998, 4999]),
-       values=tensor([0.6154, 0.9669, 0.2665,  ..., 0.0694, 0.4098, 0.1560]),
+tensor(crow_indices=tensor([       0,     2530,     5011,  ..., 12494917,
+                            12497466, 12500000]),
+       col_indices=tensor([   0,    2,    4,  ..., 4989, 4990, 4993]),
+       values=tensor([0.0612, 0.4688, 0.2670,  ..., 0.2342, 0.9548, 0.3408]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.5763, 0.6030, 0.9940,  ..., 0.2125, 0.0764, 0.1654])
+tensor([0.3044, 0.4364, 0.9300,  ..., 0.3535, 0.8813, 0.4187])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 0.4716074466705322 seconds
+Time: 0.4699594974517822 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2226', '-ss', '5000', '-sd', '0.5', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.872556447982788}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2234', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.893749475479126}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2494,     5060,  ..., 12495015,
-                            12497540, 12500000]),
-       col_indices=tensor([   0,    1,    3,  ..., 4996, 4997, 4998]),
-       values=tensor([0.6641, 0.1400, 0.8724,  ..., 0.1588, 0.2497, 0.0435]),
+tensor(crow_indices=tensor([       0,     2495,     4993,  ..., 12494938,
+                            12497473, 12500000]),
+       col_indices=tensor([   1,    2,    3,  ..., 4995, 4996, 4998]),
+       values=tensor([0.3117, 0.8528, 0.6760,  ..., 0.0359, 0.3985, 0.2346]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.4856, 0.9202, 0.9997,  ..., 0.6539, 0.7245, 0.6538])
+tensor([0.9035, 0.2602, 0.8518,  ..., 0.1682, 0.8170, 0.2713])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 9.872556447982788 seconds
+Time: 9.893749475479126 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2367', '-ss', '5000', '-sd', '0.5', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.31517243385315}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2370', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.843660354614258}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2524,     5066,  ..., 12495116,
-                            12497547, 12500000]),
-       col_indices=tensor([   0,    3,    4,  ..., 4997, 4998, 4999]),
-       values=tensor([0.7920, 0.5049, 0.7981,  ..., 0.7837, 0.6861, 0.6359]),
+tensor(crow_indices=tensor([       0,     2500,     4943,  ..., 12494930,
+                            12497475, 12500000]),
+       col_indices=tensor([   2,    4,    5,  ..., 4996, 4997, 4998]),
+       values=tensor([0.2671, 0.5264, 0.0833,  ..., 0.7249, 0.3539, 0.6800]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.9717, 0.2840, 0.2667,  ..., 0.4259, 0.7903, 0.5279])
+tensor([0.6057, 0.0631, 0.1621,  ..., 0.7635, 0.6231, 0.8684])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 10.31517243385315 seconds
+Time: 10.843660354614258 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2524,     5066,  ..., 12495116,
-                            12497547, 12500000]),
-       col_indices=tensor([   0,    3,    4,  ..., 4997, 4998, 4999]),
-       values=tensor([0.7920, 0.5049, 0.7981,  ..., 0.7837, 0.6861, 0.6359]),
+tensor(crow_indices=tensor([       0,     2500,     4943,  ..., 12494930,
+                            12497475, 12500000]),
+       col_indices=tensor([   2,    4,    5,  ..., 4996, 4997, 4998]),
+       values=tensor([0.2671, 0.5264, 0.0833,  ..., 0.7249, 0.3539, 0.6800]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.9717, 0.2840, 0.2667,  ..., 0.4259, 0.7903, 0.5279])
+tensor([0.6057, 0.0631, 0.1621,  ..., 0.7635, 0.6231, 0.8684])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 10.31517243385315 seconds
+Time: 10.843660354614258 seconds
 
-[18.22, 18.01, 18.1, 18.03, 17.97, 17.83, 18.06, 17.94, 17.8, 18.08]
-[77.42]
-18.54129457473755
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2367, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.31517243385315, 'TIME_S_1KI': 4.35790977349098, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1435.467025976181, 'W': 77.42}
-[18.22, 18.01, 18.1, 18.03, 17.97, 17.83, 18.06, 17.94, 17.8, 18.08, 18.25, 18.11, 18.19, 18.18, 17.9, 17.94, 21.89, 18.55, 18.24, 17.82]
-328.925
-16.44625
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2367, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.31517243385315, 'TIME_S_1KI': 4.35790977349098, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1435.467025976181, 'W': 77.42, 'J_1KI': 606.4499476029494, 'W_1KI': 32.708069286016055, 'W_D': 60.97375, 'J_D': 1130.5322600764036, 'W_D_1KI': 25.7599281791297, 'J_D_1KI': 10.882943886408832}
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diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.json
index f03b074..3647620 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 352628, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.273355960845947, "TIME_S_1KI": 0.029133693186150694, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 997.6947084188461, "W": 73.02, "J_1KI": 2.8293122168938547, "W_1KI": 0.20707374343500798, "W_D": 56.76349999999999, "J_D": 775.5771512097119, "W_D_1KI": 0.16097275315630066, "J_D_1KI": 0.00045649453008921774}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 358898, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.994742631912231, "TIME_S_1KI": 0.030634728061767497, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1136.2339201164245, "W": 81.52, "J_1KI": 3.1658964945929613, "W_1KI": 0.22713974443992443, "W_D": 64.66225, "J_D": 901.2689131630063, "W_D_1KI": 0.18016887806563425, "J_D_1KI": 0.000502005801273995}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.output
index a48462c..1e05b99 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,102 +1,183 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.03506755828857422}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.0216214656829834}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.5462, 0.5771, 0.7542,  ..., 0.5076, 0.8334, 0.7435])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.0216214656829834 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '48562', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.420738697052002}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([3521, 4577, 3700, 4060, 2070, 3153, 3572, 4721, 1322,
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+                           3518, 2706, 1079, 4904, 4765, 1653, 1722]),
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+                      3.8329e-01, 4.1515e-01, 5.6035e-01, 2.5407e-01,
+                      6.0859e-02, 7.3250e-01, 6.1293e-01, 8.3081e-01,
+                      6.0668e-02, 5.6773e-01]), size=(5000, 5000), nnz=250,
        layout=torch.sparse_csr)
-tensor([0.6988, 0.9907, 0.6239,  ..., 0.8127, 0.8805, 0.8314])
+tensor([0.6306, 0.5318, 0.8619,  ..., 0.0870, 0.9085, 0.1453])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -104,80 +185,107 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.03506755828857422 seconds
+Time: 1.420738697052002 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '29942', '-ss', '5000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.8915646076202393}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '358898', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.994742631912231}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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-       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.4051, 0.4452, 0.8286,  ..., 0.6416, 0.7748, 0.9825])
+       col_indices=tensor([4562, 2658,  106, 2707, 1737,   31, 1236, 2601, 4534,
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+                      9.0983e-01, 8.5134e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.6142, 0.8273, 0.9812,  ..., 0.2618, 0.5624, 0.1273])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -185,80 +293,104 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.8915646076202393 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '352628', '-ss', '5000', '-sd', '1e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.273355960845947}
+Time: 10.994742631912231 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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-       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
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+                      4.1456e-01, 1.6044e-01, 7.0167e-01, 7.8015e-01,
+                      5.4577e-02, 8.3172e-01, 6.3844e-01, 2.9679e-01,
+                      3.5497e-01, 9.5436e-01, 3.4236e-01, 7.1246e-01,
+                      9.0983e-01, 8.5134e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.6142, 0.8273, 0.9812,  ..., 0.2618, 0.5624, 0.1273])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -266,91 +398,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.273355960845947 seconds
+Time: 10.994742631912231 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([1604,    4,  806,  370, 4659, 2266,  385, 4480, 1740,
-                           2477, 1011, 4368, 1436, 1511,  582, 2881, 3146,  679,
-                           1335,  340, 2368, 3531, 2793, 4894, 1704,  800, 4449,
-                           2819, 3830,  944,  715,  291, 1651, 2756, 3425, 4366,
-                           1387, 4461, 4123, 3059,   45,   97,  307, 3123, 2010,
-                           4861, 3262,  819, 2940, 4148, 3668, 1416, 2946,  250,
-                           2020, 1865, 1972, 4176, 4993, 3807,  275, 1852, 2676,
-                           3641, 2214, 2133, 3702, 1587,   40, 2796, 4522, 2611,
-                           2391,  408, 3928, 2051, 4768, 4967,  847, 3011,  916,
-                           2658, 2737, 3985,  653, 1850, 4982, 4426, 3558, 4287,
-                           1078, 1321, 2196, 3153, 3474, 1886, 4386, 4813, 4479,
-                           1247, 1097, 4368, 3496, 4390, 2441,   28, 3845, 3018,
-                            574, 3154, 4908, 4477, 1259, 4186, 1078, 3130, 3163,
-                           3314, 3378, 1369, 3074,  965, 3176, 3034, 4337, 2593,
-                           2468, 3576, 1728, 3670, 4022, 4287,  319, 2341, 1420,
-                           3431, 3473, 1919,  368, 2943, 1836, 2897, 4091, 4055,
-                           2042,  694,  518, 3464,  437, 2319, 2327, 4527, 3332,
-                            286, 2756, 1769,  821, 2234, 2362, 3901, 2835, 3532,
-                             56, 1262, 1926, 2816,  573, 4537,  612,  849, 3556,
-                           1060, 4100, 3259, 4604, 1644,  551,  216, 1429, 4706,
-                           4000, 2046,   67, 4772, 4808, 2103, 2457,  770,   19,
-                           3752, 4627, 3183, 2351, 2290,  676, 4693, 3832, 2391,
-                           2085, 4458, 4110, 3726,  941, 4345, 4377, 4491, 3791,
-                           4120, 2339, 4337, 3728, 1293, 1315, 3558, 3212, 1812,
-                           4592, 3120, 1244, 3477, 1623,   73, 4441, 1447, 3927,
-                           4954, 4072, 1985,  625, 3210, 3147, 4908, 2800, 1924,
-                            230, 2107, 2981, 3816, 1923, 3645, 3133, 4236, 4114,
-                           2851, 2177, 1793, 4717,  666, 1768, 4852]),
-       values=tensor([0.8340, 0.7919, 0.0199, 0.7955, 0.8390, 0.2112, 0.2062,
-                      0.2416, 0.0078, 0.1110, 0.8766, 0.2461, 0.5766, 0.2522,
-                      0.8938, 0.7197, 0.9623, 0.5758, 0.3379, 0.5611, 0.1986,
-                      0.0227, 0.4551, 0.8241, 0.0932, 0.4545, 0.5055, 0.7378,
-                      0.9811, 0.7838, 0.9261, 0.3312, 0.1662, 0.7114, 0.8864,
-                      0.9809, 0.1390, 0.6532, 0.2965, 0.0298, 0.8840, 0.9398,
-                      0.6219, 0.4181, 0.3747, 0.5146, 0.8402, 0.0806, 0.9003,
-                      0.4097, 0.4861, 0.8634, 0.8848, 0.4692, 0.4523, 0.5039,
-                      0.7094, 0.3166, 0.2806, 0.4769, 0.9739, 0.8634, 0.3699,
-                      0.8453, 0.0189, 0.8787, 0.8196, 0.8724, 0.2325, 0.0224,
-                      0.5326, 0.1429, 0.6605, 0.4303, 0.9331, 0.8262, 0.4714,
-                      0.3810, 0.9149, 0.4305, 0.2891, 0.7127, 0.3828, 0.4241,
-                      0.9483, 0.5644, 0.3167, 0.4464, 0.4110, 0.1906, 0.8227,
-                      0.3284, 0.6812, 0.4592, 0.8170, 0.4218, 0.2545, 0.2861,
-                      0.1807, 0.3784, 0.2316, 0.6484, 0.4370, 0.4606, 0.6060,
-                      0.1427, 0.6182, 0.8321, 0.4963, 0.9467, 0.6222, 0.0087,
-                      0.8644, 0.1970, 0.6141, 0.5044, 0.8825, 0.7629, 0.0116,
-                      0.7947, 0.1399, 0.5336, 0.5972, 0.1395, 0.9791, 0.9029,
-                      0.5148, 0.1269, 0.3422, 0.7435, 0.2942, 0.7550, 0.2954,
-                      0.5429, 0.3946, 0.1495, 0.9295, 0.4788, 0.3075, 0.4290,
-                      0.1023, 0.3547, 0.2906, 0.5885, 0.8529, 0.0126, 0.2314,
-                      0.8888, 0.5984, 0.0063, 0.0122, 0.5164, 0.6866, 0.4135,
-                      0.9434, 0.8529, 0.4727, 0.6175, 0.7220, 0.1600, 0.7729,
-                      0.7553, 0.8476, 0.2583, 0.1648, 0.3383, 0.1827, 0.5841,
-                      0.8183, 0.2678, 0.2397, 0.1691, 0.8089, 0.7103, 0.0096,
-                      0.5130, 0.0577, 0.3835, 0.4322, 0.8199, 0.2829, 0.8244,
-                      0.4148, 0.6484, 0.7719, 0.3598, 0.6003, 0.6391, 0.8970,
-                      0.2186, 0.5556, 0.9770, 0.6002, 0.0280, 0.6160, 0.1589,
-                      0.7241, 0.2905, 0.4033, 0.4301, 0.8521, 0.8618, 0.5604,
-                      0.1077, 0.2810, 0.1105, 0.5637, 0.8228, 0.0305, 0.7660,
-                      0.3373, 0.7652, 0.7287, 0.6077, 0.2858, 0.4001, 0.8614,
-                      0.8105, 0.5021, 0.3182, 0.2015, 0.3600, 0.7160, 0.9874,
-                      0.0572, 0.8754, 0.4725, 0.5233, 0.0364, 0.1500, 0.2431,
-                      0.3915, 0.8270, 0.2064, 0.5104, 0.9129, 0.4413, 0.8801,
-                      0.9179, 0.9739, 0.2250, 0.5404, 0.9261, 0.5735, 0.2090,
-                      0.7470, 0.4131, 0.1494, 0.0532, 0.6628]),
-       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.3633, 0.6430, 0.8109,  ..., 0.6589, 0.7112, 0.9999])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 250
-Density: 1e-05
-Time: 10.273355960845947 seconds
-
-[18.62, 17.97, 17.77, 17.77, 18.2, 18.07, 17.98, 17.9, 18.36, 17.75]
-[73.02]
-13.663307428359985
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 352628, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.273355960845947, 'TIME_S_1KI': 0.029133693186150694, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.6947084188461, 'W': 73.02}
-[18.62, 17.97, 17.77, 17.77, 18.2, 18.07, 17.98, 17.9, 18.36, 17.75, 18.68, 18.07, 18.34, 18.06, 18.18, 17.85, 18.03, 18.15, 17.94, 17.93]
-325.13
-16.2565
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 352628, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.273355960845947, 'TIME_S_1KI': 0.029133693186150694, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.6947084188461, 'W': 73.02, 'J_1KI': 2.8293122168938547, 'W_1KI': 0.20707374343500798, 'W_D': 56.76349999999999, 'J_D': 775.5771512097119, 'W_D_1KI': 0.16097275315630066, 'J_D_1KI': 0.00045649453008921774}
+[19.38, 18.54, 18.64, 18.62, 18.88, 18.7, 19.3, 18.66, 18.88, 18.55]
+[81.52]
+13.938100099563599
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 358898, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.994742631912231, 'TIME_S_1KI': 0.030634728061767497, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1136.2339201164245, 'W': 81.52}
+[19.38, 18.54, 18.64, 18.62, 18.88, 18.7, 19.3, 18.66, 18.88, 18.55, 18.99, 18.73, 19.19, 18.53, 18.66, 18.43, 18.64, 18.47, 18.57, 18.51]
+337.155
+16.85775
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 358898, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.994742631912231, 'TIME_S_1KI': 0.030634728061767497, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1136.2339201164245, 'W': 81.52, 'J_1KI': 3.1658964945929613, 'W_1KI': 0.22713974443992443, 'W_D': 64.66225, 'J_D': 901.2689131630063, 'W_D_1KI': 0.18016887806563425, 'J_D_1KI': 0.000502005801273995}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.json
index f8ae472..cfada83 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.json
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 348362, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.331048488616943, "TIME_S_1KI": 0.029656071812129176, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1019.3632760500908, "W": 73.39, "J_1KI": 2.9261609361815895, "W_1KI": 0.2106716576434858, "W_D": 57.056, "J_D": 792.4893184127807, "W_D_1KI": 0.1637836503407375, "J_D_1KI": 0.0004701536055618509}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 345614, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.329712390899658, "TIME_S_1KI": 0.029888003353161788, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1114.3590168857575, "W": 80.92, "J_1KI": 3.224287838125069, "W_1KI": 0.23413403392223694, "W_D": 63.62, "J_D": 876.1186437749863, "W_D_1KI": 0.18407819127697372, "J_D_1KI": 0.0005326120795945005}
diff --git a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.output
index 7fb17e1..ac5f600 100644
--- a/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.output
+++ b/pytorch/output_synthetic_16core/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.01948690414428711}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.01959514617919922}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([3543, 2601, 4811,  ..., 3171, 1181, 2171]),
-       values=tensor([0.9467, 0.6961, 0.1720,  ..., 0.4974, 0.2968, 0.3956]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([4264, 4669,  451,  ..., 1795,  620, 4857]),
+       values=tensor([0.1704, 0.3269, 0.7430,  ..., 0.5652, 0.6816, 0.7253]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.9876, 0.5815, 0.6649,  ..., 0.6796, 0.2344, 0.7286])
+tensor([0.6615, 0.0381, 0.0367,  ..., 0.5298, 0.9396, 0.7509])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 0.01948690414428711 seconds
+Time: 0.01959514617919922 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53882', '-ss', '5000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.6240589618682861}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53584', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.627917766571045}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([ 267,  783, 3915,  ..., 3618, 4520, 1464]),
-       values=tensor([0.2837, 0.8920, 0.5250,  ..., 0.9331, 0.1091, 0.1041]),
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([1581, 3868, 1736,  ..., 4846, 4742, 3573]),
+       values=tensor([0.6556, 0.7489, 0.5131,  ..., 0.6895, 0.9760, 0.2267]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.6941, 0.6065, 0.9857,  ..., 0.4180, 0.3910, 0.2569])
+tensor([0.8272, 0.8622, 0.3196,  ..., 0.2839, 0.4073, 0.4097])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 1.6240589618682861 seconds
+Time: 1.627917766571045 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '348362', '-ss', '5000', '-sd', '5e-05', '-c', '16']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.331048488616943}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '345614', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.329712390899658}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([2866, 1356, 2436,  ...,  421, 1796, 3666]),
-       values=tensor([0.0261, 0.5356, 0.3907,  ..., 0.0828, 0.6288, 0.2100]),
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 1248, 1249, 1250]),
+       col_indices=tensor([  42, 3037, 1550,  ..., 1416,  889, 4366]),
+       values=tensor([0.7068, 0.8277, 0.9092,  ..., 0.9350, 0.4485, 0.3337]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.2739, 0.9422, 0.5483,  ..., 0.7719, 0.1377, 0.3851])
+tensor([0.4471, 0.7775, 0.4895,  ..., 0.3897, 0.9890, 0.2278])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.331048488616943 seconds
+Time: 10.329712390899658 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([2866, 1356, 2436,  ...,  421, 1796, 3666]),
-       values=tensor([0.0261, 0.5356, 0.3907,  ..., 0.0828, 0.6288, 0.2100]),
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 1248, 1249, 1250]),
+       col_indices=tensor([  42, 3037, 1550,  ..., 1416,  889, 4366]),
+       values=tensor([0.7068, 0.8277, 0.9092,  ..., 0.9350, 0.4485, 0.3337]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.2739, 0.9422, 0.5483,  ..., 0.7719, 0.1377, 0.3851])
+tensor([0.4471, 0.7775, 0.4895,  ..., 0.3897, 0.9890, 0.2278])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.331048488616943 seconds
+Time: 10.329712390899658 seconds
 
-[18.79, 18.13, 18.25, 18.09, 18.59, 17.94, 18.05, 18.04, 18.23, 18.09]
-[73.39]
-13.889675378799438
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 348362, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.331048488616943, 'TIME_S_1KI': 0.029656071812129176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1019.3632760500908, 'W': 73.39}
-[18.79, 18.13, 18.25, 18.09, 18.59, 17.94, 18.05, 18.04, 18.23, 18.09, 18.49, 18.19, 18.09, 17.88, 18.04, 18.12, 18.21, 18.0, 18.16, 17.97]
-326.68
-16.334
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 348362, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.331048488616943, 'TIME_S_1KI': 0.029656071812129176, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1019.3632760500908, 'W': 73.39, 'J_1KI': 2.9261609361815895, 'W_1KI': 0.2106716576434858, 'W_D': 57.056, 'J_D': 792.4893184127807, 'W_D_1KI': 0.1637836503407375, 'J_D_1KI': 0.0004701536055618509}
+[19.68, 18.79, 19.94, 18.61, 18.88, 18.56, 18.74, 18.48, 18.79, 24.64]
+[80.92]
+13.771119832992554
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 345614, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.329712390899658, 'TIME_S_1KI': 0.029888003353161788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1114.3590168857575, 'W': 80.92}
+[19.68, 18.79, 19.94, 18.61, 18.88, 18.56, 18.74, 18.48, 18.79, 24.64, 18.84, 18.56, 18.75, 18.85, 18.69, 22.73, 19.15, 18.71, 18.95, 18.48]
+346.00000000000006
+17.300000000000004
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 345614, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.329712390899658, 'TIME_S_1KI': 0.029888003353161788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1114.3590168857575, 'W': 80.92, 'J_1KI': 3.224287838125069, 'W_1KI': 0.23413403392223694, 'W_D': 63.62, 'J_D': 876.1186437749863, 'W_D_1KI': 0.18407819127697372, 'J_D_1KI': 0.0005326120795945005}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..e9b3498
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1748, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.143786191940308, "TIME_S_1KI": 5.8030813455036085, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 530.3414198303223, "W": 36.295146632325824, "J_1KI": 303.3989815962942, "W_1KI": 20.763813862886625, "W_D": 17.602146632325823, "J_D": 257.20098424220083, "W_D_1KI": 10.069877936113171, "J_D_1KI": 5.760799734618519}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..2da7ef7
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6005632877349854}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       7,      17,  ...,  999970,
+                             999989, 1000000]),
+       col_indices=tensor([27708, 32922, 35240,  ..., 82805, 88487, 98517]),
+       values=tensor([0.0088, 0.7733, 0.0012,  ..., 0.6420, 0.7382, 0.2177]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1129, 0.5965, 0.7496,  ..., 0.0902, 0.9107, 0.7724])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.6005632877349854 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1748 -ss 100000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.143786191940308}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      10,      23,  ...,  999980,
+                             999992, 1000000]),
+       col_indices=tensor([ 8017, 17251, 18992,  ..., 72823, 91334, 91663]),
+       values=tensor([0.4596, 0.1797, 0.9797,  ..., 0.0499, 0.7967, 0.0183]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8873, 0.5523, 0.3791,  ..., 0.8812, 0.4027, 0.2259])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.143786191940308 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      10,      23,  ...,  999980,
+                             999992, 1000000]),
+       col_indices=tensor([ 8017, 17251, 18992,  ..., 72823, 91334, 91663]),
+       values=tensor([0.4596, 0.1797, 0.9797,  ..., 0.0499, 0.7967, 0.0183]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8873, 0.5523, 0.3791,  ..., 0.8812, 0.4027, 0.2259])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.143786191940308 seconds
+
+[20.48, 20.6, 20.64, 20.68, 21.12, 21.12, 21.24, 21.12, 21.04, 21.04]
+[20.76, 20.76, 21.04, 22.36, 24.68, 32.36, 38.56, 45.04, 50.12, 51.88, 51.52, 52.0, 52.0, 51.84]
+14.611910104751587
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1748, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.143786191940308, 'TIME_S_1KI': 5.8030813455036085, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 530.3414198303223, 'W': 36.295146632325824}
+[20.48, 20.6, 20.64, 20.68, 21.12, 21.12, 21.24, 21.12, 21.04, 21.04, 20.36, 20.64, 20.56, 20.72, 20.68, 20.52, 20.72, 20.76, 20.52, 20.48]
+373.86
+18.693
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1748, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.143786191940308, 'TIME_S_1KI': 5.8030813455036085, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 530.3414198303223, 'W': 36.295146632325824, 'J_1KI': 303.3989815962942, 'W_1KI': 20.763813862886625, 'W_D': 17.602146632325823, 'J_D': 257.20098424220083, 'W_D_1KI': 10.069877936113171, 'J_D_1KI': 5.760799734618519}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..fdbe85f
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 175, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.183927774429321, "TIME_S_1KI": 63.90815871102469, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 628.162894821167, "W": 35.42748603907242, "J_1KI": 3589.5022561209544, "W_1KI": 202.44277736612813, "W_D": 16.71548603907242, "J_D": 296.38140530395515, "W_D_1KI": 95.51706308041383, "J_D_1KI": 545.8117890309362}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..887a602
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.9738054275512695}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      100,      229,  ...,  9999825,
+                             9999913, 10000000]),
+       col_indices=tensor([ 2839,  3131,  5153,  ..., 92533, 94576, 98932]),
+       values=tensor([0.4697, 0.9996, 0.7875,  ..., 0.5192, 0.5202, 0.9540]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.9598, 0.0952, 0.8851,  ..., 0.3844, 0.8104, 0.5939])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 5.9738054275512695 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 175 -ss 100000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.183927774429321}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       95,      193,  ...,  9999801,
+                             9999901, 10000000]),
+       col_indices=tensor([ 2869,  4015,  6080,  ..., 94953, 95635, 98117]),
+       values=tensor([0.0857, 0.9758, 0.7363,  ..., 0.8151, 0.8595, 0.7723]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7586, 0.5970, 0.0221,  ..., 0.6721, 0.2659, 0.4588])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 11.183927774429321 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       95,      193,  ...,  9999801,
+                             9999901, 10000000]),
+       col_indices=tensor([ 2869,  4015,  6080,  ..., 94953, 95635, 98117]),
+       values=tensor([0.0857, 0.9758, 0.7363,  ..., 0.8151, 0.8595, 0.7723]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7586, 0.5970, 0.0221,  ..., 0.6721, 0.2659, 0.4588])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 11.183927774429321 seconds
+
+[20.52, 20.84, 20.72, 20.68, 20.8, 20.68, 20.68, 20.72, 20.64, 20.64]
+[20.64, 20.76, 20.84, 24.4, 27.0, 28.6, 31.96, 32.96, 34.0, 38.76, 44.2, 47.96, 51.6, 50.84, 50.88, 50.6, 50.76]
+17.730947494506836
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 175, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.183927774429321, 'TIME_S_1KI': 63.90815871102469, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 628.162894821167, 'W': 35.42748603907242}
+[20.52, 20.84, 20.72, 20.68, 20.8, 20.68, 20.68, 20.72, 20.64, 20.64, 20.64, 20.64, 20.64, 20.72, 20.76, 20.76, 21.0, 20.92, 21.36, 21.56]
+374.24
+18.712
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 175, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.183927774429321, 'TIME_S_1KI': 63.90815871102469, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 628.162894821167, 'W': 35.42748603907242, 'J_1KI': 3589.5022561209544, 'W_1KI': 202.44277736612813, 'W_D': 16.71548603907242, 'J_D': 296.38140530395515, 'W_D_1KI': 95.51706308041383, 'J_D_1KI': 545.8117890309362}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..eff1e86
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 11597, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.436057329177856, "TIME_S_1KI": 0.8998928454926151, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 483.6025880336762, "W": 33.00177532885384, "J_1KI": 41.700662932971994, "W_1KI": 2.845716592985586, "W_D": 14.225775328853839, "J_D": 208.462172027588, "W_D_1KI": 1.2266771862424626, "J_D_1KI": 0.10577538900081596}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..cdfe200
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.12187767028808594}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      2,  ...,  99998,  99999,
+                            100000]),
+       col_indices=tensor([99237, 81965, 52149,  ..., 94819, 50598, 82628]),
+       values=tensor([0.3300, 0.8237, 0.5005,  ..., 0.6469, 0.1010, 0.4687]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0038, 0.2456, 0.3182,  ..., 0.7163, 0.7510, 0.9775])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.12187767028808594 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 8615 -ss 100000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.799410104751587}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998,  99999,
+                            100000]),
+       col_indices=tensor([88588, 42232, 90125,  ..., 27244, 80106, 39636]),
+       values=tensor([0.8018, 0.8315, 0.5597,  ..., 0.5532, 0.0030, 0.5793]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1929, 0.1411, 0.4568,  ..., 0.6294, 0.2188, 0.4350])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 7.799410104751587 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 11597 -ss 100000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.436057329177856}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998,  99998,
+                            100000]),
+       col_indices=tensor([24172, 83350, 29274,  ..., 76990, 53592, 71081]),
+       values=tensor([0.7302, 0.8346, 0.3553,  ..., 0.4222, 0.8183, 0.0288]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8537, 0.9431, 0.0277,  ..., 0.1357, 0.7019, 0.9196])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.436057329177856 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998,  99998,
+                            100000]),
+       col_indices=tensor([24172, 83350, 29274,  ..., 76990, 53592, 71081]),
+       values=tensor([0.7302, 0.8346, 0.3553,  ..., 0.4222, 0.8183, 0.0288]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8537, 0.9431, 0.0277,  ..., 0.1357, 0.7019, 0.9196])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.436057329177856 seconds
+
+[21.08, 20.64, 20.76, 20.76, 20.92, 21.32, 21.32, 21.36, 21.0, 20.8]
+[20.64, 20.36, 20.72, 23.08, 24.72, 29.44, 35.88, 40.08, 43.56, 45.4, 45.96, 45.6, 45.36, 46.04]
+14.653835535049438
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11597, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.436057329177856, 'TIME_S_1KI': 0.8998928454926151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 483.6025880336762, 'W': 33.00177532885384}
+[21.08, 20.64, 20.76, 20.76, 20.92, 21.32, 21.32, 21.36, 21.0, 20.8, 20.36, 20.32, 20.32, 20.44, 20.64, 20.8, 21.0, 21.16, 21.12, 21.04]
+375.52
+18.776
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 11597, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.436057329177856, 'TIME_S_1KI': 0.8998928454926151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 483.6025880336762, 'W': 33.00177532885384, 'J_1KI': 41.700662932971994, 'W_1KI': 2.845716592985586, 'W_D': 14.225775328853839, 'J_D': 208.462172027588, 'W_D_1KI': 1.2266771862424626, 'J_D_1KI': 0.10577538900081596}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..67cd5e0
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3297, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.891435861587524, "TIME_S_1KI": 3.3034382352403777, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 528.672490940094, "W": 36.05478479295784, "J_1KI": 160.34955745832394, "W_1KI": 10.935633846817664, "W_D": 17.45578479295784, "J_D": 255.95474444794658, "W_D_1KI": 5.2944448871573675, "J_D_1KI": 1.605837090432929}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..3c3d8e5
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.4573814868927002}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     10,     16,  ..., 499990, 499995,
+                            500000]),
+       col_indices=tensor([ 5164,  6869,  8448,  ..., 29154, 68140, 97893]),
+       values=tensor([0.8386, 0.0921, 0.7067,  ..., 0.9232, 0.1449, 0.6848]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.0246, 0.8160, 0.3295,  ..., 0.0588, 0.6998, 0.9868])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 0.4573814868927002 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 2295 -ss 100000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.306779146194458}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      8,  ..., 499989, 499995,
+                            500000]),
+       col_indices=tensor([ 2059, 19971, 54406,  ..., 65065, 65922, 83323]),
+       values=tensor([0.5530, 0.6181, 0.7781,  ..., 0.5380, 0.6243, 0.8378]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.4055, 0.5945, 0.9428,  ..., 0.6446, 0.1456, 0.3700])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 7.306779146194458 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3297 -ss 100000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.891435861587524}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,     11,  ..., 499995, 499995,
+                            500000]),
+       col_indices=tensor([ 8913, 22689, 49331,  ..., 65321, 72756, 72788]),
+       values=tensor([0.7511, 0.0782, 0.7533,  ..., 0.6341, 0.1803, 0.2288]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.5601, 0.4293, 0.2285,  ..., 0.5137, 0.5400, 0.5797])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.891435861587524 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,     11,  ..., 499995, 499995,
+                            500000]),
+       col_indices=tensor([ 8913, 22689, 49331,  ..., 65321, 72756, 72788]),
+       values=tensor([0.7511, 0.0782, 0.7533,  ..., 0.6341, 0.1803, 0.2288]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.5601, 0.4293, 0.2285,  ..., 0.5137, 0.5400, 0.5797])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.891435861587524 seconds
+
+[20.84, 20.48, 20.44, 20.64, 20.64, 20.68, 20.72, 20.64, 20.64, 20.92]
+[20.6, 20.48, 20.72, 25.12, 26.88, 32.4, 38.84, 42.28, 46.96, 50.12, 51.2, 51.8, 51.56, 51.6]
+14.66303277015686
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3297, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.891435861587524, 'TIME_S_1KI': 3.3034382352403777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 528.672490940094, 'W': 36.05478479295784}
+[20.84, 20.48, 20.44, 20.64, 20.64, 20.68, 20.72, 20.64, 20.64, 20.92, 20.84, 21.04, 20.84, 20.84, 20.76, 20.8, 20.68, 20.44, 20.28, 20.24]
+371.98
+18.599
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3297, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.891435861587524, 'TIME_S_1KI': 3.3034382352403777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 528.672490940094, 'W': 36.05478479295784, 'J_1KI': 160.34955745832394, 'W_1KI': 10.935633846817664, 'W_D': 17.45578479295784, 'J_D': 255.95474444794658, 'W_D_1KI': 5.2944448871573675, 'J_D_1KI': 1.605837090432929}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..6fa3845
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 32636, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.47010850906372, "TIME_S_1KI": 0.32081469877018387, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 319.63640854835506, "W": 22.48260624860275, "J_1KI": 9.793982367580435, "W_1KI": 0.6888897612637195, "W_D": 3.984606248602752, "J_D": 56.64935891771315, "W_D_1KI": 0.12209235962136145, "J_D_1KI": 0.003741033203252894}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..21cdb17
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.050879478454589844}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([5382, 2827, 5658,  ..., 9195, 8647, 1137]),
+       values=tensor([0.6423, 0.5656, 0.8194,  ..., 0.3825, 0.7281, 0.0248]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.6609, 0.7541, 0.4159,  ..., 0.2180, 0.3481, 0.0053])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.050879478454589844 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 20637 -ss 10000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.639445781707764}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9998, 10000, 10000]),
+       col_indices=tensor([1538, 6690, 5733,  ..., 9607, 7438, 7782]),
+       values=tensor([0.7222, 0.1089, 0.5631,  ..., 0.3116, 0.0243, 0.6999]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.2878, 0.8940, 0.0961,  ..., 0.0631, 0.2895, 0.2219])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 6.639445781707764 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32636 -ss 10000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.47010850906372}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([7014, 8766, 3433,  ..., 9466, 1431, 7728]),
+       values=tensor([0.0370, 0.3747, 0.2051,  ..., 0.2901, 0.3737, 0.7201]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.8451, 0.4833, 0.4298,  ..., 0.9015, 0.0937, 0.6764])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.47010850906372 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([7014, 8766, 3433,  ..., 9466, 1431, 7728]),
+       values=tensor([0.0370, 0.3747, 0.2051,  ..., 0.2901, 0.3737, 0.7201]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.8451, 0.4833, 0.4298,  ..., 0.9015, 0.0937, 0.6764])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.47010850906372 seconds
+
+[20.64, 20.64, 20.48, 20.48, 20.48, 20.4, 20.32, 20.16, 20.24, 20.4]
+[20.72, 20.64, 21.24, 23.32, 25.32, 26.04, 26.72, 26.72, 26.48, 25.08, 23.72, 23.6, 23.56, 23.48]
+14.217053174972534
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32636, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.47010850906372, 'TIME_S_1KI': 0.32081469877018387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 319.63640854835506, 'W': 22.48260624860275}
+[20.64, 20.64, 20.48, 20.48, 20.48, 20.4, 20.32, 20.16, 20.24, 20.4, 20.64, 20.56, 20.48, 20.52, 20.68, 20.84, 20.88, 20.88, 20.84, 20.48]
+369.96
+18.497999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 32636, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.47010850906372, 'TIME_S_1KI': 0.32081469877018387, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 319.63640854835506, 'W': 22.48260624860275, 'J_1KI': 9.793982367580435, 'W_1KI': 0.6888897612637195, 'W_D': 3.984606248602752, 'J_D': 56.64935891771315, 'W_D_1KI': 0.12209235962136145, 'J_D_1KI': 0.003741033203252894}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..c0d7ab3
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 4519, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.300970077514648, "TIME_S_1KI": 2.279479990598506, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.2094431686402, "W": 23.392743273719635, "J_1KI": 73.51392856132777, "W_1KI": 5.176530930232272, "W_D": 4.8837432737196345, "J_D": 69.35593720483783, "W_D_1KI": 1.0807132714582062, "J_D_1KI": 0.2391487655362262}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..f0ce63e
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2727935314178467}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     18,  ...,  99975,  99993,
+                            100000]),
+       col_indices=tensor([2872, 4034, 5620,  ..., 6357, 6556, 9590]),
+       values=tensor([0.7995, 0.0045, 0.2448,  ..., 0.5761, 0.7842, 0.1546]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8077, 0.7130, 0.7281,  ..., 0.3829, 0.9486, 0.9162])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.2727935314178467 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3849 -ss 10000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.942286252975464}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     12,     21,  ...,  99991,  99998,
+                            100000]),
+       col_indices=tensor([ 425,  574,  695,  ..., 9570, 6024, 9715]),
+       values=tensor([0.7410, 0.8879, 0.5840,  ..., 0.6995, 0.9280, 0.9465]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2929, 0.5164, 0.5482,  ..., 0.5103, 0.5008, 0.9557])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 8.942286252975464 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4519 -ss 10000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.300970077514648}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     16,  ...,  99974,  99990,
+                            100000]),
+       col_indices=tensor([ 582, 1691, 2515,  ..., 7345, 7996, 8295]),
+       values=tensor([0.8177, 0.9283, 0.6030,  ..., 0.2647, 0.3717, 0.8633]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2209, 0.7260, 0.0429,  ..., 0.4887, 0.7834, 0.0043])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.300970077514648 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     16,  ...,  99974,  99990,
+                            100000]),
+       col_indices=tensor([ 582, 1691, 2515,  ..., 7345, 7996, 8295]),
+       values=tensor([0.8177, 0.9283, 0.6030,  ..., 0.2647, 0.3717, 0.8633]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2209, 0.7260, 0.0429,  ..., 0.4887, 0.7834, 0.0043])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.300970077514648 seconds
+
+[20.48, 20.64, 20.44, 20.28, 20.28, 20.6, 20.72, 20.68, 20.68, 20.48]
+[20.4, 20.36, 23.16, 24.08, 27.0, 27.6, 28.72, 28.72, 26.24, 26.28, 24.44, 24.28, 24.24, 24.08]
+14.201388835906982
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.300970077514648, 'TIME_S_1KI': 2.279479990598506, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.2094431686402, 'W': 23.392743273719635}
+[20.48, 20.64, 20.44, 20.28, 20.28, 20.6, 20.72, 20.68, 20.68, 20.48, 20.52, 20.6, 20.68, 20.64, 20.64, 20.52, 20.52, 20.48, 20.68, 20.72]
+370.18
+18.509
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 4519, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.300970077514648, 'TIME_S_1KI': 2.279479990598506, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.2094431686402, 'W': 23.392743273719635, 'J_1KI': 73.51392856132777, 'W_1KI': 5.176530930232272, 'W_D': 4.8837432737196345, 'J_D': 69.35593720483783, 'W_D_1KI': 1.0807132714582062, 'J_D_1KI': 0.2391487655362262}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..6d63757
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 490, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.49430251121521, "TIME_S_1KI": 21.416943900439204, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 322.6961988353729, "W": 22.678695903983957, "J_1KI": 658.5636710925977, "W_1KI": 46.283052865273376, "W_D": 4.3196959039839555, "J_D": 61.46515012335775, "W_D_1KI": 8.815705926497868, "J_D_1KI": 17.991236584689528}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..8697ed9
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.140977382659912}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     103,     212,  ...,  999798,
+                             999901, 1000000]),
+       col_indices=tensor([  63,  140,  146,  ..., 9691, 9771, 9918]),
+       values=tensor([0.8748, 0.2571, 0.8906,  ..., 0.1504, 0.2890, 0.7825]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5882, 0.3416, 0.1892,  ..., 0.3016, 0.5220, 0.0626])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 2.140977382659912 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 490 -ss 10000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.49430251121521}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     113,     202,  ...,  999820,
+                             999916, 1000000]),
+       col_indices=tensor([   2,   35,   39,  ..., 9519, 9605, 9656]),
+       values=tensor([0.0992, 0.7724, 0.9238,  ..., 0.3639, 0.9758, 0.0697]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2199, 0.1288, 0.7757,  ..., 0.1449, 0.2950, 0.2928])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.49430251121521 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     113,     202,  ...,  999820,
+                             999916, 1000000]),
+       col_indices=tensor([   2,   35,   39,  ..., 9519, 9605, 9656]),
+       values=tensor([0.0992, 0.7724, 0.9238,  ..., 0.3639, 0.9758, 0.0697]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2199, 0.1288, 0.7757,  ..., 0.1449, 0.2950, 0.2928])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.49430251121521 seconds
+
+[20.68, 20.72, 20.56, 20.52, 20.48, 20.2, 19.92, 19.96, 19.92, 19.92]
+[19.96, 20.36, 20.52, 22.04, 24.08, 26.92, 27.96, 27.96, 26.64, 25.04, 24.52, 24.4, 24.4, 24.52]
+14.229045629501343
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 490, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.49430251121521, 'TIME_S_1KI': 21.416943900439204, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.6961988353729, 'W': 22.678695903983957}
+[20.68, 20.72, 20.56, 20.52, 20.48, 20.2, 19.92, 19.96, 19.92, 19.92, 20.44, 20.36, 20.4, 20.52, 20.4, 20.64, 20.76, 20.52, 20.52, 20.52]
+367.18
+18.359
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 490, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.49430251121521, 'TIME_S_1KI': 21.416943900439204, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.6961988353729, 'W': 22.678695903983957, 'J_1KI': 658.5636710925977, 'W_1KI': 46.283052865273376, 'W_D': 4.3196959039839555, 'J_D': 61.46515012335775, 'W_D_1KI': 8.815705926497868, 'J_D_1KI': 17.991236584689528}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..d53721b
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.575754880905151, "TIME_S_1KI": 105.75754880905151, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 357.48722974777223, "W": 23.357767084816274, "J_1KI": 3574.8722974777224, "W_1KI": 233.57767084816274, "W_D": 4.891767084816273, "J_D": 74.86778412389756, "W_D_1KI": 48.91767084816273, "J_D_1KI": 489.1767084816273}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..5bc40f5
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.575754880905151}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     505,    1011,  ..., 4998999,
+                            4999505, 5000000]),
+       col_indices=tensor([  17,   34,   93,  ..., 9927, 9945, 9977]),
+       values=tensor([0.3942, 0.9668, 0.2842,  ..., 0.3748, 0.5474, 0.6270]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.3102, 0.7326, 0.1847,  ..., 0.2267, 0.2009, 0.0941])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.575754880905151 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     505,    1011,  ..., 4998999,
+                            4999505, 5000000]),
+       col_indices=tensor([  17,   34,   93,  ..., 9927, 9945, 9977]),
+       values=tensor([0.3942, 0.9668, 0.2842,  ..., 0.3748, 0.5474, 0.6270]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.3102, 0.7326, 0.1847,  ..., 0.2267, 0.2009, 0.0941])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.575754880905151 seconds
+
+[20.44, 20.44, 20.44, 20.52, 20.44, 20.4, 20.32, 20.2, 20.08, 20.32]
+[20.32, 20.16, 21.08, 22.56, 24.44, 27.6, 27.6, 29.28, 28.88, 28.12, 25.16, 24.16, 24.12, 24.4, 24.56]
+15.30485463142395
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.575754880905151, 'TIME_S_1KI': 105.75754880905151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.48722974777223, 'W': 23.357767084816274}
+[20.44, 20.44, 20.44, 20.52, 20.44, 20.4, 20.32, 20.2, 20.08, 20.32, 20.52, 20.56, 20.76, 20.76, 20.84, 20.84, 20.72, 20.56, 20.56, 20.48]
+369.32
+18.466
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.575754880905151, 'TIME_S_1KI': 105.75754880905151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.48722974777223, 'W': 23.357767084816274, 'J_1KI': 3574.8722974777224, 'W_1KI': 233.57767084816274, 'W_D': 4.891767084816273, 'J_D': 74.86778412389756, 'W_D_1KI': 48.91767084816273, 'J_D_1KI': 489.1767084816273}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..d4908f3
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.58586049079895, "TIME_S_1KI": 215.8586049079895, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 695.2596657943726, "W": 24.42843676698333, "J_1KI": 6952.596657943726, "W_1KI": 244.2843676698333, "W_D": 6.1854367669833294, "J_D": 176.04420374608034, "W_D_1KI": 61.854367669833294, "J_D_1KI": 618.543676698333}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..b9e17f1
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.1 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.58586049079895}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      983,     1945,  ...,  9997995,
+                             9998975, 10000000]),
+       col_indices=tensor([   7,   29,   32,  ..., 9986, 9994, 9999]),
+       values=tensor([0.5805, 0.0545, 0.7779,  ..., 0.8799, 0.6314, 0.5149]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1246, 0.2739, 0.0084,  ..., 0.7975, 0.3318, 0.0977])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 21.58586049079895 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      983,     1945,  ...,  9997995,
+                             9998975, 10000000]),
+       col_indices=tensor([   7,   29,   32,  ..., 9986, 9994, 9999]),
+       values=tensor([0.5805, 0.0545, 0.7779,  ..., 0.8799, 0.6314, 0.5149]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1246, 0.2739, 0.0084,  ..., 0.7975, 0.3318, 0.0977])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 21.58586049079895 seconds
+
+[20.44, 20.24, 20.32, 20.24, 20.16, 20.52, 20.56, 20.68, 20.88, 21.04]
+[20.8, 20.6, 23.72, 23.72, 26.04, 27.56, 30.88, 32.68, 29.8, 29.08, 27.52, 26.28, 24.44, 24.48, 24.48, 24.2, 24.2, 24.2, 24.12, 24.12, 24.28, 24.28, 24.4, 24.32, 24.16, 24.0, 24.08, 24.16]
+28.461078882217407
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.58586049079895, 'TIME_S_1KI': 215.8586049079895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 695.2596657943726, 'W': 24.42843676698333}
+[20.44, 20.24, 20.32, 20.24, 20.16, 20.52, 20.56, 20.68, 20.88, 21.04, 19.8, 19.8, 19.8, 19.96, 20.0, 20.08, 20.12, 20.36, 20.32, 20.36]
+364.86
+18.243000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.58586049079895, 'TIME_S_1KI': 215.8586049079895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 695.2596657943726, 'W': 24.42843676698333, 'J_1KI': 6952.596657943726, 'W_1KI': 244.2843676698333, 'W_D': 6.1854367669833294, 'J_D': 176.04420374608034, 'W_D_1KI': 61.854367669833294, 'J_D_1KI': 618.543676698333}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..00303c3
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.006776571273804, "TIME_S_1KI": 420.06776571273804, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1323.3857918548583, "W": 24.65839535462019, "J_1KI": 13233.857918548583, "W_1KI": 246.58395354620188, "W_D": 6.256395354620192, "J_D": 335.77305422592167, "W_D_1KI": 62.56395354620192, "J_D_1KI": 625.6395354620192}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..7c8bead
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.2 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.006776571273804}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1950,     3929,  ..., 19995954,
+                            19997973, 20000000]),
+       col_indices=tensor([   0,   10,   17,  ..., 9977, 9980, 9990]),
+       values=tensor([0.1470, 0.8510, 0.9446,  ..., 0.3735, 0.6466, 0.3885]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.1499, 0.7404, 0.4886,  ..., 0.1182, 0.4158, 0.3615])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 42.006776571273804 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1950,     3929,  ..., 19995954,
+                            19997973, 20000000]),
+       col_indices=tensor([   0,   10,   17,  ..., 9977, 9980, 9990]),
+       values=tensor([0.1470, 0.8510, 0.9446,  ..., 0.3735, 0.6466, 0.3885]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.1499, 0.7404, 0.4886,  ..., 0.1182, 0.4158, 0.3615])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 42.006776571273804 seconds
+
+[20.44, 20.28, 20.36, 20.28, 20.32, 20.16, 20.32, 20.32, 20.52, 20.56]
+[20.6, 20.56, 20.76, 24.64, 26.0, 26.92, 30.76, 29.04, 28.68, 29.52, 30.24, 30.24, 27.76, 27.48, 26.4, 24.64, 24.48, 24.32, 24.28, 24.28, 24.28, 24.08, 24.28, 24.28, 24.36, 24.2, 24.24, 24.6, 24.64, 24.52, 24.72, 24.48, 24.64, 24.4, 24.52, 24.52, 24.36, 24.4, 24.4, 24.4, 24.48, 24.68, 24.76, 24.56, 24.36, 24.16, 24.24, 24.4, 24.4, 24.76, 24.88, 24.96, 24.92]
+53.668771743774414
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.006776571273804, 'TIME_S_1KI': 420.06776571273804, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1323.3857918548583, 'W': 24.65839535462019}
+[20.44, 20.28, 20.36, 20.28, 20.32, 20.16, 20.32, 20.32, 20.52, 20.56, 20.12, 19.92, 20.0, 20.28, 20.44, 20.72, 20.96, 21.04, 21.04, 21.04]
+368.03999999999996
+18.401999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.006776571273804, 'TIME_S_1KI': 420.06776571273804, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1323.3857918548583, 'W': 24.65839535462019, 'J_1KI': 13233.857918548583, 'W_1KI': 246.58395354620188, 'W_D': 6.256395354620192, 'J_D': 335.77305422592167, 'W_D_1KI': 62.56395354620192, 'J_D_1KI': 625.6395354620192}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..efd7bc8
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 67.20304369926453, "TIME_S_1KI": 672.0304369926453, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1846.4787199783327, "W": 24.29422238106021, "J_1KI": 18464.787199783328, "W_1KI": 242.9422238106021, "W_D": 5.818222381060206, "J_D": 442.2131174325942, "W_D_1KI": 58.18222381060206, "J_D_1KI": 581.8222381060207}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..f1c9a57
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.3 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 67.20304369926453}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2926,     5920,  ..., 29993999,
+                            29997022, 30000000]),
+       col_indices=tensor([   1,    4,    6,  ..., 9978, 9982, 9992]),
+       values=tensor([0.3929, 0.6592, 0.7367,  ..., 0.3321, 0.3012, 0.1502]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.6782, 0.5388, 0.0901,  ..., 0.7339, 0.4235, 0.1483])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 67.20304369926453 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2926,     5920,  ..., 29993999,
+                            29997022, 30000000]),
+       col_indices=tensor([   1,    4,    6,  ..., 9978, 9982, 9992]),
+       values=tensor([0.3929, 0.6592, 0.7367,  ..., 0.3321, 0.3012, 0.1502]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.6782, 0.5388, 0.0901,  ..., 0.7339, 0.4235, 0.1483])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 67.20304369926453 seconds
+
+[20.6, 20.8, 20.76, 20.64, 20.32, 20.48, 20.48, 20.52, 20.8, 20.6]
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+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 67.20304369926453, 'TIME_S_1KI': 672.0304369926453, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1846.4787199783327, 'W': 24.29422238106021}
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+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 67.20304369926453, 'TIME_S_1KI': 672.0304369926453, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1846.4787199783327, 'W': 24.29422238106021, 'J_1KI': 18464.787199783328, 'W_1KI': 242.9422238106021, 'W_D': 5.818222381060206, 'J_D': 442.2131174325942, 'W_D_1KI': 58.18222381060206, 'J_D_1KI': 581.8222381060207}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..e0e6919
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 145476, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.560847282409668, "TIME_S_1KI": 0.07259511728676668, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 314.8679765701293, "W": 22.125839021878733, "J_1KI": 2.1643980902013342, "W_1KI": 0.15209270960075016, "W_D": 3.6298390218787304, "J_D": 51.65544533538807, "W_D_1KI": 0.024951462934633413, "J_D_1KI": 0.00017151600906426773}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..29cdb63
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1307 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.015525579452514648}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([3898, 1482, 5519, 8868, 7883, 9892, 3663, 8094, 3265,
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+                      1.6081e-02, 9.8894e-01, 5.8987e-01, 3.5565e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.3198, 0.6605, 0.6944,  ..., 0.6134, 0.5235, 0.6507])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.015525579452514648 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 67630 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.8812994956970215}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ...,  999,  999, 1000]),
+       col_indices=tensor([8647, 4654, 5499, 4449, 7451, 3553, 6737, 3727, 9375,
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+                      9.7133e-01, 8.6138e-01, 7.7439e-01, 9.1759e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0493, 0.7033, 0.8450,  ..., 0.3708, 0.9702, 0.0106])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 4.8812994956970215 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 145476 -ss 10000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.560847282409668}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.9874, 0.0901, 0.6221, 0.7762, 0.9765, 0.8525]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4976, 0.0481, 0.1913,  ..., 0.0301, 0.3766, 0.0826])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.560847282409668 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([5150, 3949, 6725, 9833, 6214, 5081, 1554, 3283, 4592,
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4976, 0.0481, 0.1913,  ..., 0.0301, 0.3766, 0.0826])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.560847282409668 seconds
+
+[20.4, 20.16, 20.52, 20.6, 20.92, 20.92, 20.96, 20.76, 20.68, 20.44]
+[20.4, 20.52, 20.92, 23.12, 24.52, 26.12, 26.6, 26.16, 24.72, 24.72, 23.36, 23.32, 23.4, 23.52]
+14.230781316757202
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 145476, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.560847282409668, 'TIME_S_1KI': 0.07259511728676668, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.8679765701293, 'W': 22.125839021878733}
+[20.4, 20.16, 20.52, 20.6, 20.92, 20.92, 20.96, 20.76, 20.68, 20.44, 20.2, 20.44, 20.48, 20.56, 20.52, 20.48, 20.4, 20.4, 20.4, 20.4]
+369.9200000000001
+18.496000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 145476, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.560847282409668, 'TIME_S_1KI': 0.07259511728676668, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.8679765701293, 'W': 22.125839021878733, 'J_1KI': 2.1643980902013342, 'W_1KI': 0.15209270960075016, 'W_D': 3.6298390218787304, 'J_D': 51.65544533538807, 'W_D_1KI': 0.024951462934633413, 'J_D_1KI': 0.00017151600906426773}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..2bcb239
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 52342, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.44128155708313, "TIME_S_1KI": 0.19948189899283808, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 334.7413140869141, "W": 23.567352524316387, "J_1KI": 6.395271752835469, "W_1KI": 0.4502570120422679, "W_D": 3.4093525243163825, "J_D": 48.42508902931206, "W_D_1KI": 0.06513607665577133, "J_D_1KI": 0.0012444323231013588}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..0a68cee
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.027875900268554688}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([4151, 7566,   61,  ..., 1923, 6890, 8738]),
+       values=tensor([0.6199, 0.1524, 0.8589,  ..., 0.4429, 0.5764, 0.1533]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.5335, 0.6247, 0.4039,  ..., 0.6064, 0.4993, 0.4017])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.027875900268554688 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 37666 -ss 10000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.555848598480225}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([4832, 7617, 3198,  ..., 2337, 8239, 2535]),
+       values=tensor([0.0012, 0.2497, 0.5477,  ..., 0.0331, 0.3343, 0.4565]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.1414, 0.6293, 0.2915,  ..., 0.6179, 0.0556, 0.9688])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 7.555848598480225 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 52342 -ss 10000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.44128155708313}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([6929, 6481, 6208,  ..., 5185, 5914, 4436]),
+       values=tensor([0.2292, 0.3731, 0.2148,  ..., 0.4978, 0.6385, 0.1071]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.7171, 0.2412, 0.9457,  ..., 0.4356, 0.0163, 0.8101])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.44128155708313 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([6929, 6481, 6208,  ..., 5185, 5914, 4436]),
+       values=tensor([0.2292, 0.3731, 0.2148,  ..., 0.4978, 0.6385, 0.1071]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.7171, 0.2412, 0.9457,  ..., 0.4356, 0.0163, 0.8101])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.44128155708313 seconds
+
+[25.48, 25.4, 25.4, 24.8, 24.68, 24.36, 24.4, 24.16, 24.24, 23.68]
+[23.36, 22.52, 25.48, 26.0, 27.84, 27.84, 27.8, 28.32, 24.88, 24.76, 23.72, 23.52, 23.72, 23.68]
+14.20360279083252
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52342, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.44128155708313, 'TIME_S_1KI': 0.19948189899283808, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.7413140869141, 'W': 23.567352524316387}
+[25.48, 25.4, 25.4, 24.8, 24.68, 24.36, 24.4, 24.16, 24.24, 23.68, 20.0, 19.92, 19.76, 20.2, 20.12, 20.12, 20.36, 20.24, 20.24, 20.36]
+403.1600000000001
+20.158000000000005
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 52342, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.44128155708313, 'TIME_S_1KI': 0.19948189899283808, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.7413140869141, 'W': 23.567352524316387, 'J_1KI': 6.395271752835469, 'W_1KI': 0.4502570120422679, 'W_D': 3.4093525243163825, 'J_D': 48.42508902931206, 'W_D_1KI': 0.06513607665577133, 'J_D_1KI': 0.0012444323231013588}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..ece3918
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 137, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.396055936813354, "TIME_S_1KI": 75.88361997673981, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 753.5857475948335, "W": 33.1694657622459, "J_1KI": 5500.625894852799, "W_1KI": 242.11288877551752, "W_D": 14.2684657622459, "J_D": 324.16899673771866, "W_D_1KI": 104.14938512588249, "J_D_1KI": 760.2144899699452}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..5419742
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.629654169082642}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       44,       92,  ..., 24999905,
+                            24999955, 25000000]),
+       col_indices=tensor([  2191,   6192,  41052,  ..., 471066, 488040,
+                           493296]),
+       values=tensor([0.3986, 0.5227, 0.3241,  ..., 0.9261, 0.7192, 0.3287]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.0957, 0.3468, 0.1431,  ..., 0.5849, 0.2942, 0.3782])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 7.629654169082642 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 137 -ss 500000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.396055936813354}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       53,       94,  ..., 24999919,
+                            24999951, 25000000]),
+       col_indices=tensor([  2485,  12624,  22152,  ..., 462150, 467889,
+                           476331]),
+       values=tensor([0.9572, 0.0985, 0.5455,  ..., 0.5648, 0.8530, 0.8208]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3242, 0.9080, 0.9457,  ..., 0.2147, 0.3332, 0.4113])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.396055936813354 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       53,       94,  ..., 24999919,
+                            24999951, 25000000]),
+       col_indices=tensor([  2485,  12624,  22152,  ..., 462150, 467889,
+                           476331]),
+       values=tensor([0.9572, 0.0985, 0.5455,  ..., 0.5648, 0.8530, 0.8208]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3242, 0.9080, 0.9457,  ..., 0.2147, 0.3332, 0.4113])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.396055936813354 seconds
+
+[20.64, 20.72, 20.84, 20.96, 21.08, 21.36, 21.56, 21.56, 21.36, 21.36]
+[21.36, 21.04, 20.8, 24.16, 25.16, 26.72, 28.8, 27.16, 30.52, 29.84, 29.76, 29.92, 29.92, 30.28, 32.88, 38.36, 44.52, 49.04, 53.12, 53.28, 53.2, 52.84]
+22.719260931015015
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 137, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.396055936813354, 'TIME_S_1KI': 75.88361997673981, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 753.5857475948335, 'W': 33.1694657622459}
+[20.64, 20.72, 20.84, 20.96, 21.08, 21.36, 21.56, 21.56, 21.36, 21.36, 20.8, 20.84, 20.88, 21.0, 21.04, 20.84, 20.96, 20.68, 20.68, 20.52]
+378.02
+18.901
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 137, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.396055936813354, 'TIME_S_1KI': 75.88361997673981, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 753.5857475948335, 'W': 33.1694657622459, 'J_1KI': 5500.625894852799, 'W_1KI': 242.11288877551752, 'W_D': 14.2684657622459, 'J_D': 324.16899673771866, 'W_D_1KI': 104.14938512588249, 'J_D_1KI': 760.2144899699452}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..28ed47d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1548, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.061279058456421, "TIME_S_1KI": 7.145529107529987, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 576.3272948646545, "W": 36.84956710355697, "J_1KI": 372.3044540469344, "W_1KI": 23.80462991185851, "W_D": 18.03556710355697, "J_D": 282.0763014917373, "W_D_1KI": 11.65088314183267, "J_D_1KI": 7.526410298341518}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..ddb3af3
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.6782102584838867}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      13,  ..., 2499990,
+                            2499997, 2500000]),
+       col_indices=tensor([ 40175, 122073, 147940,  ..., 245767, 297950,
+                           495791]),
+       values=tensor([0.1248, 0.8645, 0.7112,  ..., 0.2227, 0.8085, 0.2637]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3055, 0.1588, 0.3916,  ..., 0.3608, 0.8122, 0.4114])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 0.6782102584838867 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1548 -ss 500000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.061279058456421}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       0,       4,  ..., 2499989,
+                            2499994, 2500000]),
+       col_indices=tensor([ 33871,  87157, 252512,  ..., 380315, 410804,
+                           497208]),
+       values=tensor([0.0607, 0.8545, 0.0688,  ..., 0.9965, 0.6178, 0.6113]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3094, 0.9384, 0.5289,  ..., 0.5205, 0.7717, 0.7334])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 11.061279058456421 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       0,       4,  ..., 2499989,
+                            2499994, 2500000]),
+       col_indices=tensor([ 33871,  87157, 252512,  ..., 380315, 410804,
+                           497208]),
+       values=tensor([0.0607, 0.8545, 0.0688,  ..., 0.9965, 0.6178, 0.6113]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3094, 0.9384, 0.5289,  ..., 0.5205, 0.7717, 0.7334])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 11.061279058456421 seconds
+
+[20.72, 20.76, 21.12, 20.92, 20.92, 20.92, 20.88, 20.44, 20.48, 20.48]
+[20.56, 20.56, 21.24, 22.4, 24.48, 28.6, 36.72, 41.88, 48.52, 48.52, 52.36, 52.92, 53.48, 53.4, 53.48]
+15.640001773834229
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1548, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.061279058456421, 'TIME_S_1KI': 7.145529107529987, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 576.3272948646545, 'W': 36.84956710355697}
+[20.72, 20.76, 21.12, 20.92, 20.92, 20.92, 20.88, 20.44, 20.48, 20.48, 20.6, 20.68, 20.88, 21.24, 21.48, 21.32, 21.48, 20.96, 20.64, 20.52]
+376.28
+18.814
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1548, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.061279058456421, 'TIME_S_1KI': 7.145529107529987, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 576.3272948646545, 'W': 36.84956710355697, 'J_1KI': 372.3044540469344, 'W_1KI': 23.80462991185851, 'W_D': 18.03556710355697, 'J_D': 282.0763014917373, 'W_D_1KI': 11.65088314183267, 'J_D_1KI': 7.526410298341518}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..9620135
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 285, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.060697317123413, "TIME_S_1KI": 38.809464270608466, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 641.8192787170411, "W": 34.323302144261945, "J_1KI": 2251.9974691826005, "W_1KI": 120.43263910267349, "W_D": 15.818302144261942, "J_D": 295.7900504469872, "W_D_1KI": 55.50281454126997, "J_D_1KI": 194.74671768866656}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..0f40f5a
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.6834404468536377}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       36,       64,  ..., 12499962,
+                            12499977, 12500000]),
+       col_indices=tensor([  6540,  37225,  45963,  ..., 476281, 491551,
+                           491729]),
+       values=tensor([0.4995, 0.3434, 0.2289,  ..., 0.9980, 0.3953, 0.2839]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.6494, 0.0196, 0.1697,  ..., 0.1655, 0.3294, 0.8926])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 3.6834404468536377 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 285 -ss 500000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.060697317123413}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       30,       51,  ..., 12499947,
+                            12499979, 12500000]),
+       col_indices=tensor([ 32854,  40713,  51141,  ..., 464012, 471829,
+                           496055]),
+       values=tensor([0.7704, 0.8573, 0.2864,  ..., 0.9432, 0.9508, 0.7094]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9627, 0.2273, 0.6691,  ..., 0.1238, 0.9472, 0.0057])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 11.060697317123413 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       30,       51,  ..., 12499947,
+                            12499979, 12500000]),
+       col_indices=tensor([ 32854,  40713,  51141,  ..., 464012, 471829,
+                           496055]),
+       values=tensor([0.7704, 0.8573, 0.2864,  ..., 0.9432, 0.9508, 0.7094]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9627, 0.2273, 0.6691,  ..., 0.1238, 0.9472, 0.0057])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 11.060697317123413 seconds
+
+[20.52, 20.84, 20.92, 20.88, 20.88, 20.72, 20.52, 20.32, 20.36, 20.2]
+[20.44, 20.6, 21.48, 21.48, 23.84, 25.24, 27.32, 30.28, 29.76, 32.76, 37.84, 41.44, 46.92, 51.96, 52.12, 52.64, 52.6, 52.8]
+18.699228763580322
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 285, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.060697317123413, 'TIME_S_1KI': 38.809464270608466, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 641.8192787170411, 'W': 34.323302144261945}
+[20.52, 20.84, 20.92, 20.88, 20.88, 20.72, 20.52, 20.32, 20.36, 20.2, 20.52, 20.44, 20.6, 20.44, 20.44, 20.48, 20.48, 20.36, 20.44, 20.72]
+370.1
+18.505000000000003
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 285, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.060697317123413, 'TIME_S_1KI': 38.809464270608466, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 641.8192787170411, 'W': 34.323302144261945, 'J_1KI': 2251.9974691826005, 'W_1KI': 120.43263910267349, 'W_D': 15.818302144261942, 'J_D': 295.7900504469872, 'W_D_1KI': 55.50281454126997, 'J_D_1KI': 194.74671768866656}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..29a7f3d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 3424, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.731184720993042, "TIME_S_1KI": 3.1341076872059115, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 463.79336466789243, "W": 31.765422510706816, "J_1KI": 135.4536695875854, "W_1KI": 9.27728461177185, "W_D": 13.349422510706813, "J_D": 194.90921553230277, "W_D_1KI": 3.8987799388746534, "J_D_1KI": 1.1386623653255412}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..192c47b
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.4611988067626953}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,      9,  ..., 249990, 249996,
+                            250000]),
+       col_indices=tensor([ 1266,  4071, 18947,  ..., 33754, 36171, 46993]),
+       values=tensor([0.2894, 0.3028, 0.5808,  ..., 0.9499, 0.5530, 0.4490]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9097, 0.0887, 0.0049,  ..., 0.6179, 0.8641, 0.1772])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.4611988067626953 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 2276 -ss 50000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.978086233139038}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,      9,  ..., 249990, 249995,
+                            250000]),
+       col_indices=tensor([ 2233,  6887, 19755,  ..., 38632, 41476, 48223]),
+       values=tensor([0.3109, 0.9167, 0.4160,  ..., 0.6671, 0.8506, 0.5777]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7812, 0.4224, 0.5960,  ..., 0.2514, 0.6292, 0.3012])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 6.978086233139038 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3424 -ss 50000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.731184720993042}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,      7,  ..., 249990, 249996,
+                            250000]),
+       col_indices=tensor([12104, 14436, 24112,  ..., 12878, 32819, 38734]),
+       values=tensor([0.5759, 0.9600, 0.3696,  ..., 0.0040, 0.7766, 0.9665]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4011, 0.3434, 0.3941,  ..., 0.7256, 0.6030, 0.5117])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.731184720993042 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,      7,  ..., 249990, 249996,
+                            250000]),
+       col_indices=tensor([12104, 14436, 24112,  ..., 12878, 32819, 38734]),
+       values=tensor([0.5759, 0.9600, 0.3696,  ..., 0.0040, 0.7766, 0.9665]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4011, 0.3434, 0.3941,  ..., 0.7256, 0.6030, 0.5117])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.731184720993042 seconds
+
+[20.72, 20.68, 20.48, 20.6, 20.64, 20.32, 20.32, 20.36, 20.32, 20.4]
+[20.52, 20.76, 22.24, 22.24, 24.16, 27.64, 32.84, 38.08, 39.84, 44.4, 44.52, 43.96, 44.12, 44.28]
+14.60057282447815
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3424, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.731184720993042, 'TIME_S_1KI': 3.1341076872059115, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 463.79336466789243, 'W': 31.765422510706816}
+[20.72, 20.68, 20.48, 20.6, 20.64, 20.32, 20.32, 20.36, 20.32, 20.4, 20.68, 20.44, 20.48, 20.48, 20.2, 20.32, 20.44, 20.44, 20.6, 20.6]
+368.32000000000005
+18.416000000000004
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 3424, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.731184720993042, 'TIME_S_1KI': 3.1341076872059115, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 463.79336466789243, 'W': 31.765422510706816, 'J_1KI': 135.4536695875854, 'W_1KI': 9.27728461177185, 'W_D': 13.349422510706813, 'J_D': 194.90921553230277, 'W_D_1KI': 3.8987799388746534, 'J_D_1KI': 1.1386623653255412}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..e699ae2
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 385, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.064192295074463, "TIME_S_1KI": 28.738161805388216, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 468.0283072185517, "W": 32.037671646494225, "J_1KI": 1215.657940827407, "W_1KI": 83.21473154933565, "W_D": 13.341671646494227, "J_D": 194.90430094528205, "W_D_1KI": 34.65369258829669, "J_D_1KI": 90.00959113843297}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..c964dd6
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.9453940391540527}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      37,      86,  ..., 2499902,
+                            2499952, 2500000]),
+       col_indices=tensor([  541,  1139,  1813,  ..., 42919, 43072, 44933]),
+       values=tensor([0.0452, 0.1724, 0.8861,  ..., 0.4157, 0.9772, 0.2120]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1045, 0.1557, 0.1178,  ..., 0.5894, 0.9079, 0.5773])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 2.9453940391540527 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 356 -ss 50000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.708060026168823}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      46,      99,  ..., 2499902,
+                            2499948, 2500000]),
+       col_indices=tensor([ 4031,  7226,  7309,  ..., 44877, 48582, 49711]),
+       values=tensor([0.9329, 0.4420, 0.5313,  ..., 0.9423, 0.2849, 0.2389]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0088, 0.8123, 0.3302,  ..., 0.9483, 0.6171, 0.9552])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 9.708060026168823 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 385 -ss 50000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.064192295074463}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      67,     125,  ..., 2499902,
+                            2499956, 2500000]),
+       col_indices=tensor([ 1129,  2884,  2891,  ..., 49010, 49022, 49816]),
+       values=tensor([0.8127, 0.7656, 0.2912,  ..., 0.8978, 0.1718, 0.1428]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7569, 0.5985, 0.1427,  ..., 0.6714, 0.1732, 0.3064])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 11.064192295074463 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      67,     125,  ..., 2499902,
+                            2499956, 2500000]),
+       col_indices=tensor([ 1129,  2884,  2891,  ..., 49010, 49022, 49816]),
+       values=tensor([0.8127, 0.7656, 0.2912,  ..., 0.8978, 0.1718, 0.1428]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7569, 0.5985, 0.1427,  ..., 0.6714, 0.1732, 0.3064])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 11.064192295074463 seconds
+
+[20.72, 20.52, 20.56, 20.48, 20.48, 20.52, 20.56, 20.6, 20.36, 20.6]
+[20.48, 20.52, 21.48, 23.4, 25.44, 29.64, 35.04, 38.48, 42.0, 43.12, 43.12, 43.84, 43.68, 43.32]
+14.608686685562134
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 385, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.064192295074463, 'TIME_S_1KI': 28.738161805388216, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 468.0283072185517, 'W': 32.037671646494225}
+[20.72, 20.52, 20.56, 20.48, 20.48, 20.52, 20.56, 20.6, 20.36, 20.6, 20.84, 20.8, 21.0, 21.24, 21.16, 21.08, 21.28, 20.92, 20.84, 20.88]
+373.91999999999996
+18.695999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 385, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.064192295074463, 'TIME_S_1KI': 28.738161805388216, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 468.0283072185517, 'W': 32.037671646494225, 'J_1KI': 1215.657940827407, 'W_1KI': 83.21473154933565, 'W_D': 13.341671646494227, 'J_D': 194.90430094528205, 'W_D_1KI': 34.65369258829669, 'J_D_1KI': 90.00959113843297}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..3c1f90f
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 32.80737018585205, "TIME_S_1KI": 328.0737018585205, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1398.267660713196, "W": 32.405416577679354, "J_1KI": 13982.676607131958, "W_1KI": 324.0541657767935, "W_D": 13.697416577679352, "J_D": 591.0325080986024, "W_D_1KI": 136.97416577679354, "J_D_1KI": 1369.7416577679353}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..0d92e6b
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 32.80737018585205}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      489,      963,  ..., 24999055,
+                            24999529, 25000000]),
+       col_indices=tensor([   18,   157,   241,  ..., 49747, 49771, 49960]),
+       values=tensor([0.7706, 0.7949, 0.9210,  ..., 0.0962, 0.6322, 0.0053]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.2729, 0.2896, 0.6966,  ..., 0.6831, 0.4086, 0.6520])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 32.80737018585205 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      489,      963,  ..., 24999055,
+                            24999529, 25000000]),
+       col_indices=tensor([   18,   157,   241,  ..., 49747, 49771, 49960]),
+       values=tensor([0.7706, 0.7949, 0.9210,  ..., 0.0962, 0.6322, 0.0053]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.2729, 0.2896, 0.6966,  ..., 0.6831, 0.4086, 0.6520])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 32.80737018585205 seconds
+
+[20.96, 20.68, 20.68, 20.68, 20.72, 20.64, 20.64, 20.76, 20.84, 20.92]
+[20.92, 20.84, 21.32, 22.6, 25.12, 26.44, 26.44, 28.96, 28.92, 32.68, 31.76, 31.16, 31.4, 31.52, 32.12, 34.6, 36.28, 37.8, 37.28, 37.56, 37.96, 37.96, 38.28, 38.36, 37.96, 37.32, 36.48, 36.68, 36.84, 37.44, 37.44, 37.76, 38.0, 39.0, 37.68, 36.84, 37.4, 37.4, 36.8, 37.32, 37.6, 37.72]
+43.14919567108154
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 32.80737018585205, 'TIME_S_1KI': 328.0737018585205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1398.267660713196, 'W': 32.405416577679354}
+[20.96, 20.68, 20.68, 20.68, 20.72, 20.64, 20.64, 20.76, 20.84, 20.92, 21.04, 20.84, 20.72, 20.68, 20.72, 20.6, 20.92, 21.2, 20.96, 20.84]
+374.16
+18.708000000000002
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 32.80737018585205, 'TIME_S_1KI': 328.0737018585205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1398.267660713196, 'W': 32.405416577679354, 'J_1KI': 13982.676607131958, 'W_1KI': 324.0541657767935, 'W_D': 13.697416577679352, 'J_D': 591.0325080986024, 'W_D_1KI': 136.97416577679354, 'J_D_1KI': 1369.7416577679353}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..a4de095
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 19951, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.462696075439453, "TIME_S_1KI": 0.5244196318700542, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 488.71538955688476, "W": 33.3802333465878, "J_1KI": 24.49578414900931, "W_1KI": 1.6731107887618566, "W_D": 15.011233346587801, "J_D": 219.77739569807053, "W_D_1KI": 0.752405059725718, "J_D_1KI": 0.03771264897627778}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..06a37e6
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.05949115753173828}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([35821, 49411,  3789,  ..., 32092, 27347, 39445]),
+       values=tensor([0.1439, 0.1701, 0.0383,  ..., 0.6521, 0.3755, 0.5678]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8709, 0.6173, 0.3475,  ..., 0.7020, 0.1451, 0.7453])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.05949115753173828 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17649 -ss 50000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.288093090057373}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([10903, 22613,  1325,  ...,  4616, 25772, 38217]),
+       values=tensor([0.1548, 0.5404, 0.0562,  ..., 0.6796, 0.5534, 0.6437]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8066, 0.3465, 0.3699,  ..., 0.5654, 0.2544, 0.1290])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 9.288093090057373 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 19951 -ss 50000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.462696075439453}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24997, 24998, 25000]),
+       col_indices=tensor([36526, 27522,  9271,  ..., 28337, 20494, 41611]),
+       values=tensor([0.2838, 0.5711, 0.3512,  ..., 0.1758, 0.7475, 0.3339]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9803, 0.0496, 0.4924,  ..., 0.5397, 0.0486, 0.3592])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.462696075439453 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24997, 24998, 25000]),
+       col_indices=tensor([36526, 27522,  9271,  ..., 28337, 20494, 41611]),
+       values=tensor([0.2838, 0.5711, 0.3512,  ..., 0.1758, 0.7475, 0.3339]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9803, 0.0496, 0.4924,  ..., 0.5397, 0.0486, 0.3592])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.462696075439453 seconds
+
+[20.44, 20.56, 20.6, 20.64, 20.64, 20.44, 20.44, 20.44, 20.4, 20.56]
+[20.44, 20.64, 23.28, 24.2, 26.6, 32.52, 37.24, 40.44, 44.28, 44.96, 44.96, 44.92, 44.44, 43.84]
+14.640861988067627
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 19951, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.462696075439453, 'TIME_S_1KI': 0.5244196318700542, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 488.71538955688476, 'W': 33.3802333465878}
+[20.44, 20.56, 20.6, 20.64, 20.64, 20.44, 20.44, 20.44, 20.4, 20.56, 20.32, 20.32, 20.12, 20.24, 20.32, 20.56, 20.52, 20.28, 20.2, 20.0]
+367.38
+18.369
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 19951, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.462696075439453, 'TIME_S_1KI': 0.5244196318700542, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 488.71538955688476, 'W': 33.3802333465878, 'J_1KI': 24.49578414900931, 'W_1KI': 1.6731107887618566, 'W_D': 15.011233346587801, 'J_D': 219.77739569807053, 'W_D_1KI': 0.752405059725718, 'J_D_1KI': 0.03771264897627778}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..cff7542
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 6322, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.657772779464722, "TIME_S_1KI": 1.68582296416715, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 425.8721401214599, "W": 31.344652680689325, "J_1KI": 67.3635147297469, "W_1KI": 4.958027946961298, "W_D": 12.573652680689325, "J_D": 170.83514789009087, "W_D_1KI": 1.9888726163697126, "J_D_1KI": 0.31459547870447846}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..57f8461
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.3423471450805664}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 124998, 124999,
+                            125000]),
+       col_indices=tensor([  303, 26221, 28347,  ...,  8622, 14261,  4291]),
+       values=tensor([0.9240, 0.5223, 0.0365,  ..., 0.6044, 0.0072, 0.5479]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.1523, 0.9417, 0.1754,  ..., 0.6908, 0.2427, 0.5501])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.3423471450805664 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3067 -ss 50000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 5.0935986042022705}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      3,  ..., 124997, 125000,
+                            125000]),
+       col_indices=tensor([ 1194,  5034,  6320,  ..., 11179, 21504, 33093]),
+       values=tensor([0.7209, 0.3055, 0.4482,  ..., 0.3076, 0.8643, 0.0918]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.9680, 0.6265, 0.9723,  ..., 0.1304, 0.1284, 0.7215])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 5.0935986042022705 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 6322 -ss 50000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.657772779464722}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      2,  ..., 124992, 124996,
+                            125000]),
+       col_indices=tensor([41720,  5446, 23409,  ..., 23991, 37197, 42632]),
+       values=tensor([0.7857, 0.2010, 0.0929,  ..., 0.8446, 0.3352, 0.3559]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.5851, 0.1828, 0.1733,  ..., 0.7326, 0.4663, 0.8685])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.657772779464722 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      2,  ..., 124992, 124996,
+                            125000]),
+       col_indices=tensor([41720,  5446, 23409,  ..., 23991, 37197, 42632]),
+       values=tensor([0.7857, 0.2010, 0.0929,  ..., 0.8446, 0.3352, 0.3559]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.5851, 0.1828, 0.1733,  ..., 0.7326, 0.4663, 0.8685])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.657772779464722 seconds
+
+[20.44, 20.32, 20.44, 20.56, 20.76, 20.84, 20.88, 21.04, 21.08, 21.08]
+[21.08, 20.88, 20.92, 24.6, 25.64, 31.32, 35.8, 37.92, 41.28, 43.2, 43.12, 43.4, 43.48]
+13.586755752563477
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 6322, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.657772779464722, 'TIME_S_1KI': 1.68582296416715, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 425.8721401214599, 'W': 31.344652680689325}
+[20.44, 20.32, 20.44, 20.56, 20.76, 20.84, 20.88, 21.04, 21.08, 21.08, 20.84, 20.88, 20.84, 20.72, 20.96, 20.96, 21.08, 21.16, 21.2, 21.04]
+375.42
+18.771
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 6322, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.657772779464722, 'TIME_S_1KI': 1.68582296416715, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 425.8721401214599, 'W': 31.344652680689325, 'J_1KI': 67.3635147297469, 'W_1KI': 4.958027946961298, 'W_D': 12.573652680689325, 'J_D': 170.83514789009087, 'W_D_1KI': 1.9888726163697126, 'J_D_1KI': 0.31459547870447846}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..3609346
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 98325, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.519834756851196, "TIME_S_1KI": 0.10699043739487614, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 316.21768825531, "W": 22.200046075478067, "J_1KI": 3.216045647142741, "W_1KI": 0.22578231452304162, "W_D": 3.72304607547807, "J_D": 53.03110719919203, "W_D_1KI": 0.03786469438574187, "J_D_1KI": 0.0003850973240350051}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..555507d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,82 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.018892765045166016}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([3456, 1605,  749,  ..., 2516,  837, 4620]),
+       values=tensor([0.8429, 0.4221, 0.2092,  ..., 0.3256, 0.3578, 0.9398]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.1595, 0.2560, 0.8545,  ..., 0.4673, 0.4412, 0.6412])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.018892765045166016 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 55576 -ss 5000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.934850692749023}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    2,    2,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([2304, 3497, 2599,  ..., 3517, 2336, 3180]),
+       values=tensor([7.9793e-01, 1.3489e-04, 7.1193e-01,  ...,
+                      7.4115e-01, 8.0632e-01, 9.8789e-03]), size=(5000, 5000),
+       nnz=2500, layout=torch.sparse_csr)
+tensor([0.4232, 0.5545, 0.0889,  ..., 0.2237, 0.6245, 0.5041])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 5.934850692749023 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 98325 -ss 5000 -sd 0.0001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.519834756851196}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([ 417, 1523, 4116,  ..., 1599, 2107, 3220]),
+       values=tensor([0.7284, 0.4903, 0.1270,  ..., 0.3684, 0.2323, 0.2388]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.8570, 0.2399, 0.2271,  ..., 0.1785, 0.2270, 0.3588])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.519834756851196 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([ 417, 1523, 4116,  ..., 1599, 2107, 3220]),
+       values=tensor([0.7284, 0.4903, 0.1270,  ..., 0.3684, 0.2323, 0.2388]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.8570, 0.2399, 0.2271,  ..., 0.1785, 0.2270, 0.3588])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.519834756851196 seconds
+
+[20.24, 20.24, 20.16, 20.08, 20.32, 20.6, 20.6, 20.56, 20.52, 20.6]
+[20.68, 20.8, 21.12, 22.88, 24.6, 25.72, 26.32, 26.12, 25.12, 25.12, 23.36, 23.52, 23.52, 23.76]
+14.24401044845581
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 98325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.519834756851196, 'TIME_S_1KI': 0.10699043739487614, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.21768825531, 'W': 22.200046075478067}
+[20.24, 20.24, 20.16, 20.08, 20.32, 20.6, 20.6, 20.56, 20.52, 20.6, 20.68, 20.68, 20.72, 20.52, 20.44, 20.56, 20.8, 20.84, 20.76, 20.76]
+369.53999999999996
+18.476999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 98325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.519834756851196, 'TIME_S_1KI': 0.10699043739487614, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.21768825531, 'W': 22.200046075478067, 'J_1KI': 3.216045647142741, 'W_1KI': 0.22578231452304162, 'W_D': 3.72304607547807, 'J_D': 53.03110719919203, 'W_D_1KI': 0.03786469438574187, 'J_D_1KI': 0.0003850973240350051}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..2fa1f21
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 17780, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.605318784713745, "TIME_S_1KI": 0.5964746223123591, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 313.57909806251524, "W": 22.019197605482734, "J_1KI": 17.636619688555413, "W_1KI": 1.2384250621756319, "W_D": 3.4661976054827335, "J_D": 49.36270332407948, "W_D_1KI": 0.19494924665257218, "J_D_1KI": 0.010964524558637355}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..8d3b43b
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06766819953918457}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     7,    15,  ..., 24992, 24996, 25000]),
+       col_indices=tensor([ 734,  800, 1880,  ..., 3125, 3280, 3794]),
+       values=tensor([0.0540, 0.4911, 0.3592,  ..., 0.2590, 0.5736, 0.3057]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9823, 0.9343, 0.9377,  ..., 0.0786, 0.0908, 0.1511])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.06766819953918457 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 15516 -ss 5000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.162637948989868}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     8,    11,  ..., 24988, 24995, 25000]),
+       col_indices=tensor([  62,  227,  575,  ..., 2337, 2631, 3700]),
+       values=tensor([0.5265, 0.4146, 0.5026,  ..., 0.0706, 0.1241, 0.5991]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6610, 0.4053, 0.0257,  ..., 0.7779, 0.2973, 0.6422])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 9.162637948989868 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17780 -ss 5000 -sd 0.001 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.605318784713745}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     6,     8,  ..., 24994, 24997, 25000]),
+       col_indices=tensor([ 423, 1662, 2124,  ...,  288, 1379, 2658]),
+       values=tensor([0.1096, 0.1453, 0.3978,  ..., 0.4089, 0.5724, 0.6122]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.2174, 0.6127, 0.5782,  ..., 0.6057, 0.7055, 0.7233])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.605318784713745 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     6,     8,  ..., 24994, 24997, 25000]),
+       col_indices=tensor([ 423, 1662, 2124,  ...,  288, 1379, 2658]),
+       values=tensor([0.1096, 0.1453, 0.3978,  ..., 0.4089, 0.5724, 0.6122]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.2174, 0.6127, 0.5782,  ..., 0.6057, 0.7055, 0.7233])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.605318784713745 seconds
+
+[20.64, 20.56, 20.28, 20.4, 20.28, 20.28, 20.36, 20.44, 20.4, 20.4]
+[20.64, 20.56, 20.76, 22.16, 23.52, 25.36, 26.08, 26.2, 25.48, 23.84, 23.92, 23.84, 23.84, 23.88]
+14.24116826057434
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 17780, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.605318784713745, 'TIME_S_1KI': 0.5964746223123591, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 313.57909806251524, 'W': 22.019197605482734}
+[20.64, 20.56, 20.28, 20.4, 20.28, 20.28, 20.36, 20.44, 20.4, 20.4, 20.84, 20.68, 20.92, 20.96, 20.88, 20.88, 21.0, 20.8, 20.68, 20.64]
+371.06
+18.553
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 17780, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.605318784713745, 'TIME_S_1KI': 0.5964746223123591, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 313.57909806251524, 'W': 22.019197605482734, 'J_1KI': 17.636619688555413, 'W_1KI': 1.2384250621756319, 'W_D': 3.4661976054827335, 'J_D': 49.36270332407948, 'W_D_1KI': 0.19494924665257218, 'J_D_1KI': 0.010964524558637355}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..84660f7
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 1921, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.388477563858032, "TIME_S_1KI": 5.407848809920892, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 312.91259566307076, "W": 21.966357313024826, "J_1KI": 162.89047145396708, "W_1KI": 11.434855446655297, "W_D": 3.429357313024827, "J_D": 48.85148151707659, "W_D_1KI": 1.785193812089967, "J_D_1KI": 0.9293044310723411}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..1a4afe9
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.593717098236084}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     39,     88,  ..., 249897, 249951,
+                            250000]),
+       col_indices=tensor([   1,   41,  120,  ..., 4868, 4902, 4963]),
+       values=tensor([0.6487, 0.6379, 0.3189,  ..., 0.3941, 0.1960, 0.9453]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9493, 0.7713, 0.4212,  ..., 0.5345, 0.1694, 0.1229])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.593717098236084 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1768 -ss 5000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.65909719467163}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     53,    105,  ..., 249907, 249948,
+                            250000]),
+       col_indices=tensor([ 103,  261,  471,  ..., 4857, 4933, 4959]),
+       values=tensor([0.8889, 0.3073, 0.1638,  ..., 0.6109, 0.3049, 0.0052]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5335, 0.0728, 0.9615,  ..., 0.8926, 0.1348, 0.8188])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 9.65909719467163 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1921 -ss 5000 -sd 0.01 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.388477563858032}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     65,     98,  ..., 249897, 249948,
+                            250000]),
+       col_indices=tensor([ 141,  179,  219,  ..., 4719, 4923, 4985]),
+       values=tensor([0.6589, 0.9882, 0.9555,  ..., 0.3007, 0.0365, 0.3378]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9859, 0.3282, 0.7924,  ..., 0.6550, 0.5905, 0.4141])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.388477563858032 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     65,     98,  ..., 249897, 249948,
+                            250000]),
+       col_indices=tensor([ 141,  179,  219,  ..., 4719, 4923, 4985]),
+       values=tensor([0.6589, 0.9882, 0.9555,  ..., 0.3007, 0.0365, 0.3378]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9859, 0.3282, 0.7924,  ..., 0.6550, 0.5905, 0.4141])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.388477563858032 seconds
+
+[21.12, 20.84, 20.6, 20.44, 20.44, 20.44, 20.52, 20.52, 20.6, 20.96]
+[20.8, 20.8, 20.76, 21.68, 22.28, 24.84, 25.84, 26.2, 25.88, 24.96, 23.84, 23.92, 23.72, 23.88]
+14.245083570480347
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1921, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.388477563858032, 'TIME_S_1KI': 5.407848809920892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 312.91259566307076, 'W': 21.966357313024826}
+[21.12, 20.84, 20.6, 20.44, 20.44, 20.44, 20.52, 20.52, 20.6, 20.96, 20.44, 20.44, 20.44, 20.48, 20.56, 20.68, 20.68, 20.56, 20.84, 20.8]
+370.74
+18.537
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 1921, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.388477563858032, 'TIME_S_1KI': 5.407848809920892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 312.91259566307076, 'W': 21.966357313024826, 'J_1KI': 162.89047145396708, 'W_1KI': 11.434855446655297, 'W_D': 3.429357313024827, 'J_D': 48.85148151707659, 'W_D_1KI': 1.785193812089967, 'J_D_1KI': 0.9293044310723411}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..50afdc0
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 396, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.481398582458496, "TIME_S_1KI": 26.468178238531554, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 323.34643746376037, "W": 22.685982650996596, "J_1KI": 816.5314077367685, "W_1KI": 57.28783497726413, "W_D": 4.214982650996596, "J_D": 60.07672866272925, "W_D_1KI": 10.643895583324738, "J_D_1KI": 26.878524200314995}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..07e41de
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.6507530212402344}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     267,     531,  ..., 1249531,
+                            1249748, 1250000]),
+       col_indices=tensor([  12,   24,   45,  ..., 4958, 4983, 4986]),
+       values=tensor([0.7384, 0.2434, 0.0755,  ..., 0.4736, 0.1384, 0.4678]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.2921, 0.5624, 0.4015,  ..., 0.8005, 0.9400, 0.6114])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 2.6507530212402344 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 396 -ss 5000 -sd 0.05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.481398582458496}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     247,     505,  ..., 1249488,
+                            1249757, 1250000]),
+       col_indices=tensor([  27,   35,   41,  ..., 4930, 4938, 4952]),
+       values=tensor([0.8294, 0.9821, 0.6691,  ..., 0.3905, 0.4873, 0.1672]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.8352, 0.4457, 0.1150,  ..., 0.9988, 0.2164, 0.9018])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.481398582458496 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     247,     505,  ..., 1249488,
+                            1249757, 1250000]),
+       col_indices=tensor([  27,   35,   41,  ..., 4930, 4938, 4952]),
+       values=tensor([0.8294, 0.9821, 0.6691,  ..., 0.3905, 0.4873, 0.1672]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.8352, 0.4457, 0.1150,  ..., 0.9988, 0.2164, 0.9018])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.481398582458496 seconds
+
+[20.72, 20.84, 20.92, 20.68, 20.6, 20.44, 20.48, 20.24, 20.44, 20.24]
+[20.24, 20.52, 20.72, 22.36, 24.28, 26.72, 27.0, 27.56, 26.28, 25.88, 24.68, 24.52, 24.36, 24.36]
+14.253137826919556
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 396, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.481398582458496, 'TIME_S_1KI': 26.468178238531554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 323.34643746376037, 'W': 22.685982650996596}
+[20.72, 20.84, 20.92, 20.68, 20.6, 20.44, 20.48, 20.24, 20.44, 20.24, 20.24, 20.24, 20.36, 20.48, 20.48, 20.6, 20.68, 20.64, 20.4, 20.6]
+369.42
+18.471
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 396, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.481398582458496, 'TIME_S_1KI': 26.468178238531554, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 323.34643746376037, 'W': 22.685982650996596, 'J_1KI': 816.5314077367685, 'W_1KI': 57.28783497726413, 'W_D': 4.214982650996596, 'J_D': 60.07672866272925, 'W_D_1KI': 10.643895583324738, 'J_D_1KI': 26.878524200314995}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..721e6aa
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 199, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.477930784225464, "TIME_S_1KI": 52.6529185136958, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 366.7794834327698, "W": 23.985504753820422, "J_1KI": 1843.11298207422, "W_1KI": 120.53017464231368, "W_D": 5.225504753820424, "J_D": 79.90692520141607, "W_D_1KI": 26.25881785839409, "J_D_1KI": 131.95385858489493}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..a6a4a54
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.1 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.267488241195679}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     489,     972,  ..., 2499002,
+                            2499515, 2500000]),
+       col_indices=tensor([   0,    4,   21,  ..., 4965, 4988, 4998]),
+       values=tensor([0.4985, 0.2439, 0.0801,  ..., 0.3726, 0.6532, 0.2308]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7620, 0.1310, 0.6898,  ..., 0.4324, 0.6267, 0.4614])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 5.267488241195679 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 199 -ss 5000 -sd 0.1 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.477930784225464}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     479,     980,  ..., 2498983,
+                            2499479, 2500000]),
+       col_indices=tensor([   7,   13,   23,  ..., 4987, 4988, 4998]),
+       values=tensor([0.4519, 0.3203, 0.6830,  ..., 0.2361, 0.6866, 0.7928]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4502, 0.7188, 0.8112,  ..., 0.2797, 0.2285, 0.9848])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.477930784225464 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     479,     980,  ..., 2498983,
+                            2499479, 2500000]),
+       col_indices=tensor([   7,   13,   23,  ..., 4987, 4988, 4998]),
+       values=tensor([0.4519, 0.3203, 0.6830,  ..., 0.2361, 0.6866, 0.7928]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4502, 0.7188, 0.8112,  ..., 0.2797, 0.2285, 0.9848])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.477930784225464 seconds
+
+[20.56, 20.84, 21.36, 21.88, 21.64, 21.6, 21.4, 20.76, 20.76, 20.72]
+[21.16, 21.2, 21.4, 25.96, 27.32, 30.6, 31.24, 28.68, 27.4, 26.08, 24.2, 24.2, 24.4, 24.36, 24.2]
+15.291714191436768
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 199, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.477930784225464, 'TIME_S_1KI': 52.6529185136958, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 366.7794834327698, 'W': 23.985504753820422}
+[20.56, 20.84, 21.36, 21.88, 21.64, 21.6, 21.4, 20.76, 20.76, 20.72, 20.44, 20.32, 20.44, 20.64, 20.64, 20.44, 20.4, 20.36, 20.44, 20.84]
+375.2
+18.759999999999998
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 199, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.477930784225464, 'TIME_S_1KI': 52.6529185136958, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 366.7794834327698, 'W': 23.985504753820422, 'J_1KI': 1843.11298207422, 'W_1KI': 120.53017464231368, 'W_D': 5.225504753820424, 'J_D': 79.90692520141607, 'W_D_1KI': 26.25881785839409, 'J_D_1KI': 131.95385858489493}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..328683d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.774195671081543, "TIME_S_1KI": 107.74195671081543, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 357.5530287361145, "W": 23.422029760012947, "J_1KI": 3575.5302873611454, "W_1KI": 234.22029760012947, "W_D": 5.2480297600129475, "J_D": 80.11470204830175, "W_D_1KI": 52.480297600129475, "J_D_1KI": 524.8029760012947}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..4419f1e
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.2 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.774195671081543}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1044,    1986,  ..., 4998023,
+                            4998990, 5000000]),
+       col_indices=tensor([   2,   11,   17,  ..., 4984, 4985, 4991]),
+       values=tensor([0.4872, 0.8747, 0.2341,  ..., 0.7866, 0.4499, 0.5164]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.5529, 0.0016, 0.5040,  ..., 0.3915, 0.6771, 0.4202])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.774195671081543 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1044,    1986,  ..., 4998023,
+                            4998990, 5000000]),
+       col_indices=tensor([   2,   11,   17,  ..., 4984, 4985, 4991]),
+       values=tensor([0.4872, 0.8747, 0.2341,  ..., 0.7866, 0.4499, 0.5164]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.5529, 0.0016, 0.5040,  ..., 0.3915, 0.6771, 0.4202])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.774195671081543 seconds
+
+[20.36, 20.12, 20.12, 20.04, 20.28, 20.12, 20.08, 20.4, 20.4, 20.72]
+[20.8, 21.08, 22.4, 23.32, 25.48, 25.48, 27.96, 29.08, 28.16, 27.04, 25.64, 24.2, 24.2, 24.4, 24.44]
+15.265672206878662
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.774195671081543, 'TIME_S_1KI': 107.74195671081543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.5530287361145, 'W': 23.422029760012947}
+[20.36, 20.12, 20.12, 20.04, 20.28, 20.12, 20.08, 20.4, 20.4, 20.72, 20.32, 20.16, 20.04, 20.08, 20.08, 20.0, 20.16, 20.28, 20.28, 20.28]
+363.48
+18.174
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.774195671081543, 'TIME_S_1KI': 107.74195671081543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.5530287361145, 'W': 23.422029760012947, 'J_1KI': 3575.5302873611454, 'W_1KI': 234.22029760012947, 'W_D': 5.2480297600129475, 'J_D': 80.11470204830175, 'W_D_1KI': 52.480297600129475, 'J_D_1KI': 524.8029760012947}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..1787dbd
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 15.919427633285522, "TIME_S_1KI": 159.19427633285522, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 506.52752479553226, "W": 23.776826253573663, "J_1KI": 5065.275247955323, "W_1KI": 237.76826253573662, "W_D": 5.445826253573667, "J_D": 116.01468014574058, "W_D_1KI": 54.45826253573667, "J_D_1KI": 544.5826253573666}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..2f8bfb8
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.3 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 15.919427633285522}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1464,    2929,  ..., 7497018,
+                            7498512, 7500000]),
+       col_indices=tensor([   1,    9,   13,  ..., 4985, 4989, 4990]),
+       values=tensor([0.4014, 0.1905, 0.8906,  ..., 0.4332, 0.9731, 0.1283]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.5776, 0.8031, 0.5959,  ..., 0.3626, 0.0858, 0.0842])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 15.919427633285522 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1464,    2929,  ..., 7497018,
+                            7498512, 7500000]),
+       col_indices=tensor([   1,    9,   13,  ..., 4985, 4989, 4990]),
+       values=tensor([0.4014, 0.1905, 0.8906,  ..., 0.4332, 0.9731, 0.1283]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.5776, 0.8031, 0.5959,  ..., 0.3626, 0.0858, 0.0842])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 15.919427633285522 seconds
+
+[20.2, 20.32, 20.32, 20.32, 20.32, 20.24, 20.52, 20.56, 20.64, 20.76]
+[20.76, 20.64, 21.8, 21.8, 22.88, 24.6, 28.28, 30.28, 29.56, 29.28, 26.08, 25.32, 24.68, 24.72, 24.64, 24.44, 24.44, 24.48, 24.24, 24.32, 24.48]
+21.303411960601807
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 15.919427633285522, 'TIME_S_1KI': 159.19427633285522, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 506.52752479553226, 'W': 23.776826253573663}
+[20.2, 20.32, 20.32, 20.32, 20.32, 20.24, 20.52, 20.56, 20.64, 20.76, 20.12, 20.12, 19.96, 20.2, 20.04, 20.4, 20.6, 20.56, 20.64, 20.64]
+366.61999999999995
+18.330999999999996
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 15.919427633285522, 'TIME_S_1KI': 159.19427633285522, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 506.52752479553226, 'W': 23.776826253573663, 'J_1KI': 5065.275247955323, 'W_1KI': 237.76826253573662, 'W_D': 5.445826253573667, 'J_D': 116.01468014574058, 'W_D_1KI': 54.45826253573667, 'J_D_1KI': 544.5826253573666}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..d18f4ce
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.475390195846558, "TIME_S_1KI": 214.75390195846558, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 659.3497357940673, "W": 24.06026504427972, "J_1KI": 6593.497357940673, "W_1KI": 240.60265044279723, "W_D": 5.657265044279722, "J_D": 155.03221620368953, "W_D_1KI": 56.57265044279722, "J_D_1KI": 565.7265044279723}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..6dfec86
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.4 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.475390195846558}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2112,     4108,  ...,  9995977,
+                             9998003, 10000000]),
+       col_indices=tensor([   0,    2,    5,  ..., 4993, 4997, 4998]),
+       values=tensor([0.6521, 0.2294, 0.7060,  ..., 0.9592, 0.5713, 0.6385]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2067, 0.4320, 0.3905,  ..., 0.7782, 0.8244, 0.2696])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 21.475390195846558 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2112,     4108,  ...,  9995977,
+                             9998003, 10000000]),
+       col_indices=tensor([   0,    2,    5,  ..., 4993, 4997, 4998]),
+       values=tensor([0.6521, 0.2294, 0.7060,  ..., 0.9592, 0.5713, 0.6385]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2067, 0.4320, 0.3905,  ..., 0.7782, 0.8244, 0.2696])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 21.475390195846558 seconds
+
+[20.36, 20.32, 20.12, 20.16, 20.12, 20.28, 20.68, 20.96, 21.08, 21.04]
+[20.56, 20.44, 20.44, 23.96, 25.32, 27.12, 30.0, 32.28, 29.04, 28.16, 26.48, 25.72, 24.44, 24.36, 24.24, 24.24, 24.2, 24.16, 24.28, 24.32, 24.36, 24.48, 24.04, 23.92, 23.8, 23.68, 23.88]
+27.40409278869629
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.475390195846558, 'TIME_S_1KI': 214.75390195846558, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 659.3497357940673, 'W': 24.06026504427972}
+[20.36, 20.32, 20.12, 20.16, 20.12, 20.28, 20.68, 20.96, 21.08, 21.04, 20.4, 20.4, 20.36, 20.56, 20.6, 20.4, 20.24, 20.36, 20.28, 20.48]
+368.06
+18.403
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.475390195846558, 'TIME_S_1KI': 214.75390195846558, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 659.3497357940673, 'W': 24.06026504427972, 'J_1KI': 6593.497357940673, 'W_1KI': 240.60265044279723, 'W_D': 5.657265044279722, 'J_D': 155.03221620368953, 'W_D_1KI': 56.57265044279722, 'J_D_1KI': 565.7265044279723}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..dc66501
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.60726523399353, "TIME_S_1KI": 266.0726523399353, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 821.7415859985354, "W": 24.511735271206813, "J_1KI": 8217.415859985353, "W_1KI": 245.11735271206814, "W_D": 6.057735271206813, "J_D": 203.08203129005446, "W_D_1KI": 60.57735271206813, "J_D_1KI": 605.7735271206813}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..ad4e507
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.5 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.60726523399353}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2547,     5140,  ..., 12494977,
+                            12497506, 12500000]),
+       col_indices=tensor([   3,    4,    6,  ..., 4994, 4995, 4998]),
+       values=tensor([0.6176, 0.1216, 0.2065,  ..., 0.5783, 0.0575, 0.3833]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.3583, 0.1424, 0.2491,  ..., 0.0607, 0.2583, 0.4693])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 26.60726523399353 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2547,     5140,  ..., 12494977,
+                            12497506, 12500000]),
+       col_indices=tensor([   3,    4,    6,  ..., 4994, 4995, 4998]),
+       values=tensor([0.6176, 0.1216, 0.2065,  ..., 0.5783, 0.0575, 0.3833]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.3583, 0.1424, 0.2491,  ..., 0.0607, 0.2583, 0.4693])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 26.60726523399353 seconds
+
+[20.28, 20.52, 20.68, 20.56, 20.44, 20.52, 20.44, 20.44, 20.72, 21.0]
+[20.92, 20.68, 20.68, 23.84, 26.2, 27.84, 31.04, 29.96, 30.76, 29.48, 26.6, 26.6, 26.28, 24.68, 24.6, 24.48, 24.4, 24.24, 24.32, 24.32, 24.48, 24.52, 24.44, 24.52, 24.52, 24.56, 24.4, 24.56, 24.72, 24.68, 24.84, 24.8, 24.64]
+33.524415016174316
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.60726523399353, 'TIME_S_1KI': 266.0726523399353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 821.7415859985354, 'W': 24.511735271206813}
+[20.28, 20.52, 20.68, 20.56, 20.44, 20.52, 20.44, 20.44, 20.72, 21.0, 20.24, 20.2, 20.4, 20.6, 20.6, 20.6, 20.44, 20.56, 20.52, 20.16]
+369.08000000000004
+18.454
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.60726523399353, 'TIME_S_1KI': 266.0726523399353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 821.7415859985354, 'W': 24.511735271206813, 'J_1KI': 8217.415859985353, 'W_1KI': 245.11735271206814, 'W_D': 6.057735271206813, 'J_D': 203.08203129005446, 'W_D_1KI': 60.57735271206813, 'J_D_1KI': 605.7735271206813}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..b24de3c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 284909, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.16942024230957, "TIME_S_1KI": 0.035693573184102885, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 316.9542731094361, "W": 22.304497332617817, "J_1KI": 1.1124754679895548, "W_1KI": 0.07828639085679223, "W_D": 3.6764973326178207, "J_D": 52.24424125194558, "W_D_1KI": 0.012904110900736098, "J_D_1KI": 4.529204377796454e-05}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..ece41ca
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,329 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.012842655181884766}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1287, 2037,  612, 4005,  465, 4495, 4486, 1954, 4095,
+                           1514, 3786, 3287, 3358, 3432, 3673,  489, 2823,  505,
+                           4424, 1572, 4277,  474, 3301,   30, 2842, 4780, 2739,
+                            564, 2900, 4485, 4784, 2295,  755, 3717, 1261, 1856,
+                           2818, 3372, 3761, 1939, 4279, 1416, 4196, 1024,  159,
+                           3430, 1464,  630, 4128, 1057, 4758, 4930, 4819, 4211,
+                           3868, 1700, 2760, 4521, 1355, 4737, 4580, 1838, 4056,
+                           1953, 4561, 1726, 3125, 4174,  510, 4743,  502, 2822,
+                            338, 1706, 4412, 4712, 3417, 4607, 4478, 4287, 4365,
+                           4223, 3755,  467, 2870,  999, 1516, 3711, 3345, 4540,
+                           4303, 4477, 4047, 3188,  522,  451, 4048, 1301, 3760,
+                           3807,  142,  526, 3797, 3415,  942, 3041, 1022,  555,
+                           2433, 3440, 4291, 2481, 2516, 1226, 4664, 1242, 2239,
+                           3542, 3300, 3985, 1261,  628, 3797, 3571, 1648,  545,
+                           3417,  523,  297, 1814, 2339, 1387, 4149, 2499, 1698,
+                           4107, 3910,  907, 1601, 3072, 2976, 1955,   76, 3173,
+                             63,  633, 2089, 1360, 1226, 4574,  730, 2472, 4618,
+                            425, 3915, 1299, 1950, 4945, 1796,  628, 1797, 3210,
+                           2055, 2428,  876, 1161, 1529, 1660, 2886, 4614, 2062,
+                           2433, 3539, 1521,   33, 1294, 4198,  863, 2582, 1498,
+                             77,  507, 2697, 2034, 2514, 1935, 4132, 2925,  876,
+                           2808, 4770,  271, 3697, 1635, 2519, 4995, 3590, 3245,
+                            130,  480, 3111, 3121, 3132, 1937, 3910, 1943, 2562,
+                            426, 3962, 1910, 1189, 1897, 1056,  462, 1607, 1444,
+                            118,  191, 2005,  615, 1379,  633, 2360, 3526, 4732,
+                           2267, 3397, 1029, 3432, 2182, 2675, 4099, 3777, 2171,
+                           2640, 3913, 4300, 2946, 3758, 3305, 1103, 4800, 3668,
+                           4286, 3562,  281,  919, 4442, 2167, 2728]),
+       values=tensor([0.8347, 0.3655, 0.0811, 0.8356, 0.0205, 0.3330, 0.9286,
+                      0.0736, 0.7654, 0.8451, 0.0234, 0.4126, 0.2439, 0.1012,
+                      0.1525, 0.4404, 0.8423, 0.5434, 0.2968, 0.3607, 0.9939,
+                      0.0443, 0.6432, 0.5086, 0.6326, 0.2329, 0.7870, 0.7820,
+                      0.9646, 0.4656, 0.9109, 0.0130, 0.3562, 0.2378, 0.0761,
+                      0.1724, 0.0722, 0.8084, 0.1566, 0.8788, 0.9593, 0.2473,
+                      0.2746, 0.1767, 0.8469, 0.1106, 0.8653, 0.5297, 0.8543,
+                      0.5387, 0.4683, 0.0500, 0.6408, 0.2485, 0.5053, 0.9278,
+                      0.6730, 0.1223, 0.9361, 0.1415, 0.0908, 0.6368, 0.4532,
+                      0.7711, 0.1924, 0.7435, 0.0645, 0.3989, 0.7433, 0.7022,
+                      0.6974, 0.8264, 0.3293, 0.6363, 0.9947, 0.1723, 0.3099,
+                      0.5498, 0.6041, 0.9256, 0.6505, 0.2218, 0.5727, 0.8460,
+                      0.3386, 0.9152, 0.1985, 0.3213, 0.2437, 0.8619, 0.4265,
+                      0.8019, 0.3028, 0.4559, 0.9203, 0.9762, 0.2222, 0.3112,
+                      0.4047, 0.0709, 0.2379, 0.3209, 0.9982, 0.9963, 0.6946,
+                      0.0267, 0.7677, 0.2026, 0.6034, 0.5006, 0.8273, 0.2191,
+                      0.6497, 0.2706, 0.0892, 0.8677, 0.9857, 0.5541, 0.2974,
+                      0.1559, 0.1745, 0.4744, 0.1426, 0.1224, 0.3669, 0.1827,
+                      0.5044, 0.5810, 0.3220, 0.7231, 0.9240, 0.0412, 0.3152,
+                      0.9088, 0.3617, 0.9935, 0.3508, 0.0434, 0.0453, 0.5299,
+                      0.2529, 0.0232, 0.7419, 0.0564, 0.5519, 0.6136, 0.5013,
+                      0.9801, 0.4708, 0.5636, 0.5144, 0.1368, 0.7207, 0.1775,
+                      0.9552, 0.2262, 0.7144, 0.1124, 0.8514, 0.1783, 0.8401,
+                      0.1256, 0.7454, 0.1258, 0.2191, 0.5753, 0.9252, 0.8693,
+                      0.6514, 0.3440, 0.7780, 0.4771, 0.0787, 0.5042, 0.0634,
+                      0.8013, 0.8286, 0.4280, 0.3433, 0.9749, 0.0712, 0.9286,
+                      0.0320, 0.8979, 0.5094, 0.4000, 0.4693, 0.8308, 0.6000,
+                      0.3933, 0.7591, 0.2335, 0.5450, 0.3018, 0.3121, 0.4779,
+                      0.9302, 0.5324, 0.1295, 0.6438, 0.5030, 0.3371, 0.9613,
+                      0.8059, 0.9687, 0.2898, 0.7067, 0.8974, 0.1763, 0.0222,
+                      0.0300, 0.9494, 0.3209, 0.6515, 0.7028, 0.8063, 0.2794,
+                      0.7392, 0.1814, 0.3171, 0.4591, 0.7578, 0.6336, 0.8392,
+                      0.6142, 0.8521, 0.4206, 0.9799, 0.4517, 0.1512, 0.3696,
+                      0.0957, 0.3165, 0.3328, 0.9242, 0.5247, 0.8176, 0.9760,
+                      0.3689, 0.9384, 0.3805, 0.7826, 0.4113, 0.3311, 0.7250,
+                      0.9146, 0.3319, 0.6199, 0.8288, 0.1278]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.0363, 0.1704, 0.8959,  ..., 0.1381, 0.6314, 0.8045])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.012842655181884766 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 81758 -ss 5000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.01309871673584}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([3901, 3663,  229, 3286, 2751, 3483, 2128, 1446, 4503,
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+                           4165, 1821, 3059,  730, 1341, 2913, 4066, 2638, 3070,
+                            430,  826, 3316, 2761, 4646, 4961, 2657]),
+       values=tensor([0.2777, 0.7994, 0.6055, 0.8858, 0.5401, 0.6289, 0.2315,
+                      0.7108, 0.2919, 0.9440, 0.4352, 0.9935, 0.7547, 0.9487,
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+                      0.3534, 0.8270, 0.9704, 0.5262, 0.1397]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.2610, 0.0051, 0.8611,  ..., 0.6706, 0.7457, 0.2823])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 3.01309871673584 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 284909 -ss 5000 -sd 1e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.16942024230957}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1669, 2388, 3410,  214, 4888, 2047, 1859, 3824, 1130,
+                           3331, 4650,  808, 1845, 4600, 2980, 4756, 2639, 4242,
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+                           1530, 1512, 2880, 1124,  741, 2337, 2820, 1096,  969,
+                           4662, 1861, 4067, 2109, 3996, 1635,  499]),
+       values=tensor([0.8132, 0.1702, 0.5583, 0.1261, 0.6291, 0.5508, 0.1330,
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+                      0.1394, 0.2220, 0.2355, 0.9726, 0.9750]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.9060, 0.0911, 0.6185,  ..., 0.7353, 0.0547, 0.2301])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.16942024230957 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1669, 2388, 3410,  214, 4888, 2047, 1859, 3824, 1130,
+                           3331, 4650,  808, 1845, 4600, 2980, 4756, 2639, 4242,
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+                           1530, 1512, 2880, 1124,  741, 2337, 2820, 1096,  969,
+                           4662, 1861, 4067, 2109, 3996, 1635,  499]),
+       values=tensor([0.8132, 0.1702, 0.5583, 0.1261, 0.6291, 0.5508, 0.1330,
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+                      0.7999, 0.0688, 0.3315, 0.2496, 0.2006, 0.0199, 0.1239,
+                      0.0030, 0.9251, 0.8374, 0.2492, 0.6001, 0.0171, 0.3645,
+                      0.9564, 0.7314, 0.8427, 0.8917, 0.1465, 0.2355, 0.6975,
+                      0.9025, 0.0358, 0.2860, 0.4051, 0.9734, 0.8626, 0.4028,
+                      0.9642, 0.0743, 0.8714, 0.6919, 0.3640, 0.9239, 0.1573,
+                      0.9549, 0.3068, 0.2789, 0.0169, 0.6253, 0.7318, 0.1857,
+                      0.1394, 0.2220, 0.2355, 0.9726, 0.9750]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.9060, 0.0911, 0.6185,  ..., 0.7353, 0.0547, 0.2301])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.16942024230957 seconds
+
+[20.28, 20.4, 20.4, 20.48, 20.76, 21.28, 21.16, 20.96, 20.76, 20.4]
+[20.12, 20.44, 21.32, 22.44, 25.12, 25.12, 25.76, 26.28, 25.96, 25.44, 23.88, 24.04, 24.16, 23.84]
+14.210330247879028
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 284909, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.16942024230957, 'TIME_S_1KI': 0.035693573184102885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.9542731094361, 'W': 22.304497332617817}
+[20.28, 20.4, 20.4, 20.48, 20.76, 21.28, 21.16, 20.96, 20.76, 20.4, 20.8, 20.88, 20.72, 20.76, 20.84, 20.84, 20.44, 20.52, 20.32, 20.6]
+372.55999999999995
+18.627999999999997
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 284909, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.16942024230957, 'TIME_S_1KI': 0.035693573184102885, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.9542731094361, 'W': 22.304497332617817, 'J_1KI': 1.1124754679895548, 'W_1KI': 0.07828639085679223, 'W_D': 3.6764973326178207, 'J_D': 52.24424125194558, 'W_D_1KI': 0.012904110900736098, 'J_D_1KI': 4.529204377796454e-05}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..758834a
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 16, "ITERATIONS": 154432, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.621034145355225, "TIME_S_1KI": 0.06877482740206191, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 316.4244461250305, "W": 22.262246477486542, "J_1KI": 2.0489564735613763, "W_1KI": 0.144155657360434, "W_D": 3.7392464774865424, "J_D": 53.14778078484536, "W_D_1KI": 0.024212899382812774, "J_D_1KI": 0.00015678680184685024}
diff --git a/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..8f01bb5
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/altra_16_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.014879941940307617}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1248, 1250, 1250]),
+       col_indices=tensor([1397, 3608,  621,  ..., 1983, 2722, 4972]),
+       values=tensor([0.7898, 0.8890, 0.9853,  ..., 0.2806, 0.4332, 0.7785]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.8515, 0.1205, 0.1290,  ..., 0.0596, 0.1294, 0.2178])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.014879941940307617 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 70564 -ss 5000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.797720670700073}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([4236, 1927,  389,  ..., 3900, 4084, 4178]),
+       values=tensor([0.5819, 0.5926, 0.4032,  ..., 0.1422, 0.8129, 0.9187]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.4782, 0.7587, 0.6755,  ..., 0.4641, 0.3230, 0.1517])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 4.797720670700073 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 154432 -ss 5000 -sd 5e-05 -c 16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.621034145355225}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([  91, 2944, 3974,  ..., 4430,   70, 3263]),
+       values=tensor([0.2553, 0.0855, 0.4739,  ..., 0.3797, 0.6721, 0.4378]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.8009, 0.9874, 0.1682,  ..., 0.8612, 0.3697, 0.0752])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.621034145355225 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([  91, 2944, 3974,  ..., 4430,   70, 3263]),
+       values=tensor([0.2553, 0.0855, 0.4739,  ..., 0.3797, 0.6721, 0.4378]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.8009, 0.9874, 0.1682,  ..., 0.8612, 0.3697, 0.0752])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.621034145355225 seconds
+
+[20.4, 20.68, 20.44, 20.16, 20.16, 20.48, 20.44, 20.68, 20.64, 20.88]
+[20.92, 20.96, 21.52, 22.88, 25.2, 25.52, 26.2, 26.2, 25.6, 24.92, 23.32, 23.36, 23.56, 23.44]
+14.213500261306763
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 154432, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.621034145355225, 'TIME_S_1KI': 0.06877482740206191, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.4244461250305, 'W': 22.262246477486542}
+[20.4, 20.68, 20.44, 20.16, 20.16, 20.48, 20.44, 20.68, 20.64, 20.88, 20.72, 20.64, 20.68, 20.52, 20.64, 20.8, 20.76, 20.76, 20.68, 20.6]
+370.46
+18.523
+{'CPU': 'Altra', 'CORES': 16, 'ITERATIONS': 154432, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.621034145355225, 'TIME_S_1KI': 0.06877482740206191, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.4244461250305, 'W': 22.262246477486542, 'J_1KI': 2.0489564735613763, 'W_1KI': 0.144155657360434, 'W_D': 3.7392464774865424, 'J_D': 53.14778078484536, 'W_D_1KI': 0.024212899382812774, 'J_D_1KI': 0.00015678680184685024}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..a438e21
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 65446, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.748796463012695, "TIME_S_1KI": 0.16423916607604278, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1866.1015530323982, "W": 143.13, "J_1KI": 28.51360744785622, "W_1KI": 2.1869938575314, "W_D": 106.99974999999999, "J_D": 1395.0422668139338, "W_D_1KI": 1.634931852214039, "J_D_1KI": 0.024981386978792274}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..7f0e014
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.060246944427490234}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      16,      25,  ...,  999980,
+                             999989, 1000000]),
+       col_indices=tensor([ 4573,  4595,  4948,  ..., 71788, 92544, 99741]),
+       values=tensor([0.3512, 0.1040, 0.2729,  ..., 0.2513, 0.9554, 0.9408]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1257, 0.5794, 0.5612,  ..., 0.8235, 0.1474, 0.3975])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.060246944427490234 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '17428', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.7960927486419678}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      17,  ...,  999980,
+                             999989, 1000000]),
+       col_indices=tensor([11836, 34889, 39226,  ..., 79566, 86668, 94364]),
+       values=tensor([0.7886, 0.3777, 0.4340,  ..., 0.5250, 0.8836, 0.4934]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9435, 0.7532, 0.3829,  ..., 0.0561, 0.6547, 0.0145])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 2.7960927486419678 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65446', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.748796463012695}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      17,  ...,  999978,
+                             999990, 1000000]),
+       col_indices=tensor([ 6624,  6694, 37331,  ..., 71444, 97628, 99166]),
+       values=tensor([0.8094, 0.0427, 0.0622,  ..., 0.4502, 0.4633, 0.1157]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2357, 0.1643, 0.3206,  ..., 0.7759, 0.8620, 0.1771])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.748796463012695 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      17,  ...,  999978,
+                             999990, 1000000]),
+       col_indices=tensor([ 6624,  6694, 37331,  ..., 71444, 97628, 99166]),
+       values=tensor([0.8094, 0.0427, 0.0622,  ..., 0.4502, 0.4633, 0.1157]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2357, 0.1643, 0.3206,  ..., 0.7759, 0.8620, 0.1771])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.748796463012695 seconds
+
+[40.51, 40.31, 40.08, 39.71, 39.8, 39.59, 39.58, 41.48, 39.56, 40.06]
+[143.13]
+13.037808656692505
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 65446, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.748796463012695, 'TIME_S_1KI': 0.16423916607604278, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1866.1015530323982, 'W': 143.13}
+[40.51, 40.31, 40.08, 39.71, 39.8, 39.59, 39.58, 41.48, 39.56, 40.06, 41.48, 39.67, 40.17, 39.98, 41.44, 39.98, 40.5, 40.23, 39.59, 39.82]
+722.605
+36.130250000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 65446, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.748796463012695, 'TIME_S_1KI': 0.16423916607604278, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1866.1015530323982, 'W': 143.13, 'J_1KI': 28.51360744785622, 'W_1KI': 2.1869938575314, 'W_D': 106.99974999999999, 'J_D': 1395.0422668139338, 'W_D_1KI': 1.634931852214039, 'J_D_1KI': 0.024981386978792274}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..d0be3f6
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4693, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 12.009309530258179, "TIME_S_1KI": 2.558983492490556, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2204.305100774765, "W": 128.95, "J_1KI": 469.7006394150362, "W_1KI": 27.477093543575535, "W_D": 92.7465, "J_D": 1585.4329820008277, "W_D_1KI": 19.76273172810569, "J_D_1KI": 4.211108401471487}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..561971c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.27472805976867676}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      103,      224,  ...,  9999788,
+                             9999890, 10000000]),
+       col_indices=tensor([  311,  3365,  5161,  ..., 98602, 99530, 99576]),
+       values=tensor([0.9917, 0.0583, 0.3712,  ..., 0.9136, 0.4986, 0.7909]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4323, 0.4083, 0.9080,  ..., 0.7530, 0.1922, 0.7136])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 0.27472805976867676 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3821', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.548935651779175}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       86,      193,  ...,  9999790,
+                             9999889, 10000000]),
+       col_indices=tensor([  598,  3163,  6325,  ..., 93333, 94869, 95502]),
+       values=tensor([0.3479, 0.2007, 0.7107,  ..., 0.5121, 0.1193, 0.0296]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.9967, 0.6546, 0.0107,  ..., 0.1473, 0.4856, 0.1261])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 8.548935651779175 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4693', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 12.009309530258179}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       80,      177,  ...,  9999782,
+                             9999890, 10000000]),
+       col_indices=tensor([ 1894,  3295,  3747,  ..., 98404, 98823, 99018]),
+       values=tensor([0.1540, 0.7163, 0.3077,  ..., 0.3211, 0.5255, 0.5012]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8104, 0.7178, 0.6885,  ..., 0.8661, 0.7147, 0.1559])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 12.009309530258179 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       80,      177,  ...,  9999782,
+                             9999890, 10000000]),
+       col_indices=tensor([ 1894,  3295,  3747,  ..., 98404, 98823, 99018]),
+       values=tensor([0.1540, 0.7163, 0.3077,  ..., 0.3211, 0.5255, 0.5012]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8104, 0.7178, 0.6885,  ..., 0.8661, 0.7147, 0.1559])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 12.009309530258179 seconds
+
+[41.32, 39.94, 39.97, 39.81, 39.83, 40.24, 40.5, 40.22, 40.21, 41.33]
+[128.95]
+17.09426212310791
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 12.009309530258179, 'TIME_S_1KI': 2.558983492490556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2204.305100774765, 'W': 128.95}
+[41.32, 39.94, 39.97, 39.81, 39.83, 40.24, 40.5, 40.22, 40.21, 41.33, 40.63, 40.93, 40.28, 39.66, 39.87, 41.7, 39.67, 40.03, 39.68, 39.78]
+724.0699999999999
+36.2035
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 12.009309530258179, 'TIME_S_1KI': 2.558983492490556, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2204.305100774765, 'W': 128.95, 'J_1KI': 469.7006394150362, 'W_1KI': 27.477093543575535, 'W_D': 92.7465, 'J_D': 1585.4329820008277, 'W_D_1KI': 19.76273172810569, 'J_D_1KI': 4.211108401471487}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..5f6b7ac
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 99857, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.432250738143921, "TIME_S_1KI": 0.10447190220158749, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1536.9044136524199, "W": 115.09999999999998, "J_1KI": 15.391053342804408, "W_1KI": 1.152648287050482, "W_D": 79.15799999999997, "J_D": 1056.9789711198803, "W_D_1KI": 0.7927135804200004, "J_D_1KI": 0.007938487841813797}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..5aa8a41
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.043670654296875}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99996,  99998,
+                            100000]),
+       col_indices=tensor([ 6609, 19255, 81333,  ..., 81128, 51531, 76130]),
+       values=tensor([0.9876, 0.0139, 0.8085,  ..., 0.3685, 0.4758, 0.0266]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1735, 0.8240, 0.8190,  ..., 0.4288, 0.7745, 0.1715])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.043670654296875 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24043', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.5281074047088623}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99997,  99999,
+                            100000]),
+       col_indices=tensor([69039, 75318, 84133,  ..., 16483, 23976, 47642]),
+       values=tensor([0.3961, 0.2517, 0.3876,  ..., 0.3761, 0.7912, 0.1675]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.9918, 0.3750, 0.7737,  ..., 0.5214, 0.0832, 0.2225])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 2.5281074047088623 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '99857', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.432250738143921}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998, 100000,
+                            100000]),
+       col_indices=tensor([18969, 38131, 43029,  ..., 81495,  1519, 27704]),
+       values=tensor([0.3850, 0.3770, 0.8820,  ..., 0.3865, 0.0804, 0.8829]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4374, 0.1348, 0.8967,  ..., 0.5157, 0.0353, 0.0014])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.432250738143921 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998, 100000,
+                            100000]),
+       col_indices=tensor([18969, 38131, 43029,  ..., 81495,  1519, 27704]),
+       values=tensor([0.3850, 0.3770, 0.8820,  ..., 0.3865, 0.0804, 0.8829]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4374, 0.1348, 0.8967,  ..., 0.5157, 0.0353, 0.0014])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.432250738143921 seconds
+
+[40.36, 39.67, 39.74, 39.69, 39.75, 39.62, 40.16, 41.41, 40.17, 40.09]
+[115.1]
+13.35277509689331
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 99857, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.432250738143921, 'TIME_S_1KI': 0.10447190220158749, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1536.9044136524199, 'W': 115.09999999999998}
+[40.36, 39.67, 39.74, 39.69, 39.75, 39.62, 40.16, 41.41, 40.17, 40.09, 40.33, 39.61, 39.56, 41.58, 39.51, 39.51, 39.84, 39.34, 39.38, 39.82]
+718.8400000000001
+35.94200000000001
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 99857, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.432250738143921, 'TIME_S_1KI': 0.10447190220158749, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1536.9044136524199, 'W': 115.09999999999998, 'J_1KI': 15.391053342804408, 'W_1KI': 1.152648287050482, 'W_D': 79.15799999999997, 'J_D': 1056.9789711198803, 'W_D_1KI': 0.7927135804200004, 'J_D_1KI': 0.007938487841813797}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..80ab5da
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 81276, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.078009605407715, "TIME_S_1KI": 0.12399736214144047, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1722.4750410318375, "W": 132.75, "J_1KI": 21.192911081153568, "W_1KI": 1.6333234903292484, "W_D": 96.411, "J_D": 1250.9645286698342, "W_D_1KI": 1.1862173335301935, "J_D_1KI": 0.014594927574317062}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..0f8dc83
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.04085874557495117}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      9,  ..., 499987, 499993,
+                            500000]),
+       col_indices=tensor([ 4658, 51132, 55767,  ..., 77897, 84680, 91168]),
+       values=tensor([0.8716, 0.7460, 0.9968,  ..., 0.7762, 0.8585, 0.9878]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.7678, 0.5187, 0.4774,  ..., 0.8664, 0.3724, 0.0254])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 0.04085874557495117 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '25698', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.3198819160461426}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      4,  ..., 499992, 499998,
+                            500000]),
+       col_indices=tensor([33478, 35089, 63624,  ..., 93258,  3464, 77760]),
+       values=tensor([0.8303, 0.5286, 0.9064,  ..., 0.8655, 0.5788, 0.5903]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.0892, 0.6340, 0.1475,  ..., 0.5230, 0.0009, 0.8265])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 3.3198819160461426 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '81276', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.078009605407715}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      9,  ..., 499997, 500000,
+                            500000]),
+       col_indices=tensor([38450, 44227, 69625,  ...,  8507, 39094, 82179]),
+       values=tensor([0.2677, 0.9845, 0.1042,  ..., 0.9974, 0.0756, 0.3422]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.8400, 0.1962, 0.3075,  ..., 0.6034, 0.5737, 0.0994])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.078009605407715 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      9,  ..., 499997, 500000,
+                            500000]),
+       col_indices=tensor([38450, 44227, 69625,  ...,  8507, 39094, 82179]),
+       values=tensor([0.2677, 0.9845, 0.1042,  ..., 0.9974, 0.0756, 0.3422]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.8400, 0.1962, 0.3075,  ..., 0.6034, 0.5737, 0.0994])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.078009605407715 seconds
+
+[41.19, 39.74, 39.62, 40.23, 39.69, 39.5, 44.76, 39.57, 39.56, 39.98]
+[132.75]
+12.975329875946045
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 81276, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.078009605407715, 'TIME_S_1KI': 0.12399736214144047, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1722.4750410318375, 'W': 132.75}
+[41.19, 39.74, 39.62, 40.23, 39.69, 39.5, 44.76, 39.57, 39.56, 39.98, 40.29, 39.57, 39.62, 45.06, 39.69, 39.48, 40.04, 39.95, 40.05, 39.84]
+726.78
+36.339
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 81276, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.078009605407715, 'TIME_S_1KI': 0.12399736214144047, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1722.4750410318375, 'W': 132.75, 'J_1KI': 21.192911081153568, 'W_1KI': 1.6333234903292484, 'W_D': 96.411, 'J_D': 1250.9645286698342, 'W_D_1KI': 1.1862173335301935, 'J_D_1KI': 0.014594927574317062}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..0d37837
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 280711, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.449347734451294, "TIME_S_1KI": 0.03722457521953644, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1289.5745413303375, "W": 98.5, "J_1KI": 4.593957989998032, "W_1KI": 0.35089469240606885, "W_D": 62.83475, "J_D": 822.6405473183394, "W_D_1KI": 0.22384142409809377, "J_D_1KI": 0.0007974088086968226}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..d302c45
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.019724130630493164}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([ 730, 4220, 7544,  ..., 4458, 7562, 5619]),
+       values=tensor([0.0181, 0.7832, 0.5914,  ..., 0.2469, 0.2734, 0.2796]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.6994, 0.7339, 0.7582,  ..., 0.9456, 0.1186, 0.3856])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.019724130630493164 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '53234', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.991217851638794}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     5,     5,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([2031, 5960, 7493,  ..., 3747, 8534, 6060]),
+       values=tensor([0.1847, 0.1000, 0.1920,  ..., 0.9911, 0.4392, 0.2330]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.7239, 0.0636, 0.4781,  ..., 0.2276, 0.2279, 0.8613])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 1.991217851638794 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '280711', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.449347734451294}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([8732,   42, 2512,  ..., 1373, 9550, 9690]),
+       values=tensor([0.4706, 0.1126, 0.6045,  ..., 0.0102, 0.1178, 0.6557]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.4976, 0.6299, 0.3127,  ..., 0.9623, 0.9434, 0.7070])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.449347734451294 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([8732,   42, 2512,  ..., 1373, 9550, 9690]),
+       values=tensor([0.4706, 0.1126, 0.6045,  ..., 0.0102, 0.1178, 0.6557]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.4976, 0.6299, 0.3127,  ..., 0.9623, 0.9434, 0.7070])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.449347734451294 seconds
+
+[40.09, 39.2, 39.41, 39.39, 39.38, 42.02, 40.51, 39.25, 39.25, 39.51]
+[98.5]
+13.092127323150635
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 280711, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.449347734451294, 'TIME_S_1KI': 0.03722457521953644, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1289.5745413303375, 'W': 98.5}
+[40.09, 39.2, 39.41, 39.39, 39.38, 42.02, 40.51, 39.25, 39.25, 39.51, 40.09, 39.17, 39.95, 40.04, 39.24, 39.23, 39.51, 39.16, 39.2, 39.1]
+713.3050000000001
+35.66525
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 280711, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.449347734451294, 'TIME_S_1KI': 0.03722457521953644, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1289.5745413303375, 'W': 98.5, 'J_1KI': 4.593957989998032, 'W_1KI': 0.35089469240606885, 'W_D': 62.83475, 'J_D': 822.6405473183394, 'W_D_1KI': 0.22384142409809377, 'J_D_1KI': 0.0007974088086968226}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..9665b3b
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 193546, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.657205820083618, "TIME_S_1KI": 0.05506290917964524, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1424.268603067398, "W": 108.67, "J_1KI": 7.358811874528009, "W_1KI": 0.5614685914459612, "W_D": 72.91375, "J_D": 955.6341663467883, "W_D_1KI": 0.3767256879501513, "J_D_1KI": 0.001946440060503195}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..77b5a62
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.025844097137451172}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     12,     28,  ...,  99969,  99983,
+                            100000]),
+       col_indices=tensor([1079, 2122, 3254,  ..., 9373, 9823, 9958]),
+       values=tensor([0.1589, 0.8596, 0.7837,  ..., 0.1493, 0.1272, 0.2084]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0719, 0.4122, 0.7875,  ..., 0.0407, 0.8322, 0.6511])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.025844097137451172 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '40628', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.4707634449005127}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     12,     18,  ...,  99974,  99987,
+                            100000]),
+       col_indices=tensor([ 792, 1032, 1238,  ..., 8561, 8731, 9370]),
+       values=tensor([0.4488, 0.9659, 0.1268,  ..., 0.7863, 0.6709, 0.3638]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8213, 0.7389, 0.9585,  ..., 0.8858, 0.0787, 0.3979])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 2.4707634449005127 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '172656', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.366683721542358}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     10,     24,  ...,  99973,  99986,
+                            100000]),
+       col_indices=tensor([ 684, 3301, 3344,  ..., 8499, 8709, 9229]),
+       values=tensor([0.0104, 0.6771, 0.5927,  ..., 0.6883, 0.2524, 0.4550]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4786, 0.6837, 0.1379,  ..., 0.3005, 0.2266, 0.1673])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 9.366683721542358 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '193546', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.657205820083618}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     13,     24,  ...,  99982,  99990,
+                            100000]),
+       col_indices=tensor([ 667,  823, 2535,  ..., 7218, 8112, 8309]),
+       values=tensor([0.9044, 0.9079, 0.6825,  ..., 0.1587, 0.6143, 0.0618]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5914, 0.6686, 0.5823,  ..., 0.5362, 0.3609, 0.2297])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.657205820083618 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     13,     24,  ...,  99982,  99990,
+                            100000]),
+       col_indices=tensor([ 667,  823, 2535,  ..., 7218, 8112, 8309]),
+       values=tensor([0.9044, 0.9079, 0.6825,  ..., 0.1587, 0.6143, 0.0618]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5914, 0.6686, 0.5823,  ..., 0.5362, 0.3609, 0.2297])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.657205820083618 seconds
+
+[40.9, 39.63, 39.53, 39.38, 39.44, 39.45, 39.92, 39.64, 39.85, 39.99]
+[108.67]
+13.106364250183105
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 193546, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.657205820083618, 'TIME_S_1KI': 0.05506290917964524, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1424.268603067398, 'W': 108.67}
+[40.9, 39.63, 39.53, 39.38, 39.44, 39.45, 39.92, 39.64, 39.85, 39.99, 39.96, 39.48, 39.45, 39.41, 39.63, 39.38, 40.94, 39.79, 39.91, 39.74]
+715.125
+35.75625
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 193546, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.657205820083618, 'TIME_S_1KI': 0.05506290917964524, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1424.268603067398, 'W': 108.67, 'J_1KI': 7.358811874528009, 'W_1KI': 0.5614685914459612, 'W_D': 72.91375, 'J_D': 955.6341663467883, 'W_D_1KI': 0.3767256879501513, 'J_D_1KI': 0.001946440060503195}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..4c6ec73
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 102691, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.217256307601929, "TIME_S_1KI": 0.09949514862648069, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1713.9247414016722, "W": 132.92, "J_1KI": 16.690116382172462, "W_1KI": 1.294368542520766, "W_D": 96.75025, "J_D": 1247.5372194688318, "W_D_1KI": 0.9421492633239523, "J_D_1KI": 0.009174604038561825}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..7818ee2
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.027860403060913086}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      86,     184,  ...,  999787,
+                             999901, 1000000]),
+       col_indices=tensor([  81,   93,  211,  ..., 9891, 9936, 9983]),
+       values=tensor([0.0273, 0.9948, 0.2764,  ..., 0.0318, 0.5538, 0.8532]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8459, 0.7440, 0.9932,  ..., 0.5464, 0.7654, 0.2266])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.027860403060913086 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '37687', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.853411912918091}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      90,     178,  ...,  999807,
+                             999899, 1000000]),
+       col_indices=tensor([   9,   87,  435,  ..., 9776, 9821, 9947]),
+       values=tensor([0.6051, 0.3509, 0.6551,  ..., 0.3060, 0.1178, 0.2325]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6802, 0.0969, 0.8232,  ..., 0.8757, 0.6573, 0.4893])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 3.853411912918091 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '102691', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.217256307601929}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      98,     191,  ...,  999800,
+                             999904, 1000000]),
+       col_indices=tensor([  18,   19,   89,  ..., 9675, 9719, 9959]),
+       values=tensor([0.5811, 0.2000, 0.4195,  ..., 0.8918, 0.7545, 0.5786]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.3032, 0.6522, 0.8844,  ..., 0.7793, 0.6874, 0.5546])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.217256307601929 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      98,     191,  ...,  999800,
+                             999904, 1000000]),
+       col_indices=tensor([  18,   19,   89,  ..., 9675, 9719, 9959]),
+       values=tensor([0.5811, 0.2000, 0.4195,  ..., 0.8918, 0.7545, 0.5786]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.3032, 0.6522, 0.8844,  ..., 0.7793, 0.6874, 0.5546])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.217256307601929 seconds
+
+[40.93, 39.91, 40.43, 39.4, 39.51, 40.07, 39.51, 39.35, 39.39, 44.7]
+[132.92]
+12.894408226013184
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102691, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.217256307601929, 'TIME_S_1KI': 0.09949514862648069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1713.9247414016722, 'W': 132.92}
+[40.93, 39.91, 40.43, 39.4, 39.51, 40.07, 39.51, 39.35, 39.39, 44.7, 40.04, 39.9, 39.44, 39.42, 39.81, 39.87, 45.14, 39.82, 39.78, 39.62]
+723.395
+36.16975
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102691, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.217256307601929, 'TIME_S_1KI': 0.09949514862648069, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1713.9247414016722, 'W': 132.92, 'J_1KI': 16.690116382172462, 'W_1KI': 1.294368542520766, 'W_D': 96.75025, 'J_D': 1247.5372194688318, 'W_D_1KI': 0.9421492633239523, 'J_D_1KI': 0.009174604038561825}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..52cad91
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 27775, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.346900463104248, "TIME_S_1KI": 0.3725256692386768, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2138.1740951538086, "W": 151.25, "J_1KI": 76.98196562209931, "W_1KI": 5.445544554455445, "W_D": 115.164, "J_D": 1628.037563598633, "W_D_1KI": 4.146318631863187, "J_D_1KI": 0.1492823989869734}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..19caf4c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.07912850379943848}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     485,     973,  ..., 4998984,
+                            4999512, 5000000]),
+       col_indices=tensor([  23,   33,   35,  ..., 9878, 9920, 9946]),
+       values=tensor([0.8956, 0.5440, 0.5650,  ..., 0.6571, 0.0981, 0.4530]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.3320, 0.0557, 0.6993,  ..., 0.8374, 0.3528, 0.6849])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 0.07912850379943848 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '13269', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 5.016182899475098}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     490,     975,  ..., 4999017,
+                            4999511, 5000000]),
+       col_indices=tensor([   5,    7,   17,  ..., 9925, 9927, 9956]),
+       values=tensor([0.3061, 0.0982, 0.7519,  ..., 0.4711, 0.1343, 0.2753]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.4300, 0.5593, 0.7816,  ..., 0.7590, 0.1985, 0.5681])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 5.016182899475098 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27775', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.346900463104248}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     516,     985,  ..., 4998986,
+                            4999503, 5000000]),
+       col_indices=tensor([   0,   38,   62,  ..., 9969, 9984, 9993]),
+       values=tensor([0.4538, 0.1922, 0.3497,  ..., 0.8541, 0.7038, 0.0561]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.3516, 0.9610, 0.6827,  ..., 0.5287, 0.4040, 0.0575])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.346900463104248 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     516,     985,  ..., 4998986,
+                            4999503, 5000000]),
+       col_indices=tensor([   0,   38,   62,  ..., 9969, 9984, 9993]),
+       values=tensor([0.4538, 0.1922, 0.3497,  ..., 0.8541, 0.7038, 0.0561]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.3516, 0.9610, 0.6827,  ..., 0.5287, 0.4040, 0.0575])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.346900463104248 seconds
+
+[41.25, 39.91, 39.68, 39.83, 39.58, 41.43, 40.59, 39.78, 40.26, 39.69]
+[151.25]
+14.136688232421875
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27775, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.346900463104248, 'TIME_S_1KI': 0.3725256692386768, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2138.1740951538086, 'W': 151.25}
+[41.25, 39.91, 39.68, 39.83, 39.58, 41.43, 40.59, 39.78, 40.26, 39.69, 40.82, 40.72, 40.11, 39.71, 39.6, 39.85, 39.65, 39.99, 39.77, 40.76]
+721.72
+36.086
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27775, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.346900463104248, 'TIME_S_1KI': 0.3725256692386768, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2138.1740951538086, 'W': 151.25, 'J_1KI': 76.98196562209931, 'W_1KI': 5.445544554455445, 'W_D': 115.164, 'J_D': 1628.037563598633, 'W_D_1KI': 4.146318631863187, 'J_D_1KI': 0.1492823989869734}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..6336d4d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4427, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.78234076499939, "TIME_S_1KI": 2.4355863485428935, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1855.6078462219239, "W": 121.94, "J_1KI": 419.15695645401485, "W_1KI": 27.544612604472555, "W_D": 85.48649999999999, "J_D": 1300.8809262428283, "W_D_1KI": 19.310255251863563, "J_D_1KI": 4.361927999065633}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..68c803b
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.23713254928588867}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      967,     1927,  ...,  9997983,
+                             9998974, 10000000]),
+       col_indices=tensor([   2,    7,   17,  ..., 9977, 9981, 9986]),
+       values=tensor([0.0113, 0.4578, 0.3712,  ..., 0.8300, 0.4518, 0.5288]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6464, 0.5946, 0.9135,  ..., 0.7384, 0.8851, 0.3138])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 0.23713254928588867 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4427', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.78234076499939}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1000,     1968,  ...,  9997976,
+                             9998957, 10000000]),
+       col_indices=tensor([  18,   35,   37,  ..., 9972, 9974, 9993]),
+       values=tensor([0.5495, 0.5155, 0.6909,  ..., 0.5748, 0.2988, 0.6189]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2327, 0.3005, 0.5005,  ..., 0.5867, 0.2890, 0.0524])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.78234076499939 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1000,     1968,  ...,  9997976,
+                             9998957, 10000000]),
+       col_indices=tensor([  18,   35,   37,  ..., 9972, 9974, 9993]),
+       values=tensor([0.5495, 0.5155, 0.6909,  ..., 0.5748, 0.2988, 0.6189]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2327, 0.3005, 0.5005,  ..., 0.5867, 0.2890, 0.0524])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.78234076499939 seconds
+
+[40.15, 39.73, 39.55, 39.93, 39.94, 39.96, 44.96, 39.82, 40.15, 39.43]
+[121.94]
+15.217384338378906
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4427, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.78234076499939, 'TIME_S_1KI': 2.4355863485428935, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1855.6078462219239, 'W': 121.94}
+[40.15, 39.73, 39.55, 39.93, 39.94, 39.96, 44.96, 39.82, 40.15, 39.43, 40.99, 45.07, 39.68, 40.13, 40.01, 39.56, 39.72, 40.75, 40.01, 39.63]
+729.07
+36.453500000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4427, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.78234076499939, 'TIME_S_1KI': 2.4355863485428935, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1855.6078462219239, 'W': 121.94, 'J_1KI': 419.15695645401485, 'W_1KI': 27.544612604472555, 'W_D': 85.48649999999999, 'J_D': 1300.8809262428283, 'W_D_1KI': 19.310255251863563, 'J_D_1KI': 4.361927999065633}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..3673122
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2210, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.475984573364258, "TIME_S_1KI": 4.740264512834506, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2065.6901198005676, "W": 119.44, "J_1KI": 934.701411674465, "W_1KI": 54.04524886877828, "W_D": 83.048, "J_D": 1436.2979995746614, "W_D_1KI": 37.57828054298643, "J_D_1KI": 17.00374685203006}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..5e9953a
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.5103092193603516}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2001,     3993,  ..., 19996027,
+                            19997998, 20000000]),
+       col_indices=tensor([   4,    8,   12,  ..., 9988, 9991, 9998]),
+       values=tensor([0.1397, 0.5991, 0.8904,  ..., 0.1163, 0.3047, 0.7503]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.7325, 0.0863, 0.4494,  ..., 0.5445, 0.3494, 0.7015])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 0.5103092193603516 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2057', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.769307136535645}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1965,     3996,  ..., 19995929,
+                            19997992, 20000000]),
+       col_indices=tensor([   4,    9,   15,  ..., 9975, 9986, 9992]),
+       values=tensor([0.0708, 0.7889, 0.9973,  ..., 0.4384, 0.2830, 0.3299]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.8359, 0.1884, 0.2769,  ..., 0.8252, 0.8191, 0.5472])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 9.769307136535645 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2210', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.475984573364258}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2066,     4070,  ..., 19995990,
+                            19998002, 20000000]),
+       col_indices=tensor([   1,    2,    8,  ..., 9986, 9990, 9993]),
+       values=tensor([0.6258, 0.8376, 0.0180,  ..., 0.7990, 0.4511, 0.0511]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.7373, 0.4078, 0.5568,  ..., 0.6016, 0.2858, 0.4434])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.475984573364258 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2066,     4070,  ..., 19995990,
+                            19998002, 20000000]),
+       col_indices=tensor([   1,    2,    8,  ..., 9986, 9990, 9993]),
+       values=tensor([0.6258, 0.8376, 0.0180,  ..., 0.7990, 0.4511, 0.0511]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.7373, 0.4078, 0.5568,  ..., 0.6016, 0.2858, 0.4434])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.475984573364258 seconds
+
+[40.41, 45.21, 40.37, 40.41, 40.26, 39.65, 40.47, 41.87, 39.92, 39.65]
+[119.44]
+17.294793367385864
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2210, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.475984573364258, 'TIME_S_1KI': 4.740264512834506, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2065.6901198005676, 'W': 119.44}
+[40.41, 45.21, 40.37, 40.41, 40.26, 39.65, 40.47, 41.87, 39.92, 39.65, 40.39, 39.85, 39.69, 40.06, 40.15, 39.58, 40.59, 39.58, 39.95, 40.01]
+727.8399999999999
+36.391999999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2210, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.475984573364258, 'TIME_S_1KI': 4.740264512834506, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2065.6901198005676, 'W': 119.44, 'J_1KI': 934.701411674465, 'W_1KI': 54.04524886877828, 'W_D': 83.048, 'J_D': 1436.2979995746614, 'W_D_1KI': 37.57828054298643, 'J_D_1KI': 17.00374685203006}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..21b0219
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1434, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.430570602416992, "TIME_S_1KI": 7.273759136971403, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2171.006755914688, "W": 115.95999999999998, "J_1KI": 1513.9517126322787, "W_1KI": 80.8647140864714, "W_D": 79.27299999999998, "J_D": 1484.1515915973182, "W_D_1KI": 55.281032078103195, "J_D_1KI": 38.55023157468842}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..29bb118
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.7320935726165771}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2976,     5887,  ..., 29993981,
+                            29996974, 30000000]),
+       col_indices=tensor([   2,   12,   13,  ..., 9995, 9997, 9999]),
+       values=tensor([0.2872, 0.6919, 0.0045,  ..., 0.7234, 0.8152, 0.1470]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.3759, 0.5048, 0.7452,  ..., 0.9323, 0.0206, 0.6020])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 0.7320935726165771 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1434', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.430570602416992}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2946,     5856,  ..., 29993956,
+                            29997054, 30000000]),
+       col_indices=tensor([   1,    3,   10,  ..., 9992, 9994, 9995]),
+       values=tensor([0.6658, 0.8893, 0.2640,  ..., 0.2436, 0.9944, 0.7745]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.7478, 0.4417, 0.0487,  ..., 0.7713, 0.8445, 0.5646])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.430570602416992 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2946,     5856,  ..., 29993956,
+                            29997054, 30000000]),
+       col_indices=tensor([   1,    3,   10,  ..., 9992, 9994, 9995]),
+       values=tensor([0.6658, 0.8893, 0.2640,  ..., 0.2436, 0.9944, 0.7745]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.7478, 0.4417, 0.0487,  ..., 0.7713, 0.8445, 0.5646])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.430570602416992 seconds
+
+[40.39, 40.27, 40.31, 40.01, 39.78, 40.12, 45.36, 39.94, 40.02, 52.21]
+[115.96]
+18.722031354904175
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1434, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.430570602416992, 'TIME_S_1KI': 7.273759136971403, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2171.006755914688, 'W': 115.95999999999998}
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+733.74
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+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1434, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.430570602416992, 'TIME_S_1KI': 7.273759136971403, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2171.006755914688, 'W': 115.95999999999998, 'J_1KI': 1513.9517126322787, 'W_1KI': 80.8647140864714, 'W_D': 79.27299999999998, 'J_D': 1484.1515915973182, 'W_D_1KI': 55.281032078103195, 'J_D_1KI': 38.55023157468842}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..7eea71b
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 349456, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.173964023590088, "TIME_S_1KI": 0.03197531026392475, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1252.9686733055114, "W": 97.63, "J_1KI": 3.585483360724988, "W_1KI": 0.2793770889611282, "W_D": 61.798, "J_D": 793.1061976127625, "W_D_1KI": 0.17684057506524428, "J_D_1KI": 0.0005060453249200021}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..518e71c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1307 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.018783092498779297}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      5.8909e-01, 6.1073e-01, 5.5245e-01, 6.0942e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4323, 0.3291, 0.3785,  ..., 0.2185, 0.3372, 0.4003])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.018783092498779297 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '55901', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.679638147354126}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      4.0420e-01, 7.3745e-01, 2.3749e-01, 2.0239e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.3431, 0.4493, 0.0337,  ..., 0.1419, 0.4440, 0.1516])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 1.679638147354126 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '349456', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.173964023590088}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1000, 1000, 1000]),
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+                      0.6983, 0.4500, 0.7444, 0.9235, 0.5009, 0.2575]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5348, 0.7986, 0.2200,  ..., 0.0453, 0.2085, 0.0080])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 11.173964023590088 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([1074, 7022, 4087, 4354, 1115, 9252, 2574, 3979,  915,
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+                      0.6983, 0.4500, 0.7444, 0.9235, 0.5009, 0.2575]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5348, 0.7986, 0.2200,  ..., 0.0453, 0.2085, 0.0080])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 11.173964023590088 seconds
+
+[40.48, 41.2, 39.7, 39.62, 39.96, 39.55, 39.84, 39.41, 40.04, 39.52]
+[97.63]
+12.83384895324707
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 349456, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.173964023590088, 'TIME_S_1KI': 0.03197531026392475, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1252.9686733055114, 'W': 97.63}
+[40.48, 41.2, 39.7, 39.62, 39.96, 39.55, 39.84, 39.41, 40.04, 39.52, 40.24, 39.6, 39.87, 39.86, 39.87, 39.92, 39.37, 39.31, 39.74, 39.32]
+716.6399999999999
+35.831999999999994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 349456, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.173964023590088, 'TIME_S_1KI': 0.03197531026392475, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1252.9686733055114, 'W': 97.63, 'J_1KI': 3.585483360724988, 'W_1KI': 0.2793770889611282, 'W_D': 61.798, 'J_D': 793.1061976127625, 'W_D_1KI': 0.17684057506524428, 'J_D_1KI': 0.0005060453249200021}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..6ded063
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 308023, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.28289532661438, "TIME_S_1KI": 0.03338353086170311, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1226.4741826629638, "W": 98.08, "J_1KI": 3.9817616952726382, "W_1KI": 0.3184177804904179, "W_D": 61.967, "J_D": 774.8870888772011, "W_D_1KI": 0.20117653551845155, "J_D_1KI": 0.0006531217977828004}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..df70699
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.019933462142944336}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    2,    5,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([4080, 6557, 3158,  ..., 4357, 6307, 2550]),
+       values=tensor([0.9910, 0.3414, 0.4855,  ..., 0.2598, 0.6108, 0.2815]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.2787, 0.7388, 0.8319,  ..., 0.5413, 0.0496, 0.2437])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.019933462142944336 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52675', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.7956035137176514}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([ 408,  476, 3837,  ..., 3097, 8388, 8856]),
+       values=tensor([0.3698, 0.9808, 0.6496,  ..., 0.7839, 0.4021, 0.3346]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.1775, 0.8809, 0.7204,  ..., 0.4994, 0.6943, 0.3851])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 1.7956035137176514 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '308023', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.28289532661438}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([2483, 6584, 3017,  ...,  870, 3138, 2052]),
+       values=tensor([0.7385, 0.7043, 0.9061,  ..., 0.4377, 0.8515, 0.3180]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.1916, 0.9837, 0.2990,  ..., 0.4110, 0.2807, 0.4933])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.28289532661438 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([2483, 6584, 3017,  ...,  870, 3138, 2052]),
+       values=tensor([0.7385, 0.7043, 0.9061,  ..., 0.4377, 0.8515, 0.3180]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.1916, 0.9837, 0.2990,  ..., 0.4110, 0.2807, 0.4933])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.28289532661438 seconds
+
+[40.25, 39.88, 39.44, 44.89, 40.01, 39.55, 39.45, 39.48, 39.91, 41.29]
+[98.08]
+12.504834651947021
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 308023, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.28289532661438, 'TIME_S_1KI': 0.03338353086170311, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1226.4741826629638, 'W': 98.08}
+[40.25, 39.88, 39.44, 44.89, 40.01, 39.55, 39.45, 39.48, 39.91, 41.29, 44.96, 39.78, 39.79, 40.18, 39.36, 39.46, 39.39, 39.37, 39.43, 39.28]
+722.26
+36.113
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 308023, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.28289532661438, 'TIME_S_1KI': 0.03338353086170311, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1226.4741826629638, 'W': 98.08, 'J_1KI': 3.9817616952726382, 'W_1KI': 0.3184177804904179, 'W_D': 61.967, 'J_D': 774.8870888772011, 'W_D_1KI': 0.20117653551845155, 'J_D_1KI': 0.0006531217977828004}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..f68b6b9
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1275, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.370937824249268, "TIME_S_1KI": 8.134068881764131, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2317.954887828827, "W": 118.94, "J_1KI": 1818.0038335912368, "W_1KI": 93.28627450980392, "W_D": 82.976, "J_D": 1617.072681793213, "W_D_1KI": 65.07921568627451, "J_D_1KI": 51.04252210688197}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..8fe4739
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.8229217529296875}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       48,       92,  ..., 24999906,
+                            24999954, 25000000]),
+       col_indices=tensor([ 13687,  16103,  22085,  ..., 466250, 497468,
+                           498839]),
+       values=tensor([0.1763, 0.0612, 0.1831,  ..., 0.7206, 0.9735, 0.4201]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.0392, 0.3068, 0.8540,  ..., 0.0771, 0.2433, 0.8939])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 0.8229217529296875 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1275', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.370937824249268}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       45,       89,  ..., 24999893,
+                            24999957, 25000000]),
+       col_indices=tensor([ 25264,  35882,  38786,  ..., 487781, 491680,
+                           492236]),
+       values=tensor([0.0901, 0.4292, 0.0295,  ..., 0.7641, 0.5758, 0.3435]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.7878, 0.6485, 0.9023,  ..., 0.5055, 0.2764, 0.4227])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.370937824249268 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       45,       89,  ..., 24999893,
+                            24999957, 25000000]),
+       col_indices=tensor([ 25264,  35882,  38786,  ..., 487781, 491680,
+                           492236]),
+       values=tensor([0.0901, 0.4292, 0.0295,  ..., 0.7641, 0.5758, 0.3435]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.7878, 0.6485, 0.9023,  ..., 0.5055, 0.2764, 0.4227])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.370937824249268 seconds
+
+[40.75, 40.02, 39.59, 39.62, 39.74, 39.84, 39.97, 39.74, 40.73, 39.5]
+[118.94]
+19.488438606262207
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1275, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.370937824249268, 'TIME_S_1KI': 8.134068881764131, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2317.954887828827, 'W': 118.94}
+[40.75, 40.02, 39.59, 39.62, 39.74, 39.84, 39.97, 39.74, 40.73, 39.5, 41.45, 40.23, 40.12, 39.57, 39.77, 39.94, 39.75, 39.69, 40.29, 39.64]
+719.28
+35.964
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1275, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.370937824249268, 'TIME_S_1KI': 8.134068881764131, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2317.954887828827, 'W': 118.94, 'J_1KI': 1818.0038335912368, 'W_1KI': 93.28627450980392, 'W_D': 82.976, 'J_D': 1617.072681793213, 'W_D_1KI': 65.07921568627451, 'J_D_1KI': 51.04252210688197}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..24823a1
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 20602, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.051029920578003, "TIME_S_1KI": 0.4878667081146492, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2012.3513662075998, "W": 152.11, "J_1KI": 97.67747627451702, "W_1KI": 7.383263760799923, "W_D": 115.75800000000001, "J_D": 1531.429685421467, "W_D_1KI": 5.61877487622561, "J_D_1KI": 0.272729583352374}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..60d167b
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08110809326171875}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,       8,  ..., 2499988,
+                            2499994, 2500000]),
+       col_indices=tensor([ 61750, 191731, 192878,  ..., 292292, 347392,
+                           413452]),
+       values=tensor([0.4333, 0.7749, 0.6975,  ..., 0.5571, 0.2303, 0.6423]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7573, 0.7811, 0.2609,  ..., 0.7028, 0.0683, 0.1077])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 0.08110809326171875 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12945', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.597289562225342}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       9,  ..., 2499996,
+                            2499997, 2500000]),
+       col_indices=tensor([304373, 374974, 396567,  ..., 161828, 243938,
+                           306700]),
+       values=tensor([0.0234, 0.0111, 0.7752,  ..., 0.4123, 0.0911, 0.7333]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.2563, 0.2400, 0.1997,  ..., 0.9331, 0.1838, 0.9541])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 6.597289562225342 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '20602', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.051029920578003}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       7,  ..., 2499995,
+                            2499998, 2500000]),
+       col_indices=tensor([ 84683, 221772, 250792,  ..., 457280, 123381,
+                           490345]),
+       values=tensor([0.6671, 0.6498, 0.8275,  ..., 0.5282, 0.6912, 0.3058]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8099, 0.6830, 0.6662,  ..., 0.4435, 0.6731, 0.4595])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.051029920578003 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       7,  ..., 2499995,
+                            2499998, 2500000]),
+       col_indices=tensor([ 84683, 221772, 250792,  ..., 457280, 123381,
+                           490345]),
+       values=tensor([0.6671, 0.6498, 0.8275,  ..., 0.5282, 0.6912, 0.3058]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8099, 0.6830, 0.6662,  ..., 0.4435, 0.6731, 0.4595])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.051029920578003 seconds
+
+[40.55, 45.24, 39.91, 39.85, 39.84, 39.77, 40.43, 40.43, 42.0, 39.73]
+[152.11]
+13.22957968711853
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 20602, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.051029920578003, 'TIME_S_1KI': 0.4878667081146492, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2012.3513662075998, 'W': 152.11}
+[40.55, 45.24, 39.91, 39.85, 39.84, 39.77, 40.43, 40.43, 42.0, 39.73, 40.67, 40.21, 39.79, 39.83, 39.69, 40.04, 39.86, 39.69, 40.08, 39.81]
+727.04
+36.352
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 20602, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.051029920578003, 'TIME_S_1KI': 0.4878667081146492, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2012.3513662075998, 'W': 152.11, 'J_1KI': 97.67747627451702, 'W_1KI': 7.383263760799923, 'W_D': 115.75800000000001, 'J_D': 1531.429685421467, 'W_D_1KI': 5.61877487622561, 'J_D_1KI': 0.272729583352374}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..ac74c89
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2268, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.338330507278442, "TIME_S_1KI": 4.558346784514304, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1929.4526886749268, "W": 124.72, "J_1KI": 850.7286987102852, "W_1KI": 54.99118165784832, "W_D": 88.6155, "J_D": 1370.9061476368904, "W_D_1KI": 39.07208994708994, "J_D_1KI": 17.227552886723963}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..4f74cbe
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.46280455589294434}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       21,       39,  ..., 12499960,
+                            12499981, 12500000]),
+       col_indices=tensor([  5530,  18658,  36900,  ..., 388989, 426254,
+                           497258]),
+       values=tensor([0.8053, 0.3880, 0.4779,  ..., 0.4773, 0.4279, 0.6817]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.5886, 0.4606, 0.7255,  ..., 0.1606, 0.2608, 0.5232])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 0.46280455589294434 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2268', '-ss', '500000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.338330507278442}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       30,       63,  ..., 12499957,
+                            12499979, 12500000]),
+       col_indices=tensor([ 14790,  16334,  55074,  ..., 466420, 486794,
+                           499923]),
+       values=tensor([0.8543, 0.1686, 0.8292,  ..., 0.6567, 0.2357, 0.6950]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.8639, 0.3423, 0.4800,  ..., 0.1443, 0.7816, 0.0060])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.338330507278442 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       30,       63,  ..., 12499957,
+                            12499979, 12500000]),
+       col_indices=tensor([ 14790,  16334,  55074,  ..., 466420, 486794,
+                           499923]),
+       values=tensor([0.8543, 0.1686, 0.8292,  ..., 0.6567, 0.2357, 0.6950]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.8639, 0.3423, 0.4800,  ..., 0.1443, 0.7816, 0.0060])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.338330507278442 seconds
+
+[40.4, 42.56, 40.55, 40.15, 39.72, 40.38, 39.75, 39.66, 39.85, 39.57]
+[124.72]
+15.470274925231934
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2268, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.338330507278442, 'TIME_S_1KI': 4.558346784514304, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1929.4526886749268, 'W': 124.72}
+[40.4, 42.56, 40.55, 40.15, 39.72, 40.38, 39.75, 39.66, 39.85, 39.57, 40.6, 40.35, 40.27, 40.13, 39.75, 39.68, 39.73, 39.6, 39.89, 39.57]
+722.09
+36.1045
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2268, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.338330507278442, 'TIME_S_1KI': 4.558346784514304, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1929.4526886749268, 'W': 124.72, 'J_1KI': 850.7286987102852, 'W_1KI': 54.99118165784832, 'W_D': 88.6155, 'J_D': 1370.9061476368904, 'W_D_1KI': 39.07208994708994, 'J_D_1KI': 17.227552886723963}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..366bd80
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 89538, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.289806604385376, "TIME_S_1KI": 0.11492111287258344, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1521.4007213592529, "W": 116.96, "J_1KI": 16.991676398392332, "W_1KI": 1.3062610288369183, "W_D": 80.71074999999999, "J_D": 1049.8751134699583, "W_D_1KI": 0.9014133663919228, "J_D_1KI": 0.010067383305322017}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..aedb6df
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.049301862716674805}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,     13,  ..., 249993, 249996,
+                            250000]),
+       col_indices=tensor([ 1709, 19790, 28830,  ...,  3831, 22257, 48856]),
+       values=tensor([0.9244, 0.7522, 0.6687,  ..., 0.7540, 0.7318, 0.7260]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0785, 0.8938, 0.5541,  ..., 0.5935, 0.2052, 0.2232])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.049301862716674805 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21297', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.7824935913085938}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      8,  ..., 249989, 249993,
+                            250000]),
+       col_indices=tensor([16415, 16632, 32449,  ..., 45169, 45288, 48610]),
+       values=tensor([0.0101, 0.6954, 0.6241,  ..., 0.3711, 0.7246, 0.3748]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6515, 0.7514, 0.0204,  ..., 0.8861, 0.6124, 0.4798])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 2.7824935913085938 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '80366', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.42440915107727}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     10,  ..., 249987, 249993,
+                            250000]),
+       col_indices=tensor([ 2445, 24855, 26173,  ..., 23560, 26333, 46130]),
+       values=tensor([0.2012, 0.2713, 0.8391,  ..., 0.5844, 0.7972, 0.4463]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5580, 0.1767, 0.6905,  ..., 0.9860, 0.6709, 0.2165])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 9.42440915107727 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '89538', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.289806604385376}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 249988, 249996,
+                            250000]),
+       col_indices=tensor([ 2244, 34732,  7243,  ...,  9132, 13610, 19520]),
+       values=tensor([0.6983, 0.0446, 0.9216,  ..., 0.0232, 0.0374, 0.6300]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8539, 0.6321, 0.4259,  ..., 0.2899, 0.6274, 0.3350])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.289806604385376 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 249988, 249996,
+                            250000]),
+       col_indices=tensor([ 2244, 34732,  7243,  ...,  9132, 13610, 19520]),
+       values=tensor([0.6983, 0.0446, 0.9216,  ..., 0.0232, 0.0374, 0.6300]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8539, 0.6321, 0.4259,  ..., 0.2899, 0.6274, 0.3350])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.289806604385376 seconds
+
+[40.27, 39.54, 39.5, 39.65, 39.93, 39.44, 40.01, 39.51, 45.05, 39.37]
+[116.96]
+13.007872104644775
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 89538, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.289806604385376, 'TIME_S_1KI': 0.11492111287258344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1521.4007213592529, 'W': 116.96}
+[40.27, 39.54, 39.5, 39.65, 39.93, 39.44, 40.01, 39.51, 45.05, 39.37, 40.45, 39.41, 39.86, 39.36, 39.91, 44.54, 39.53, 39.97, 39.84, 39.78]
+724.985
+36.24925
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 89538, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.289806604385376, 'TIME_S_1KI': 0.11492111287258344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1521.4007213592529, 'W': 116.96, 'J_1KI': 16.991676398392332, 'W_1KI': 1.3062610288369183, 'W_D': 80.71074999999999, 'J_D': 1049.8751134699583, 'W_D_1KI': 0.9014133663919228, 'J_D_1KI': 0.010067383305322017}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..16ebf8c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 45908, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.364075660705566, "TIME_S_1KI": 0.22575750763931268, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1982.6904290771486, "W": 146.58, "J_1KI": 43.18834253457238, "W_1KI": 3.192907554238913, "W_D": 110.64150000000001, "J_D": 1496.5741786651613, "W_D_1KI": 2.410070140280561, "J_D_1KI": 0.05249782478610615}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..f9b44d5
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.0605926513671875}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      42,      99,  ..., 2499902,
+                            2499955, 2500000]),
+       col_indices=tensor([ 1009,  1628,  5292,  ..., 43455, 47256, 47946]),
+       values=tensor([0.2339, 0.7843, 0.8407,  ..., 0.0388, 0.2390, 0.6904]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3494, 0.3893, 0.8826,  ..., 0.0693, 0.0070, 0.7582])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.0605926513671875 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '17328', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.963207721710205}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      45,      85,  ..., 2499905,
+                            2499952, 2500000]),
+       col_indices=tensor([ 2138,  2192,  2629,  ..., 48532, 49646, 49876]),
+       values=tensor([0.7824, 0.0061, 0.7967,  ..., 0.1635, 0.4732, 0.5157]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8165, 0.7580, 0.0903,  ..., 0.6290, 0.7559, 0.6116])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 3.963207721710205 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '45908', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.364075660705566}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      48,      99,  ..., 2499901,
+                            2499955, 2500000]),
+       col_indices=tensor([ 2242,  2630,  4307,  ..., 47333, 48170, 49131]),
+       values=tensor([0.3970, 0.2919, 0.1690,  ..., 0.5693, 0.6652, 0.4283]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7545, 0.7866, 0.4331,  ..., 0.1722, 0.5406, 0.9467])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.364075660705566 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      48,      99,  ..., 2499901,
+                            2499955, 2500000]),
+       col_indices=tensor([ 2242,  2630,  4307,  ..., 47333, 48170, 49131]),
+       values=tensor([0.3970, 0.2919, 0.1690,  ..., 0.5693, 0.6652, 0.4283]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7545, 0.7866, 0.4331,  ..., 0.1722, 0.5406, 0.9467])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.364075660705566 seconds
+
+[40.59, 39.91, 39.52, 39.54, 39.7, 39.41, 39.47, 40.95, 40.05, 39.39]
+[146.58]
+13.526336669921875
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 45908, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.364075660705566, 'TIME_S_1KI': 0.22575750763931268, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1982.6904290771486, 'W': 146.58}
+[40.59, 39.91, 39.52, 39.54, 39.7, 39.41, 39.47, 40.95, 40.05, 39.39, 40.78, 39.79, 39.69, 41.93, 40.24, 39.57, 39.58, 39.7, 39.52, 39.64]
+718.77
+35.9385
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 45908, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.364075660705566, 'TIME_S_1KI': 0.22575750763931268, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1982.6904290771486, 'W': 146.58, 'J_1KI': 43.18834253457238, 'W_1KI': 3.192907554238913, 'W_D': 110.64150000000001, 'J_D': 1496.5741786651613, 'W_D_1KI': 2.410070140280561, 'J_D_1KI': 0.05249782478610615}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..20ca6ca
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1726, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.692668676376343, "TIME_S_1KI": 6.1950571705540805, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2256.2120828294755, "W": 117.97000000000001, "J_1KI": 1307.1912415002755, "W_1KI": 68.34878331402086, "W_D": 81.57950000000002, "J_D": 1560.2327168872362, "W_D_1KI": 47.26506373117035, "J_D_1KI": 27.384162069044237}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..939a7fb
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.6629180908203125}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      514,      996,  ..., 24998956,
+                            24999472, 25000000]),
+       col_indices=tensor([    1,    94,   348,  ..., 49850, 49922, 49959]),
+       values=tensor([0.7408, 0.6252, 0.3689,  ..., 0.4667, 0.9642, 0.0582]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.9247, 0.2733, 0.8266,  ..., 0.5422, 0.1520, 0.6812])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 0.6629180908203125 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1583', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.627561807632446}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      562,     1043,  ..., 24999032,
+                            24999504, 25000000]),
+       col_indices=tensor([    4,    50,    78,  ..., 49916, 49920, 49965]),
+       values=tensor([0.0759, 0.2514, 0.9400,  ..., 0.2240, 0.9432, 0.3438]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3732, 0.6785, 0.5695,  ..., 0.6003, 0.8169, 0.4003])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 9.627561807632446 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1726', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.692668676376343}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      457,      970,  ..., 24999002,
+                            24999486, 25000000]),
+       col_indices=tensor([  100,   360,   480,  ..., 49859, 49889, 49953]),
+       values=tensor([0.3856, 0.6378, 0.2660,  ..., 0.6784, 0.6537, 0.7029]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.4800, 0.0280, 0.3242,  ..., 0.3544, 0.6298, 0.7207])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.692668676376343 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      457,      970,  ..., 24999002,
+                            24999486, 25000000]),
+       col_indices=tensor([  100,   360,   480,  ..., 49859, 49889, 49953]),
+       values=tensor([0.3856, 0.6378, 0.2660,  ..., 0.6784, 0.6537, 0.7029]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.4800, 0.0280, 0.3242,  ..., 0.3544, 0.6298, 0.7207])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.692668676376343 seconds
+
+[41.47, 39.7, 39.74, 40.1, 40.28, 40.05, 40.37, 39.58, 39.73, 40.01]
+[117.97]
+19.125303745269775
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1726, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.692668676376343, 'TIME_S_1KI': 6.1950571705540805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2256.2120828294755, 'W': 117.97000000000001}
+[41.47, 39.7, 39.74, 40.1, 40.28, 40.05, 40.37, 39.58, 39.73, 40.01, 41.18, 40.13, 45.73, 39.77, 39.88, 40.17, 39.76, 40.02, 41.27, 40.4]
+727.81
+36.390499999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1726, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.692668676376343, 'TIME_S_1KI': 6.1950571705540805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2256.2120828294755, 'W': 117.97000000000001, 'J_1KI': 1307.1912415002755, 'W_1KI': 68.34878331402086, 'W_D': 81.57950000000002, 'J_D': 1560.2327168872362, 'W_D_1KI': 47.26506373117035, 'J_D_1KI': 27.384162069044237}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..53cb9a4
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 125418, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.786596775054932, "TIME_S_1KI": 0.0860051729022543, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1353.9629184293747, "W": 103.38, "J_1KI": 10.795602851499583, "W_1KI": 0.8242835956561259, "W_D": 67.61375, "J_D": 885.5340518084168, "W_D_1KI": 0.5391072254381349, "J_D_1KI": 0.004298483674098893}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..b030af9
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.05780529975891113}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([26335, 27290, 38418,  ..., 19756, 20120,  4010]),
+       values=tensor([0.3389, 0.0656, 0.4529,  ..., 0.8287, 0.8944, 0.8355]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6330, 0.8862, 0.5805,  ..., 0.8180, 0.2124, 0.8337])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.05780529975891113 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18164', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.5206849575042725}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([47070, 16594, 28343,  ..., 43440, 28747, 28655]),
+       values=tensor([0.5955, 0.4100, 0.8378,  ..., 0.8449, 0.3361, 0.6219]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0973, 0.1697, 0.0749,  ..., 0.0145, 0.6554, 0.2719])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 1.5206849575042725 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '125418', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.786596775054932}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([27120, 12941, 15664,  ...,  3161, 41560, 29450]),
+       values=tensor([0.4509, 0.2974, 0.8733,  ..., 0.8770, 0.0483, 0.7990]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0817, 0.8974, 0.0414,  ..., 0.9825, 0.3309, 0.2047])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.786596775054932 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([27120, 12941, 15664,  ...,  3161, 41560, 29450]),
+       values=tensor([0.4509, 0.2974, 0.8733,  ..., 0.8770, 0.0483, 0.7990]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0817, 0.8974, 0.0414,  ..., 0.9825, 0.3309, 0.2047])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.786596775054932 seconds
+
+[40.88, 40.06, 39.87, 40.02, 39.61, 39.65, 39.87, 39.87, 39.51, 39.91]
+[103.38]
+13.096952199935913
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 125418, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.786596775054932, 'TIME_S_1KI': 0.0860051729022543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1353.9629184293747, 'W': 103.38}
+[40.88, 40.06, 39.87, 40.02, 39.61, 39.65, 39.87, 39.87, 39.51, 39.91, 40.56, 39.51, 39.82, 39.66, 39.34, 39.38, 39.55, 39.32, 39.66, 39.9]
+715.325
+35.76625
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 125418, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.786596775054932, 'TIME_S_1KI': 0.0860051729022543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1353.9629184293747, 'W': 103.38, 'J_1KI': 10.795602851499583, 'W_1KI': 0.8242835956561259, 'W_D': 67.61375, 'J_D': 885.5340518084168, 'W_D_1KI': 0.5391072254381349, 'J_D_1KI': 0.004298483674098893}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..d831091
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 106823, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.303539752960205, "TIME_S_1KI": 0.09645431932224527, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1429.2441740632057, "W": 111.25, "J_1KI": 13.379554721953191, "W_1KI": 1.0414423860030144, "W_D": 75.065, "J_D": 964.37046225667, "W_D_1KI": 0.7027044737556518, "J_D_1KI": 0.006578213247668122}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..a27a2d5
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.05483698844909668}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 124996, 124998,
+                            125000]),
+       col_indices=tensor([28119, 29640, 21715,  ..., 29199, 13516, 45728]),
+       values=tensor([0.3782, 0.4368, 0.3959,  ..., 0.8630, 0.5532, 0.4165]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.8607, 0.2103, 0.4385,  ..., 0.0263, 0.3906, 0.3161])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.05483698844909668 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19147', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.8820090293884277}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      6,  ..., 124991, 124992,
+                            125000]),
+       col_indices=tensor([32530, 36762,   311,  ..., 24158, 32618, 44758]),
+       values=tensor([0.9615, 0.3318, 0.5732,  ..., 0.8773, 0.1422, 0.4683]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.1372, 0.3779, 0.3457,  ..., 0.7036, 0.6193, 0.2501])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 1.8820090293884277 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '106823', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.303539752960205}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      5,  ..., 124996, 124998,
+                            125000]),
+       col_indices=tensor([16502, 37527, 11294,  ..., 28497,  8084, 35661]),
+       values=tensor([0.2823, 0.8232, 0.0849,  ..., 0.6885, 0.2665, 0.0851]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.8734, 0.5898, 0.3749,  ..., 0.2817, 0.4056, 0.3872])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.303539752960205 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      5,  ..., 124996, 124998,
+                            125000]),
+       col_indices=tensor([16502, 37527, 11294,  ..., 28497,  8084, 35661]),
+       values=tensor([0.2823, 0.8232, 0.0849,  ..., 0.6885, 0.2665, 0.0851]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.8734, 0.5898, 0.3749,  ..., 0.2817, 0.4056, 0.3872])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.303539752960205 seconds
+
+[40.0, 39.65, 39.5, 39.28, 44.73, 39.57, 39.57, 39.75, 39.32, 39.43]
+[111.25]
+12.84713864326477
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 106823, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.303539752960205, 'TIME_S_1KI': 0.09645431932224527, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1429.2441740632057, 'W': 111.25}
+[40.0, 39.65, 39.5, 39.28, 44.73, 39.57, 39.57, 39.75, 39.32, 39.43, 41.22, 44.93, 39.95, 39.46, 39.81, 39.42, 39.55, 39.68, 39.52, 39.37]
+723.7
+36.185
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 106823, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.303539752960205, 'TIME_S_1KI': 0.09645431932224527, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1429.2441740632057, 'W': 111.25, 'J_1KI': 13.379554721953191, 'W_1KI': 1.0414423860030144, 'W_D': 75.065, 'J_D': 964.37046225667, 'W_D_1KI': 0.7027044737556518, 'J_D_1KI': 0.006578213247668122}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..59bfbf5
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 423606, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.169308185577393, "TIME_S_1KI": 0.024006525369275677, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1199.050221145153, "W": 95.75, "J_1KI": 2.8305789369016328, "W_1KI": 0.2260355141334164, "W_D": 60.24975, "J_D": 754.4906116077303, "W_D_1KI": 0.14223063412699538, "J_D_1KI": 0.0003357616136858198}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..ae86751
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.018892288208007812}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    4,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([1108, 1116, 4456,  ..., 2396,  548, 1385]),
+       values=tensor([0.8638, 0.8794, 0.8595,  ..., 0.2787, 0.2270, 0.2436]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.1028, 0.3454, 0.0668,  ..., 0.0203, 0.1099, 0.2752])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.018892288208007812 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '55578', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.3776216506958008}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([1067, 4726, 2617,  ..., 4515, 4937,  207]),
+       values=tensor([0.7749, 0.8447, 0.6931,  ..., 0.5698, 0.5658, 0.7624]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.8259, 0.6183, 0.2744,  ..., 0.6644, 0.1716, 0.4385])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 1.3776216506958008 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '423606', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.169308185577393}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2497, 2498, 2500]),
+       col_indices=tensor([ 723, 3357, 4021,  ..., 1038, 2195, 2669]),
+       values=tensor([0.5380, 0.6250, 0.5522,  ..., 0.2239, 0.7354, 0.7870]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.8647, 0.0404, 0.2214,  ..., 0.2716, 0.6887, 0.8481])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.169308185577393 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2497, 2498, 2500]),
+       col_indices=tensor([ 723, 3357, 4021,  ..., 1038, 2195, 2669]),
+       values=tensor([0.5380, 0.6250, 0.5522,  ..., 0.2239, 0.7354, 0.7870]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.8647, 0.0404, 0.2214,  ..., 0.2716, 0.6887, 0.8481])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.169308185577393 seconds
+
+[40.09, 39.04, 39.2, 39.28, 39.5, 39.12, 39.02, 39.36, 39.0, 39.1]
+[95.75]
+12.522717714309692
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 423606, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.169308185577393, 'TIME_S_1KI': 0.024006525369275677, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1199.050221145153, 'W': 95.75}
+[40.09, 39.04, 39.2, 39.28, 39.5, 39.12, 39.02, 39.36, 39.0, 39.1, 39.76, 39.19, 39.37, 38.99, 39.74, 39.45, 39.52, 39.05, 39.58, 44.24]
+710.005
+35.50025
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 423606, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.169308185577393, 'TIME_S_1KI': 0.024006525369275677, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1199.050221145153, 'W': 95.75, 'J_1KI': 2.8305789369016328, 'W_1KI': 0.2260355141334164, 'W_D': 60.24975, 'J_D': 754.4906116077303, 'W_D_1KI': 0.14223063412699538, 'J_D_1KI': 0.0003357616136858198}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..1eeb5de
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 247437, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.32590627670288, "TIME_S_1KI": 0.04173145599365851, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1233.848487944603, "W": 98.03, "J_1KI": 4.986515710846005, "W_1KI": 0.39618165432008956, "W_D": 62.5795, "J_D": 787.6529781835079, "W_D_1KI": 0.2529108419516887, "J_D_1KI": 0.001022122164234487}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..f512a45
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.016702651977539062}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,    10,  ..., 24993, 24996, 25000]),
+       col_indices=tensor([ 203, 3164, 3874,  ..., 1660, 2575, 4898]),
+       values=tensor([0.2509, 0.0733, 0.7857,  ..., 0.9782, 0.1584, 0.7182]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6754, 0.2035, 0.5445,  ..., 0.8964, 0.5875, 0.7630])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.016702651977539062 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '62864', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.6676299571990967}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     7,  ..., 24992, 24998, 25000]),
+       col_indices=tensor([1528, 1565, 2407,  ..., 4843,  196, 1526]),
+       values=tensor([0.9615, 0.4377, 0.6921,  ..., 0.0433, 0.3280, 0.0962]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1897, 0.1898, 0.6419,  ..., 0.9248, 0.4513, 0.5147])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 2.6676299571990967 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '247437', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.32590627670288}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     9,    13,  ..., 24992, 24996, 25000]),
+       col_indices=tensor([ 429,  548,  735,  ..., 2923, 3331, 3611]),
+       values=tensor([0.1470, 0.7094, 0.7244,  ..., 0.3013, 0.3840, 0.1701]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0349, 0.9280, 0.8549,  ..., 0.2131, 0.1223, 0.0130])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.32590627670288 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     9,    13,  ..., 24992, 24996, 25000]),
+       col_indices=tensor([ 429,  548,  735,  ..., 2923, 3331, 3611]),
+       values=tensor([0.1470, 0.7094, 0.7244,  ..., 0.3013, 0.3840, 0.1701]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0349, 0.9280, 0.8549,  ..., 0.2131, 0.1223, 0.0130])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.32590627670288 seconds
+
+[40.5, 39.52, 39.15, 39.13, 39.57, 40.5, 39.17, 39.06, 39.52, 39.1]
+[98.03]
+12.586437702178955
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 247437, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.32590627670288, 'TIME_S_1KI': 0.04173145599365851, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1233.848487944603, 'W': 98.03}
+[40.5, 39.52, 39.15, 39.13, 39.57, 40.5, 39.17, 39.06, 39.52, 39.1, 40.17, 39.6, 39.6, 39.15, 39.15, 39.05, 39.05, 39.08, 39.25, 39.15]
+709.01
+35.4505
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 247437, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.32590627670288, 'TIME_S_1KI': 0.04173145599365851, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1233.848487944603, 'W': 98.03, 'J_1KI': 4.986515710846005, 'W_1KI': 0.39618165432008956, 'W_D': 62.5795, 'J_D': 787.6529781835079, 'W_D_1KI': 0.2529108419516887, 'J_D_1KI': 0.001022122164234487}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..685c3bf
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 163068, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.72166895866394, "TIME_S_1KI": 0.07188209187985345, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1665.769057817459, "W": 116.82, "J_1KI": 10.215180524796153, "W_1KI": 0.7163882552064169, "W_D": 81.24949999999998, "J_D": 1158.5593482549189, "W_D_1KI": 0.4982553290651751, "J_D_1KI": 0.0030555064700933054}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..bc4d2ed
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.02829742431640625}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     58,    108,  ..., 249919, 249959,
+                            250000]),
+       col_indices=tensor([  73,  104,  551,  ..., 4719, 4888, 4958]),
+       values=tensor([0.4939, 0.4915, 0.0888,  ..., 0.3493, 0.1552, 0.1459]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.1377, 0.0837, 0.3150,  ..., 0.5794, 0.8670, 0.3865])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.02829742431640625 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '37105', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.3891899585723877}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     55,     99,  ..., 249890, 249937,
+                            250000]),
+       col_indices=tensor([   6,   32,   44,  ..., 4844, 4921, 4988]),
+       values=tensor([0.1281, 0.2469, 0.7745,  ..., 0.0638, 0.9042, 0.9189]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6302, 0.1474, 0.6987,  ..., 0.1092, 0.0062, 0.2645])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 2.3891899585723877 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '163068', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.72166895866394}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     45,     94,  ..., 249900, 249951,
+                            250000]),
+       col_indices=tensor([  17,  114,  188,  ..., 4806, 4921, 4968]),
+       values=tensor([0.1229, 0.8785, 0.6808,  ..., 0.9268, 0.7326, 0.7148]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4545, 0.7872, 0.8321,  ..., 0.0206, 0.6423, 0.1627])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 11.72166895866394 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     45,     94,  ..., 249900, 249951,
+                            250000]),
+       col_indices=tensor([  17,  114,  188,  ..., 4806, 4921, 4968]),
+       values=tensor([0.1229, 0.8785, 0.6808,  ..., 0.9268, 0.7326, 0.7148]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4545, 0.7872, 0.8321,  ..., 0.0206, 0.6423, 0.1627])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 11.72166895866394 seconds
+
+[40.87, 39.29, 39.18, 39.72, 39.08, 39.54, 39.6, 39.54, 39.86, 39.27]
+[116.82]
+14.259279727935791
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 163068, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.72166895866394, 'TIME_S_1KI': 0.07188209187985345, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1665.769057817459, 'W': 116.82}
+[40.87, 39.29, 39.18, 39.72, 39.08, 39.54, 39.6, 39.54, 39.86, 39.27, 40.71, 39.19, 39.43, 39.27, 39.46, 39.83, 39.54, 39.53, 39.37, 39.11]
+711.4100000000001
+35.5705
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 163068, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.72166895866394, 'TIME_S_1KI': 0.07188209187985345, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1665.769057817459, 'W': 116.82, 'J_1KI': 10.215180524796153, 'W_1KI': 0.7163882552064169, 'W_D': 81.24949999999998, 'J_D': 1158.5593482549189, 'W_D_1KI': 0.4982553290651751, 'J_D_1KI': 0.0030555064700933054}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..022fbf8
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 90395, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.401180028915405, "TIME_S_1KI": 0.11506366534559882, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1713.355565071106, "W": 132.0, "J_1KI": 18.954096632237466, "W_1KI": 1.4602577576193374, "W_D": 96.25175, "J_D": 1249.344481138885, "W_D_1KI": 1.0647906410752808, "J_D_1KI": 0.011779309044474592}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..6ede2ab
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03801298141479492}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     279,     546,  ..., 1249528,
+                            1249760, 1250000]),
+       col_indices=tensor([  17,   21,   26,  ..., 4944, 4980, 4991]),
+       values=tensor([0.9138, 0.1459, 0.7159,  ..., 0.0773, 0.4834, 0.3377]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.0568, 0.1587, 0.8688,  ..., 0.8476, 0.8640, 0.6593])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 0.03801298141479492 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27622', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 3.2084648609161377}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     251,     530,  ..., 1249473,
+                            1249739, 1250000]),
+       col_indices=tensor([  53,   63,   72,  ..., 4941, 4984, 4995]),
+       values=tensor([0.4190, 0.6332, 0.1682,  ..., 0.1102, 0.0295, 0.1696]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.4081, 0.8253, 0.9060,  ..., 0.4379, 0.8960, 0.7193])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 3.2084648609161377 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '90395', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.401180028915405}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     225,     477,  ..., 1249532,
+                            1249754, 1250000]),
+       col_indices=tensor([   0,    4,   16,  ..., 4911, 4963, 4980]),
+       values=tensor([0.5027, 0.8615, 0.8405,  ..., 0.5051, 0.1395, 0.2376]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.7929, 0.2058, 0.1103,  ..., 0.0989, 0.8674, 0.8642])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.401180028915405 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     225,     477,  ..., 1249532,
+                            1249754, 1250000]),
+       col_indices=tensor([   0,    4,   16,  ..., 4911, 4963, 4980]),
+       values=tensor([0.5027, 0.8615, 0.8405,  ..., 0.5051, 0.1395, 0.2376]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.7929, 0.2058, 0.1103,  ..., 0.0989, 0.8674, 0.8642])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.401180028915405 seconds
+
+[40.46, 40.0, 39.58, 39.65, 39.39, 39.96, 39.38, 39.29, 39.31, 39.34]
+[132.0]
+12.979966402053833
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 90395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.401180028915405, 'TIME_S_1KI': 0.11506366534559882, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1713.355565071106, 'W': 132.0}
+[40.46, 40.0, 39.58, 39.65, 39.39, 39.96, 39.38, 39.29, 39.31, 39.34, 40.66, 39.72, 41.06, 39.66, 39.37, 39.71, 39.69, 39.36, 39.44, 40.33]
+714.9649999999999
+35.74825
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 90395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.401180028915405, 'TIME_S_1KI': 0.11506366534559882, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1713.355565071106, 'W': 132.0, 'J_1KI': 18.954096632237466, 'W_1KI': 1.4602577576193374, 'W_D': 96.25175, 'J_D': 1249.344481138885, 'W_D_1KI': 1.0647906410752808, 'J_D_1KI': 0.011779309044474592}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..28b4bfb
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 52843, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.472357749938965, "TIME_S_1KI": 0.19817871335728413, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1834.1895242333412, "W": 138.65, "J_1KI": 34.710170206713116, "W_1KI": 2.623810154608936, "W_D": 102.62450000000001, "J_D": 1357.611127513051, "W_D_1KI": 1.9420642279961398, "J_D_1KI": 0.036751589198117815}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..05a194a
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.05003714561462402}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     498,     977,  ..., 2499006,
+                            2499489, 2500000]),
+       col_indices=tensor([   2,   20,   22,  ..., 4935, 4945, 4946]),
+       values=tensor([0.7520, 0.3359, 0.1395,  ..., 0.8155, 0.8337, 0.5892]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3287, 0.7670, 0.4633,  ..., 0.8662, 0.8996, 0.4236])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 0.05003714561462402 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '20984', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 4.169527769088745}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     527,    1017,  ..., 2498957,
+                            2499469, 2500000]),
+       col_indices=tensor([   0,    2,   46,  ..., 4971, 4981, 4983]),
+       values=tensor([0.4264, 0.7891, 0.1289,  ..., 0.9402, 0.9265, 0.8274]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1592, 0.0229, 0.7345,  ..., 0.9022, 0.9396, 0.4003])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 4.169527769088745 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52843', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.472357749938965}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     536,    1043,  ..., 2499033,
+                            2499531, 2500000]),
+       col_indices=tensor([   2,    4,    9,  ..., 4990, 4992, 4998]),
+       values=tensor([0.3950, 0.1857, 0.2386,  ..., 0.4312, 0.4990, 0.5416]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5233, 0.3838, 0.2090,  ..., 0.9440, 0.1891, 0.8384])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.472357749938965 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     536,    1043,  ..., 2499033,
+                            2499531, 2500000]),
+       col_indices=tensor([   2,    4,    9,  ..., 4990, 4992, 4998]),
+       values=tensor([0.3950, 0.1857, 0.2386,  ..., 0.4312, 0.4990, 0.5416]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5233, 0.3838, 0.2090,  ..., 0.9440, 0.1891, 0.8384])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.472357749938965 seconds
+
+[40.4, 39.54, 40.0, 39.38, 39.54, 39.86, 39.47, 40.22, 39.37, 39.4]
+[138.65]
+13.228918313980103
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52843, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.472357749938965, 'TIME_S_1KI': 0.19817871335728413, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1834.1895242333412, 'W': 138.65}
+[40.4, 39.54, 40.0, 39.38, 39.54, 39.86, 39.47, 40.22, 39.37, 39.4, 41.49, 40.09, 39.59, 39.6, 39.65, 39.39, 39.42, 39.69, 45.2, 39.71]
+720.51
+36.0255
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52843, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.472357749938965, 'TIME_S_1KI': 0.19817871335728413, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1834.1895242333412, 'W': 138.65, 'J_1KI': 34.710170206713116, 'W_1KI': 2.623810154608936, 'W_D': 102.62450000000001, 'J_D': 1357.611127513051, 'W_D_1KI': 1.9420642279961398, 'J_D_1KI': 0.036751589198117815}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..3a471cf
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28798, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.444172620773315, "TIME_S_1KI": 0.36267006808713503, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1937.146224603653, "W": 139.06, "J_1KI": 67.26669298575086, "W_1KI": 4.8288075560802834, "W_D": 102.9745, "J_D": 1434.4647195847035, "W_D_1KI": 3.5757517883186334, "J_D_1KI": 0.12416667089098664}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..fd0984f
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.06763482093811035}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     956,    1961,  ..., 4997943,
+                            4998975, 5000000]),
+       col_indices=tensor([   6,   18,   19,  ..., 4986, 4993, 4998]),
+       values=tensor([0.4638, 0.8169, 0.7421,  ..., 0.5926, 0.1207, 0.0279]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1902, 0.8341, 0.1608,  ..., 0.1172, 0.3175, 0.0262])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 0.06763482093811035 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15524', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 5.659992933273315}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     987,    2000,  ..., 4998018,
+                            4999001, 5000000]),
+       col_indices=tensor([   4,   16,   17,  ..., 4983, 4988, 4998]),
+       values=tensor([0.3168, 0.3066, 0.1113,  ..., 0.0328, 0.5136, 0.1275]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.9704, 0.5285, 0.3815,  ..., 0.6149, 0.3291, 0.1983])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 5.659992933273315 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28798', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.444172620773315}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1005,    2014,  ..., 4998103,
+                            4999056, 5000000]),
+       col_indices=tensor([   4,    9,   14,  ..., 4980, 4983, 4993]),
+       values=tensor([0.7293, 0.3445, 0.3834,  ..., 0.7374, 0.4715, 0.7945]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.9531, 0.9906, 0.0327,  ..., 0.2819, 0.9884, 0.0185])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.444172620773315 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1005,    2014,  ..., 4998103,
+                            4999056, 5000000]),
+       col_indices=tensor([   4,    9,   14,  ..., 4980, 4983, 4993]),
+       values=tensor([0.7293, 0.3445, 0.3834,  ..., 0.7374, 0.4715, 0.7945]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.9531, 0.9906, 0.0327,  ..., 0.2819, 0.9884, 0.0185])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.444172620773315 seconds
+
+[40.89, 39.68, 39.94, 39.87, 39.8, 39.94, 40.85, 40.96, 39.65, 40.01]
+[139.06]
+13.930290699005127
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28798, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.444172620773315, 'TIME_S_1KI': 0.36267006808713503, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1937.146224603653, 'W': 139.06}
+[40.89, 39.68, 39.94, 39.87, 39.8, 39.94, 40.85, 40.96, 39.65, 40.01, 41.32, 40.08, 40.09, 40.7, 40.16, 39.61, 39.76, 39.88, 39.81, 39.64]
+721.71
+36.0855
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28798, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.444172620773315, 'TIME_S_1KI': 0.36267006808713503, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1937.146224603653, 'W': 139.06, 'J_1KI': 67.26669298575086, 'W_1KI': 4.8288075560802834, 'W_D': 102.9745, 'J_D': 1434.4647195847035, 'W_D_1KI': 3.5757517883186334, 'J_D_1KI': 0.12416667089098664}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..6f0dd43
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 18401, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.148674011230469, "TIME_S_1KI": 0.5515283958062317, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1912.232966468334, "W": 137.63, "J_1KI": 103.92005687018825, "W_1KI": 7.479484810608119, "W_D": 101.70774999999999, "J_D": 1413.1287691296934, "W_D_1KI": 5.5272947122439, "J_D_1KI": 0.3003801267454975}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..60a702d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.093505859375}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1476,    2946,  ..., 7497092,
+                            7498499, 7500000]),
+       col_indices=tensor([   3,    4,    9,  ..., 4993, 4995, 4998]),
+       values=tensor([0.7692, 0.9577, 0.6421,  ..., 0.4974, 0.8037, 0.4799]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.8552, 0.1634, 0.3191,  ..., 0.0243, 0.9305, 0.7580])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 0.093505859375 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '11229', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 6.407301664352417}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1491,    2991,  ..., 7496987,
+                            7498491, 7500000]),
+       col_indices=tensor([   6,    7,   10,  ..., 4987, 4988, 4999]),
+       values=tensor([0.9932, 0.6823, 0.0941,  ..., 0.3170, 0.1700, 0.5277]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.7857, 0.3541, 0.7153,  ..., 0.0858, 0.7918, 0.2952])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 6.407301664352417 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18401', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.148674011230469}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1524,    2999,  ..., 7496978,
+                            7498500, 7500000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4975, 4979, 4999]),
+       values=tensor([0.7847, 0.2112, 0.1435,  ..., 0.9949, 0.2225, 0.8434]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.9436, 0.7820, 0.2976,  ..., 0.7279, 0.8012, 0.5089])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.148674011230469 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1524,    2999,  ..., 7496978,
+                            7498500, 7500000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4975, 4979, 4999]),
+       values=tensor([0.7847, 0.2112, 0.1435,  ..., 0.9949, 0.2225, 0.8434]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.9436, 0.7820, 0.2976,  ..., 0.7279, 0.8012, 0.5089])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.148674011230469 seconds
+
+[40.88, 40.15, 40.48, 39.7, 39.9, 39.72, 39.73, 39.77, 39.68, 39.5]
+[137.63]
+13.894012689590454
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 18401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.148674011230469, 'TIME_S_1KI': 0.5515283958062317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1912.232966468334, 'W': 137.63}
+[40.88, 40.15, 40.48, 39.7, 39.9, 39.72, 39.73, 39.77, 39.68, 39.5, 40.73, 39.74, 41.44, 39.63, 39.52, 39.59, 39.98, 39.43, 39.67, 39.52]
+718.445
+35.922250000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 18401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.148674011230469, 'TIME_S_1KI': 0.5515283958062317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1912.232966468334, 'W': 137.63, 'J_1KI': 103.92005687018825, 'W_1KI': 7.479484810608119, 'W_D': 101.70774999999999, 'J_D': 1413.1287691296934, 'W_D_1KI': 5.5272947122439, 'J_D_1KI': 0.3003801267454975}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..b33500c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4623, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.869210720062256, "TIME_S_1KI": 2.3511163140952314, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1715.391318473816, "W": 123.94000000000001, "J_1KI": 371.0558768059303, "W_1KI": 26.809431105342853, "W_D": 87.73350000000002, "J_D": 1214.2753287019732, "W_D_1KI": 18.977611940298512, "J_D_1KI": 4.105042600107833}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..915d5a7
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.2556135654449463}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2026,     4021,  ...,  9995990,
+                             9997978, 10000000]),
+       col_indices=tensor([   1,    6,    9,  ..., 4991, 4993, 4997]),
+       values=tensor([0.3323, 0.6585, 0.3485,  ..., 0.6316, 0.2886, 0.7495]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3914, 0.7431, 0.0627,  ..., 0.4218, 0.6007, 0.5832])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 0.2556135654449463 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4107', '-ss', '5000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 9.327423810958862}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2032,     4046,  ...,  9995943,
+                             9997941, 10000000]),
+       col_indices=tensor([   1,    2,    4,  ..., 4988, 4990, 4991]),
+       values=tensor([0.7887, 0.1218, 0.0752,  ..., 0.7697, 0.6176, 0.4928]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3655, 0.9637, 0.0803,  ..., 0.0942, 0.4831, 0.3974])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 9.327423810958862 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4623', '-ss', '5000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.869210720062256}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2080,     4074,  ...,  9996120,
+                             9998055, 10000000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4988, 4989, 4998]),
+       values=tensor([0.8721, 0.2802, 0.5674,  ..., 0.7807, 0.4474, 0.7441]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.9197, 0.0161, 0.2580,  ..., 0.5344, 0.2373, 0.6957])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.869210720062256 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2080,     4074,  ...,  9996120,
+                             9998055, 10000000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4988, 4989, 4998]),
+       values=tensor([0.8721, 0.2802, 0.5674,  ..., 0.7807, 0.4474, 0.7441]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.9197, 0.0161, 0.2580,  ..., 0.5344, 0.2373, 0.6957])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.869210720062256 seconds
+
+[40.45, 40.3, 45.54, 40.13, 39.83, 39.68, 39.66, 40.04, 39.63, 41.25]
+[123.94]
+13.840497970581055
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4623, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.869210720062256, 'TIME_S_1KI': 2.3511163140952314, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1715.391318473816, 'W': 123.94000000000001}
+[40.45, 40.3, 45.54, 40.13, 39.83, 39.68, 39.66, 40.04, 39.63, 41.25, 41.52, 40.34, 39.67, 39.9, 39.74, 39.58, 39.63, 39.49, 39.57, 39.58]
+724.1299999999999
+36.20649999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4623, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.869210720062256, 'TIME_S_1KI': 2.3511163140952314, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1715.391318473816, 'W': 123.94000000000001, 'J_1KI': 371.0558768059303, 'W_1KI': 26.809431105342853, 'W_D': 87.73350000000002, 'J_D': 1214.2753287019732, 'W_D_1KI': 18.977611940298512, 'J_D_1KI': 4.105042600107833}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..ef8b561
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3775, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 11.108771324157715, "TIME_S_1KI": 2.94272088057158, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1948.3000312638283, "W": 122.57, "J_1KI": 516.1059685467094, "W_1KI": 32.46887417218543, "W_D": 86.1335, "J_D": 1369.1270355132817, "W_D_1KI": 22.816821192052977, "J_D_1KI": 6.044191044252445}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..05d0f73
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.2961087226867676}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2490,     4975,  ..., 12494955,
+                            12497491, 12500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4994, 4996, 4998]),
+       values=tensor([0.9230, 0.7404, 0.0716,  ..., 0.8209, 0.3183, 0.8676]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4086, 0.9880, 0.1016,  ..., 0.8907, 0.8066, 0.6446])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 0.2961087226867676 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3545', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.858964443206787}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2500,     5017,  ..., 12494975,
+                            12497542, 12500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4994, 4997, 4999]),
+       values=tensor([0.5138, 0.3202, 0.6371,  ..., 0.0572, 0.7854, 0.0609]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.5537, 0.0044, 0.4461,  ..., 0.4637, 0.2205, 0.0434])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 9.858964443206787 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3775', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 11.108771324157715}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2457,     4970,  ..., 12495118,
+                            12497586, 12500000]),
+       col_indices=tensor([   0,    5,    6,  ..., 4996, 4997, 4999]),
+       values=tensor([0.5982, 0.0244, 0.5821,  ..., 0.2791, 0.2569, 0.9852]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.1130, 0.4034, 0.5297,  ..., 0.0598, 0.7079, 0.8853])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 11.108771324157715 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2457,     4970,  ..., 12495118,
+                            12497586, 12500000]),
+       col_indices=tensor([   0,    5,    6,  ..., 4996, 4997, 4999]),
+       values=tensor([0.5982, 0.0244, 0.5821,  ..., 0.2791, 0.2569, 0.9852]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.1130, 0.4034, 0.5297,  ..., 0.0598, 0.7079, 0.8853])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 11.108771324157715 seconds
+
+[40.6, 40.02, 45.54, 40.47, 40.31, 40.18, 40.34, 40.25, 40.23, 39.68]
+[122.57]
+15.89540696144104
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3775, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 11.108771324157715, 'TIME_S_1KI': 2.94272088057158, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1948.3000312638283, 'W': 122.57}
+[40.6, 40.02, 45.54, 40.47, 40.31, 40.18, 40.34, 40.25, 40.23, 39.68, 40.3, 40.16, 40.89, 40.3, 40.11, 40.08, 40.03, 39.96, 39.79, 39.56]
+728.73
+36.4365
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3775, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 11.108771324157715, 'TIME_S_1KI': 2.94272088057158, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1948.3000312638283, 'W': 122.57, 'J_1KI': 516.1059685467094, 'W_1KI': 32.46887417218543, 'W_D': 86.1335, 'J_D': 1369.1270355132817, 'W_D_1KI': 22.816821192052977, 'J_D_1KI': 6.044191044252445}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..aad5720
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 475914, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.308423519134521, "TIME_S_1KI": 0.021660265340239036, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1189.9414702892302, "W": 94.85, "J_1KI": 2.5003287784961783, "W_1KI": 0.19930071399454524, "W_D": 59.248999999999995, "J_D": 743.3088262853622, "W_D_1KI": 0.12449518190261265, "J_D_1KI": 0.000261591762172604}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..c0883dc
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,329 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.04127812385559082}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([ 980, 1760, 2093,  785, 4671, 1948,  723, 1459, 1601,
+                           3059, 3354, 3009, 3506, 1670, 2673, 2868, 3157, 4447,
+                           1531,  659, 4281, 2627, 1377, 2950, 2583, 1810, 1775,
+                           4655, 1859, 3752, 2371, 1562,  678, 1349,  716, 1427,
+                           4744,  619, 1269,  399, 1864, 2208, 1480,  456,  208,
+                           1221, 4437,  899, 3205,  761, 3187, 2953, 2815,  148,
+                           2514, 1056, 1872, 3736, 1283, 2888, 2704, 2532, 4106,
+                           3966,  441, 2333,  946, 4406, 4968, 4149, 1833, 4307,
+                           4337, 2109, 4989, 3863, 2823, 4489, 3013, 4755, 4523,
+                           3606,  244,  568, 1109, 1740, 4991, 4457, 3089, 4428,
+                           4324, 4720, 4209, 3750, 3625,  450, 2141, 1794, 3260,
+                           4714, 3864, 2541, 1784, 2782, 3268, 3685, 3828, 1583,
+                           1640, 2421, 3816, 1055,  144, 4272,  260, 1691, 1506,
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+                           3613, 2403, 1235,  967, 1065, 2176, 1047, 2886, 1645,
+                           3823, 3916, 3782,   98, 2899, 2287, 3273, 4032, 4889,
+                           1097, 3448, 4328, 2582, 4575, 4148, 3284, 2665,  249,
+                           1314, 3187, 3430, 4704,   97, 1632,  931,  382, 3849,
+                           3930, 2162, 1791, 4253, 2474, 3110, 4678, 4692,  474,
+                           3715,  235, 3222, 4417, 4989, 3151, 1089, 1264, 3742,
+                           4431,  906,  341, 4151,  340, 1004, 4160, 1654, 1155,
+                            188, 4818, 2576,  447, 1886, 1055, 2211, 1261, 3706,
+                           4104, 4081, 2022, 1659, 4405,  105,  257, 1519, 1573,
+                           2647,  334, 3953, 2722, 2554,  628, 1350, 4587, 1239,
+                           3896,   49, 2034, 3369,  750, 1198,  743, 3025, 4827,
+                           4181, 3975, 2556, 3695, 4820, 3552, 3237]),
+       values=tensor([0.9726, 0.4932, 0.3912, 0.9738, 0.2382, 0.2194, 0.1583,
+                      0.5409, 0.3403, 0.0032, 0.6572, 0.0849, 0.3926, 0.9886,
+                      0.6424, 0.9153, 0.1836, 0.7579, 0.8401, 0.5061, 0.5178,
+                      0.6259, 0.6574, 0.6207, 0.6672, 0.0690, 0.9817, 0.7086,
+                      0.9781, 0.5592, 0.4518, 0.3448, 0.6644, 0.1469, 0.4823,
+                      0.3431, 0.4389, 0.7337, 0.3527, 0.9829, 0.5078, 0.8332,
+                      0.7767, 0.9645, 0.7365, 0.4985, 0.9411, 0.0544, 0.1772,
+                      0.3375, 0.9957, 0.2067, 0.3235, 0.4169, 0.2185, 0.9745,
+                      0.4024, 0.1951, 0.5401, 0.1889, 0.4891, 0.1494, 0.3864,
+                      0.9968, 0.5170, 0.5194, 0.2311, 0.7378, 0.0181, 0.8480,
+                      0.8709, 0.1149, 0.7166, 0.5193, 0.8973, 0.0220, 0.9337,
+                      0.7205, 0.2663, 0.4873, 0.6865, 0.7454, 0.1670, 0.6733,
+                      0.5028, 0.8469, 0.5015, 0.0183, 0.9101, 0.5044, 0.6164,
+                      0.0686, 0.2021, 0.0101, 0.2356, 0.6960, 0.5089, 0.6651,
+                      0.9123, 0.3294, 0.9018, 0.9354, 0.4913, 0.5484, 0.4661,
+                      0.9948, 0.4189, 0.4723, 0.9512, 0.6341, 0.6798, 0.5802,
+                      0.6623, 0.0188, 0.0129, 0.1565, 0.2382, 0.5939, 0.9749,
+                      0.6008, 0.1917, 0.4414, 0.2563, 0.1692, 0.9585, 0.0472,
+                      0.8804, 0.5964, 0.2177, 0.3908, 0.2373, 0.0395, 0.7888,
+                      0.2774, 0.1837, 0.8164, 0.9307, 0.6092, 0.7632, 0.0856,
+                      0.4941, 0.2709, 0.6765, 0.8735, 0.4097, 0.3394, 0.4724,
+                      0.7144, 0.5758, 0.2577, 0.2371, 0.8221, 0.9059, 0.5592,
+                      0.3289, 0.0171, 0.1330, 0.9826, 0.1215, 0.7990, 0.5552,
+                      0.1342, 0.7031, 0.1802, 0.8204, 0.3147, 0.1663, 0.8508,
+                      0.4793, 0.5702, 0.1389, 0.1455, 0.9150, 0.8457, 0.2787,
+                      0.6364, 0.0265, 0.9823, 0.1357, 0.7315, 0.5366, 0.2059,
+                      0.1967, 0.4234, 0.3059, 0.0470, 0.8504, 0.4796, 0.2864,
+                      0.4369, 0.4481, 0.7929, 0.8452, 0.0958, 0.5059, 0.6459,
+                      0.8388, 0.7761, 0.1938, 0.8212, 0.1919, 0.8336, 0.3582,
+                      0.0664, 0.5404, 0.5179, 0.4249, 0.9245, 0.0841, 0.7587,
+                      0.7759, 0.0382, 0.0647, 0.6744, 0.6055, 0.1518, 0.6047,
+                      0.4888, 0.3468, 0.0096, 0.8517, 0.8972, 0.6357, 0.5150,
+                      0.0879, 0.9902, 0.5975, 0.0921, 0.1553, 0.8301, 0.3740,
+                      0.3374, 0.9082, 0.3028, 0.9956, 0.7728, 0.1467, 0.8331,
+                      0.1315, 0.6195, 0.4346, 0.3339, 0.9836, 0.5643, 0.7400,
+                      0.8152, 0.3695, 0.3467, 0.0410, 0.0075]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4444, 0.7904, 0.6664,  ..., 0.7984, 0.5487, 0.5407])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.04127812385559082 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '25437', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.5612115859985352}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([4513, 4874, 3027,  172, 3763, 1079,  239, 4529, 4955,
+                            608, 1066, 2737, 4445, 4271, 3658,  687, 3210, 2517,
+                           2807, 1524, 3953, 1461, 3621, 1960, 4173, 4521, 4104,
+                           3884,  561, 4646, 4065, 4845, 1534, 1821, 1059, 2352,
+                           1896, 3528, 4564, 4230,  603, 1565, 2747, 4968, 3463,
+                           2434, 4028, 3145, 1082, 2478, 4336, 3976,   44, 4019,
+                           3984, 3758, 2982, 3832, 4392, 3709, 4379, 1452, 4965,
+                            778, 3505, 1776, 1029, 1733, 4085,   16, 3544,  611,
+                            487, 4244, 3327, 1553,  300,  454,  615, 1019, 1638,
+                           2606, 4708, 2838, 1644, 2517, 3419, 4512, 1409, 4514,
+                             98,  975, 2304, 4532,  578, 3866, 4888, 1913, 1427,
+                           2350, 3190,  471, 3972, 3754, 1263, 2752, 1137, 2063,
+                           1340, 4788, 4731, 1452,  253, 4150, 2425, 3321, 2705,
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+                            371,  460, 4416, 3517, 2533, 3314, 4727, 4068, 2071,
+                             89, 3259,  446, 2255, 4308, 1260, 2389, 3030, 3160,
+                            928, 1241, 1770,  729, 2255, 2572, 3942,  459,  250,
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+                           2055,  653, 1366, 4832, 1570, 1682, 1477, 4511, 1980,
+                           2756, 1220, 4509, 2420,  623,  993, 3225, 1492, 4751,
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+                           2786,  558,  866,  946, 3251, 3488, 4976, 3700, 3363,
+                           3272, 1906, 4483, 1350,  885, 3495, 1900]),
+       values=tensor([0.7997, 0.6693, 0.9766, 0.8132, 0.4494, 0.7325, 0.7457,
+                      0.7556, 0.9866, 0.9047, 0.1758, 0.6478, 0.3725, 0.9918,
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+                      0.1089, 0.2905, 0.3401, 0.3586, 0.4906, 0.7927, 0.7147,
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+                      0.7421, 0.3011, 0.1605, 0.1136, 0.9314, 0.3920, 0.9924,
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+                      0.3931, 0.9858, 0.1292, 0.6472, 0.7465, 0.0833, 0.0197,
+                      0.8484, 0.0914, 0.1498, 0.8894, 0.1548, 0.5990, 0.0393,
+                      0.7324, 0.7542, 0.7672, 0.8989, 0.1970, 0.1932, 0.9622,
+                      0.5932, 0.9630, 0.7336, 0.7453, 0.9290]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.8285, 0.4420, 0.7749,  ..., 0.7287, 0.4578, 0.9435])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.5612115859985352 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '475914', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.308423519134521}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([ 698, 3054, 4592, 1629, 4601, 3886, 4804,  318, 1415,
+                            433, 1872, 1429, 1550, 3511, 4304, 3637, 1101, 2710,
+                           4079,  541, 1194,   97, 2807, 2811, 3206, 3991, 2286,
+                           2681, 4835,   38, 1361, 1702, 1987, 1831,  485,  140,
+                           4362, 3450, 2222,  295, 3370,  591, 1718, 4950, 1639,
+                           3575, 1461, 4389, 3994, 2356, 1105, 1104, 1761, 4007,
+                           4669, 3008, 4553, 4279, 1484, 3371, 4533,  863,  587,
+                           1360, 4727, 3879,  832,  240, 2132, 2582, 1372, 4190,
+                           3588, 2592, 4310, 2614, 1567, 1660,  604, 4488, 1313,
+                           3610, 3188, 2899, 3261, 1055, 1112, 4180, 1426, 3909,
+                           3409, 4510, 3025,  703, 1794,  225,  659, 2212, 1407,
+                           4739, 1542, 2238, 2858, 4535, 4405, 3841, 3716, 4156,
+                           4416, 2641, 4372, 1051,  980, 3180, 2782,  497,  394,
+                            605,  971,  455, 3831,  523, 3209,  733, 4726, 2765,
+                            226, 3470, 1720, 2299, 2372, 1447, 3202, 1153, 3498,
+                           4698, 2998, 1466,  363, 4324, 2506, 2090, 2285, 2204,
+                            303, 4406, 1500, 1826, 4080, 1378, 1816,  980,   66,
+                           4392, 4266, 2875,  275, 1828, 3108,  989, 3152, 2508,
+                           1550, 2008, 1066,  529,   18,    1, 3369, 4893, 2556,
+                           1068, 1671, 1086, 2667, 3511, 2891, 1749, 4140, 3150,
+                             43, 1921, 1837,  980, 1293, 1802, 2390,  249,  768,
+                           4105, 1721, 1435, 4658, 2745, 4338, 2320, 3879, 2641,
+                           3097, 1585, 3887, 4913, 4556, 2794, 2705,  299,   61,
+                           2384, 1270,  740, 2129, 3392, 3774, 4644, 4192, 4506,
+                            149, 4748, 3571, 1159, 4587, 1920,  982, 2347, 1650,
+                           1882, 1955, 3910,  197, 4484, 3655, 4387,  968, 3452,
+                            181, 2166, 1855, 2452,  189, 3074, 2522, 3426,  135,
+                           1267,  666, 4928, 2908, 4053, 4593,  448]),
+       values=tensor([0.8620, 0.7365, 0.7531, 0.5477, 0.5295, 0.4268, 0.2903,
+                      0.3296, 0.4852, 0.0295, 0.6605, 0.4770, 0.1707, 0.4638,
+                      0.1229, 0.6813, 0.2237, 0.9317, 0.9546, 0.4741, 0.7915,
+                      0.3909, 0.2549, 0.7853, 0.8207, 0.9924, 0.8328, 0.0293,
+                      0.3281, 0.4028, 0.9335, 0.8141, 0.3687, 0.4243, 0.5386,
+                      0.2123, 0.0695, 0.2792, 0.2453, 0.4935, 0.1675, 0.4387,
+                      0.5777, 0.6384, 0.5870, 0.2050, 0.9519, 0.0161, 0.0462,
+                      0.8312, 0.5114, 0.5703, 0.5170, 0.0110, 0.6229, 0.7339,
+                      0.2337, 0.7709, 0.7844, 0.2062, 0.2004, 0.9990, 0.4625,
+                      0.4209, 0.7064, 0.0680, 0.6043, 0.0073, 0.1383, 0.5359,
+                      0.1641, 0.0316, 0.0479, 0.9788, 0.0764, 0.0936, 0.1603,
+                      0.1581, 0.8855, 0.4285, 0.9101, 0.6054, 0.5164, 0.1839,
+                      0.2783, 0.0513, 0.4451, 0.7375, 0.3333, 0.1348, 0.3539,
+                      0.0102, 0.1620, 0.4960, 0.1201, 0.8615, 0.2151, 0.0085,
+                      0.8133, 0.8439, 0.5713, 0.6595, 0.6728, 0.2738, 0.1487,
+                      0.3205, 0.6933, 0.0963, 0.6731, 0.6903, 0.0043, 0.7900,
+                      0.7911, 0.9496, 0.4295, 0.5758, 0.2659, 0.3025, 0.6145,
+                      0.1511, 0.7265, 0.9480, 0.6751, 0.8138, 0.6361, 0.4149,
+                      0.8899, 0.3218, 0.3413, 0.2054, 0.1555, 0.4398, 0.9946,
+                      0.6820, 0.1566, 0.7238, 0.9562, 0.1023, 0.0696, 0.9724,
+                      0.8182, 0.0031, 0.0289, 0.5187, 0.0063, 0.5262, 0.9232,
+                      0.3694, 0.0136, 0.3019, 0.9633, 0.8770, 0.0826, 0.1792,
+                      0.6372, 0.5719, 0.7979, 0.1369, 0.9923, 0.7514, 0.0627,
+                      0.3337, 0.0132, 0.9026, 0.1169, 0.4065, 0.7302, 0.3087,
+                      0.4276, 0.6874, 0.0705, 0.4727, 0.3286, 0.7188, 0.3727,
+                      0.5310, 0.1979, 0.9773, 0.3076, 0.3372, 0.2546, 0.3340,
+                      0.4532, 0.0609, 0.2279, 0.8651, 0.8162, 0.8251, 0.1216,
+                      0.3049, 0.0805, 0.1284, 0.1859, 0.3690, 0.3435, 0.7762,
+                      0.7083, 0.6529, 0.8556, 0.1421, 0.4528, 0.4045, 0.9221,
+                      0.6914, 0.6437, 0.8815, 0.0609, 0.9680, 0.2115, 0.5295,
+                      0.5418, 0.8646, 0.6735, 0.1927, 0.2578, 0.4564, 0.0603,
+                      0.1414, 0.3382, 0.0772, 0.6503, 0.3586, 0.8775, 0.8840,
+                      0.9215, 0.4825, 0.2733, 0.0423, 0.6825, 0.8144, 0.0837,
+                      0.2758, 0.6188, 0.3276, 0.0762, 0.7932, 0.5621, 0.9067,
+                      0.7339, 0.1976, 0.8462, 0.5736, 0.2659, 0.7486, 0.3053,
+                      0.7429, 0.4272, 0.8072, 0.5183, 0.4677]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4130, 0.3379, 0.7498,  ..., 0.0848, 0.9618, 0.5893])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.308423519134521 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([ 698, 3054, 4592, 1629, 4601, 3886, 4804,  318, 1415,
+                            433, 1872, 1429, 1550, 3511, 4304, 3637, 1101, 2710,
+                           4079,  541, 1194,   97, 2807, 2811, 3206, 3991, 2286,
+                           2681, 4835,   38, 1361, 1702, 1987, 1831,  485,  140,
+                           4362, 3450, 2222,  295, 3370,  591, 1718, 4950, 1639,
+                           3575, 1461, 4389, 3994, 2356, 1105, 1104, 1761, 4007,
+                           4669, 3008, 4553, 4279, 1484, 3371, 4533,  863,  587,
+                           1360, 4727, 3879,  832,  240, 2132, 2582, 1372, 4190,
+                           3588, 2592, 4310, 2614, 1567, 1660,  604, 4488, 1313,
+                           3610, 3188, 2899, 3261, 1055, 1112, 4180, 1426, 3909,
+                           3409, 4510, 3025,  703, 1794,  225,  659, 2212, 1407,
+                           4739, 1542, 2238, 2858, 4535, 4405, 3841, 3716, 4156,
+                           4416, 2641, 4372, 1051,  980, 3180, 2782,  497,  394,
+                            605,  971,  455, 3831,  523, 3209,  733, 4726, 2765,
+                            226, 3470, 1720, 2299, 2372, 1447, 3202, 1153, 3498,
+                           4698, 2998, 1466,  363, 4324, 2506, 2090, 2285, 2204,
+                            303, 4406, 1500, 1826, 4080, 1378, 1816,  980,   66,
+                           4392, 4266, 2875,  275, 1828, 3108,  989, 3152, 2508,
+                           1550, 2008, 1066,  529,   18,    1, 3369, 4893, 2556,
+                           1068, 1671, 1086, 2667, 3511, 2891, 1749, 4140, 3150,
+                             43, 1921, 1837,  980, 1293, 1802, 2390,  249,  768,
+                           4105, 1721, 1435, 4658, 2745, 4338, 2320, 3879, 2641,
+                           3097, 1585, 3887, 4913, 4556, 2794, 2705,  299,   61,
+                           2384, 1270,  740, 2129, 3392, 3774, 4644, 4192, 4506,
+                            149, 4748, 3571, 1159, 4587, 1920,  982, 2347, 1650,
+                           1882, 1955, 3910,  197, 4484, 3655, 4387,  968, 3452,
+                            181, 2166, 1855, 2452,  189, 3074, 2522, 3426,  135,
+                           1267,  666, 4928, 2908, 4053, 4593,  448]),
+       values=tensor([0.8620, 0.7365, 0.7531, 0.5477, 0.5295, 0.4268, 0.2903,
+                      0.3296, 0.4852, 0.0295, 0.6605, 0.4770, 0.1707, 0.4638,
+                      0.1229, 0.6813, 0.2237, 0.9317, 0.9546, 0.4741, 0.7915,
+                      0.3909, 0.2549, 0.7853, 0.8207, 0.9924, 0.8328, 0.0293,
+                      0.3281, 0.4028, 0.9335, 0.8141, 0.3687, 0.4243, 0.5386,
+                      0.2123, 0.0695, 0.2792, 0.2453, 0.4935, 0.1675, 0.4387,
+                      0.5777, 0.6384, 0.5870, 0.2050, 0.9519, 0.0161, 0.0462,
+                      0.8312, 0.5114, 0.5703, 0.5170, 0.0110, 0.6229, 0.7339,
+                      0.2337, 0.7709, 0.7844, 0.2062, 0.2004, 0.9990, 0.4625,
+                      0.4209, 0.7064, 0.0680, 0.6043, 0.0073, 0.1383, 0.5359,
+                      0.1641, 0.0316, 0.0479, 0.9788, 0.0764, 0.0936, 0.1603,
+                      0.1581, 0.8855, 0.4285, 0.9101, 0.6054, 0.5164, 0.1839,
+                      0.2783, 0.0513, 0.4451, 0.7375, 0.3333, 0.1348, 0.3539,
+                      0.0102, 0.1620, 0.4960, 0.1201, 0.8615, 0.2151, 0.0085,
+                      0.8133, 0.8439, 0.5713, 0.6595, 0.6728, 0.2738, 0.1487,
+                      0.3205, 0.6933, 0.0963, 0.6731, 0.6903, 0.0043, 0.7900,
+                      0.7911, 0.9496, 0.4295, 0.5758, 0.2659, 0.3025, 0.6145,
+                      0.1511, 0.7265, 0.9480, 0.6751, 0.8138, 0.6361, 0.4149,
+                      0.8899, 0.3218, 0.3413, 0.2054, 0.1555, 0.4398, 0.9946,
+                      0.6820, 0.1566, 0.7238, 0.9562, 0.1023, 0.0696, 0.9724,
+                      0.8182, 0.0031, 0.0289, 0.5187, 0.0063, 0.5262, 0.9232,
+                      0.3694, 0.0136, 0.3019, 0.9633, 0.8770, 0.0826, 0.1792,
+                      0.6372, 0.5719, 0.7979, 0.1369, 0.9923, 0.7514, 0.0627,
+                      0.3337, 0.0132, 0.9026, 0.1169, 0.4065, 0.7302, 0.3087,
+                      0.4276, 0.6874, 0.0705, 0.4727, 0.3286, 0.7188, 0.3727,
+                      0.5310, 0.1979, 0.9773, 0.3076, 0.3372, 0.2546, 0.3340,
+                      0.4532, 0.0609, 0.2279, 0.8651, 0.8162, 0.8251, 0.1216,
+                      0.3049, 0.0805, 0.1284, 0.1859, 0.3690, 0.3435, 0.7762,
+                      0.7083, 0.6529, 0.8556, 0.1421, 0.4528, 0.4045, 0.9221,
+                      0.6914, 0.6437, 0.8815, 0.0609, 0.9680, 0.2115, 0.5295,
+                      0.5418, 0.8646, 0.6735, 0.1927, 0.2578, 0.4564, 0.0603,
+                      0.1414, 0.3382, 0.0772, 0.6503, 0.3586, 0.8775, 0.8840,
+                      0.9215, 0.4825, 0.2733, 0.0423, 0.6825, 0.8144, 0.0837,
+                      0.2758, 0.6188, 0.3276, 0.0762, 0.7932, 0.5621, 0.9067,
+                      0.7339, 0.1976, 0.8462, 0.5736, 0.2659, 0.7486, 0.3053,
+                      0.7429, 0.4272, 0.8072, 0.5183, 0.4677]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4130, 0.3379, 0.7498,  ..., 0.0848, 0.9618, 0.5893])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.308423519134521 seconds
+
+[39.69, 38.64, 38.76, 38.66, 38.74, 43.79, 39.13, 39.04, 39.27, 38.98]
+[94.85]
+12.54550838470459
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 475914, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.308423519134521, 'TIME_S_1KI': 0.021660265340239036, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.9414702892302, 'W': 94.85}
+[39.69, 38.64, 38.76, 38.66, 38.74, 43.79, 39.13, 39.04, 39.27, 38.98, 39.59, 39.06, 44.18, 38.71, 38.85, 39.28, 38.92, 38.81, 38.79, 40.52]
+712.02
+35.601
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 475914, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.308423519134521, 'TIME_S_1KI': 0.021660265340239036, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1189.9414702892302, 'W': 94.85, 'J_1KI': 2.5003287784961783, 'W_1KI': 0.19930071399454524, 'W_D': 59.248999999999995, 'J_D': 743.3088262853622, 'W_D_1KI': 0.12449518190261265, 'J_D_1KI': 0.000261591762172604}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..ae25247
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 463602, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.329545974731445, "TIME_S_1KI": 0.02228106430673605, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1198.2176897239685, "W": 95.11, "J_1KI": 2.5845826586683587, "W_1KI": 0.20515442124926123, "W_D": 59.724, "J_D": 752.4167101364135, "W_D_1KI": 0.12882601886963388, "J_D_1KI": 0.00027788063655815524}
diff --git a/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..3c48d35
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/epyc_7313p_16_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.01933002471923828}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([ 393, 4092, 1605,  ..., 4543,  205, 1898]),
+       values=tensor([0.0363, 0.1593, 0.8850,  ..., 0.0884, 0.4054, 0.0261]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.7170, 0.2316, 0.8921,  ..., 0.0306, 0.1187, 0.4918])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.01933002471923828 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '54319', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.230255126953125}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4735, 1903, 2985,  ..., 3889, 4420, 4686]),
+       values=tensor([0.8501, 0.7899, 0.6223,  ..., 0.9437, 0.2014, 0.9727]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.5741, 0.3449, 0.6519,  ..., 0.7953, 0.3519, 0.0286])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 1.230255126953125 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '463602', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.329545974731445}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1248, 1249, 1250]),
+       col_indices=tensor([1727, 4803, 1040,  ..., 3710, 1053, 4648]),
+       values=tensor([0.0640, 0.8338, 0.2393,  ..., 0.0278, 0.9877, 0.3687]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.8593, 0.3881, 0.7226,  ..., 0.2122, 0.1433, 0.4534])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.329545974731445 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1248, 1249, 1250]),
+       col_indices=tensor([1727, 4803, 1040,  ..., 3710, 1053, 4648]),
+       values=tensor([0.0640, 0.8338, 0.2393,  ..., 0.0278, 0.9877, 0.3687]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.8593, 0.3881, 0.7226,  ..., 0.2122, 0.1433, 0.4534])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.329545974731445 seconds
+
+[40.79, 39.42, 39.16, 38.83, 39.21, 39.28, 39.68, 38.8, 39.35, 39.35]
+[95.11]
+12.598230361938477
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 463602, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.329545974731445, 'TIME_S_1KI': 0.02228106430673605, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1198.2176897239685, 'W': 95.11}
+[40.79, 39.42, 39.16, 38.83, 39.21, 39.28, 39.68, 38.8, 39.35, 39.35, 40.35, 38.9, 38.98, 39.04, 38.98, 40.01, 39.46, 39.09, 39.7, 39.17]
+707.72
+35.386
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 463602, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.329545974731445, 'TIME_S_1KI': 0.02228106430673605, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1198.2176897239685, 'W': 95.11, 'J_1KI': 2.5845826586683587, 'W_1KI': 0.20515442124926123, 'W_D': 59.724, 'J_D': 752.4167101364135, 'W_D_1KI': 0.12882601886963388, 'J_D_1KI': 0.00027788063655815524}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..c7b5a2b
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 33105, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.270436525344849, "TIME_S_1KI": 0.31023822761953934, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1243.494291753769, "W": 88.28, "J_1KI": 37.56212933858236, "W_1KI": 2.6666666666666665, "W_D": 71.58725, "J_D": 1008.3635788100362, "W_D_1KI": 2.1624301465035494, "J_D_1KI": 0.0653203487842788}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..31fde2c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.047393798828125}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      11,      25,  ...,  999973,
+                             999987, 1000000]),
+       col_indices=tensor([ 2538, 10020, 11588,  ..., 84720, 92719, 95287]),
+       values=tensor([0.8172, 0.5815, 0.2513,  ..., 0.2819, 0.8178, 0.1271]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9323, 0.3660, 0.2073,  ..., 0.7127, 0.8566, 0.0523])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.047393798828125 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22154', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.0265889167785645}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      11,      20,  ...,  999976,
+                             999985, 1000000]),
+       col_indices=tensor([10428, 14843, 15503,  ..., 86013, 91025, 96391]),
+       values=tensor([0.0148, 0.3731, 0.6426,  ..., 0.4125, 0.5086, 0.3848]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0982, 0.0147, 0.0440,  ..., 0.9267, 0.0489, 0.6248])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 7.0265889167785645 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33105', '-ss', '100000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.270436525344849}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      12,      18,  ...,  999974,
+                             999988, 1000000]),
+       col_indices=tensor([  697, 33076, 59577,  ..., 88840, 91058, 94574]),
+       values=tensor([0.8969, 0.3012, 0.5025,  ..., 0.5812, 0.6517, 0.5598]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0089, 0.5150, 0.8606,  ..., 0.9603, 0.9290, 0.1786])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.270436525344849 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      12,      18,  ...,  999974,
+                             999988, 1000000]),
+       col_indices=tensor([  697, 33076, 59577,  ..., 88840, 91058, 94574]),
+       values=tensor([0.8969, 0.3012, 0.5025,  ..., 0.5812, 0.6517, 0.5598]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0089, 0.5150, 0.8606,  ..., 0.9603, 0.9290, 0.1786])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.270436525344849 seconds
+
+[18.44, 17.49, 17.8, 17.92, 17.7, 20.94, 18.06, 17.75, 21.44, 18.07]
+[88.28]
+14.085798501968384
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33105, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.270436525344849, 'TIME_S_1KI': 0.31023822761953934, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1243.494291753769, 'W': 88.28}
+[18.44, 17.49, 17.8, 17.92, 17.7, 20.94, 18.06, 17.75, 21.44, 18.07, 18.18, 18.15, 17.95, 18.02, 21.88, 18.26, 18.15, 17.92, 17.92, 18.32]
+333.855
+16.69275
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33105, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.270436525344849, 'TIME_S_1KI': 0.31023822761953934, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1243.494291753769, 'W': 88.28, 'J_1KI': 37.56212933858236, 'W_1KI': 2.6666666666666665, 'W_D': 71.58725, 'J_D': 1008.3635788100362, 'W_D_1KI': 2.1624301465035494, 'J_D_1KI': 0.0653203487842788}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..8d42109
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2748, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.477163553237915, "TIME_S_1KI": 3.8126504924446563, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1424.6626942634582, "W": 81.1, "J_1KI": 518.4362060638495, "W_1KI": 29.512372634643373, "W_D": 64.67649999999999, "J_D": 1136.1553236193656, "W_D_1KI": 23.535844250363898, "J_D_1KI": 8.564717703916994}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..280d994
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.4098339080810547}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      105,      217,  ...,  9999774,
+                             9999878, 10000000]),
+       col_indices=tensor([ 2925,  3045,  3251,  ..., 98848, 99298, 99703]),
+       values=tensor([0.4813, 0.4380, 0.0490,  ..., 0.5758, 0.0326, 0.9259]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.0648, 0.1204, 0.6207,  ..., 0.1724, 0.6764, 0.4459])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 0.4098339080810547 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2562', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.787212610244751}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       90,      201,  ...,  9999780,
+                             9999895, 10000000]),
+       col_indices=tensor([ 1242,  4056,  4707,  ..., 96589, 97728, 98727]),
+       values=tensor([0.8897, 0.2716, 0.4760,  ..., 0.6356, 0.3047, 0.7796]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4134, 0.7359, 0.5031,  ..., 0.9568, 0.9528, 0.4063])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 9.787212610244751 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2748', '-ss', '100000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.477163553237915}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       82,      171,  ...,  9999801,
+                             9999897, 10000000]),
+       col_indices=tensor([ 1661,  2279,  2856,  ..., 99449, 99691, 99739]),
+       values=tensor([0.1663, 0.0376, 0.1009,  ..., 0.7118, 0.9261, 0.1836]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7724, 0.0559, 0.5235,  ..., 0.7708, 0.2517, 0.0642])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.477163553237915 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       82,      171,  ...,  9999801,
+                             9999897, 10000000]),
+       col_indices=tensor([ 1661,  2279,  2856,  ..., 99449, 99691, 99739]),
+       values=tensor([0.1663, 0.0376, 0.1009,  ..., 0.7118, 0.9261, 0.1836]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7724, 0.0559, 0.5235,  ..., 0.7708, 0.2517, 0.0642])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.477163553237915 seconds
+
+[18.37, 17.8, 17.96, 17.86, 22.53, 18.03, 17.75, 18.1, 18.23, 17.82]
+[81.1]
+17.56674098968506
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2748, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.477163553237915, 'TIME_S_1KI': 3.8126504924446563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1424.6626942634582, 'W': 81.1}
+[18.37, 17.8, 17.96, 17.86, 22.53, 18.03, 17.75, 18.1, 18.23, 17.82, 18.37, 17.83, 17.94, 18.39, 17.8, 17.92, 17.99, 17.94, 18.19, 17.86]
+328.47
+16.4235
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2748, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.477163553237915, 'TIME_S_1KI': 3.8126504924446563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1424.6626942634582, 'W': 81.1, 'J_1KI': 518.4362060638495, 'W_1KI': 29.512372634643373, 'W_D': 64.67649999999999, 'J_D': 1136.1553236193656, 'W_D_1KI': 23.535844250363898, 'J_D_1KI': 8.564717703916994}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..d39bedd
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 65044, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.857811450958252, "TIME_S_1KI": 0.1669302541503944, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1160.4457891416548, "W": 82.65999999999998, "J_1KI": 17.840935199890147, "W_1KI": 1.2708320521493142, "W_D": 66.37149999999998, "J_D": 931.7750749336478, "W_D_1KI": 1.0204092614230365, "J_D_1KI": 0.015687984463179334}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..a139e8c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.03411436080932617}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99997,  99998,
+                            100000]),
+       col_indices=tensor([61814, 31861, 93735,  ..., 37976, 26709, 88923]),
+       values=tensor([0.4964, 0.9275, 0.0463,  ..., 0.6388, 0.5613, 0.1901]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.9466, 0.9805, 0.4146,  ..., 0.4981, 0.9805, 0.0095])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.03411436080932617 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '30778', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.968464136123657}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ...,  99993,  99997,
+                            100000]),
+       col_indices=tensor([86302, 87189, 44148,  ...,  4090, 44893, 91495]),
+       values=tensor([0.5947, 0.5243, 0.4325,  ..., 0.8552, 0.8488, 0.7980]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2800, 0.7786, 0.6115,  ..., 0.5946, 0.9897, 0.5537])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 4.968464136123657 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '65044', '-ss', '100000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.857811450958252}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      2,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([27172, 43192, 23755,  ..., 52370, 88374,  3897]),
+       values=tensor([0.0211, 0.7600, 0.3262,  ..., 0.1220, 0.7210, 0.9662]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8254, 0.5550, 0.3634,  ..., 0.5298, 0.8710, 0.0274])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.857811450958252 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      2,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([27172, 43192, 23755,  ..., 52370, 88374,  3897]),
+       values=tensor([0.0211, 0.7600, 0.3262,  ..., 0.1220, 0.7210, 0.9662]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8254, 0.5550, 0.3634,  ..., 0.5298, 0.8710, 0.0274])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.857811450958252 seconds
+
+[18.23, 17.9, 18.0, 17.99, 18.15, 18.02, 18.22, 19.12, 18.05, 18.53]
+[82.66]
+14.038782835006714
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 65044, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.857811450958252, 'TIME_S_1KI': 0.1669302541503944, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1160.4457891416548, 'W': 82.65999999999998}
+[18.23, 17.9, 18.0, 17.99, 18.15, 18.02, 18.22, 19.12, 18.05, 18.53, 18.28, 17.9, 18.17, 17.79, 18.21, 17.83, 17.84, 17.79, 18.22, 18.1]
+325.77
+16.2885
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 65044, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.857811450958252, 'TIME_S_1KI': 0.1669302541503944, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1160.4457891416548, 'W': 82.65999999999998, 'J_1KI': 17.840935199890147, 'W_1KI': 1.2708320521493142, 'W_D': 66.37149999999998, 'J_D': 931.7750749336478, 'W_D_1KI': 1.0204092614230365, 'J_D_1KI': 0.015687984463179334}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..4c80d3b
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 45510, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.42077898979187, "TIME_S_1KI": 0.22897778487787013, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1214.5695349788666, "W": 87.39, "J_1KI": 26.687970445591443, "W_1KI": 1.920237310481213, "W_D": 71.04925, "J_D": 987.4614318926334, "W_D_1KI": 1.561178861788618, "J_D_1KI": 0.03430408397689778}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..d883b1c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.039800167083740234}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,     10,  ..., 499993, 499997,
+                            500000]),
+       col_indices=tensor([ 4828, 48889, 52503,  ..., 31911, 36084, 76746]),
+       values=tensor([0.4793, 0.0828, 0.1169,  ..., 0.5530, 0.3033, 0.0718]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.5059, 0.6298, 0.1664,  ..., 0.1879, 0.5431, 0.8952])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 0.039800167083740234 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '26381', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.086489200592041}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 499989, 499994,
+                            500000]),
+       col_indices=tensor([14519, 22983, 80951,  ..., 84187, 95762, 97051]),
+       values=tensor([0.1542, 0.8524, 0.3039,  ..., 0.4189, 0.6409, 0.4295]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.0352, 0.2725, 0.4170,  ..., 0.1491, 0.4370, 0.8032])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 6.086489200592041 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '45510', '-ss', '100000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.42077898979187}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     19,  ..., 499990, 499995,
+                            500000]),
+       col_indices=tensor([14786, 31808, 59751,  ..., 39791, 89593, 95677]),
+       values=tensor([0.6756, 0.3891, 0.0863,  ..., 0.6881, 0.4209, 0.2818]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.4948, 0.2234, 0.2049,  ..., 0.0447, 0.7948, 0.3022])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.42077898979187 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     19,  ..., 499990, 499995,
+                            500000]),
+       col_indices=tensor([14786, 31808, 59751,  ..., 39791, 89593, 95677]),
+       values=tensor([0.6756, 0.3891, 0.0863,  ..., 0.6881, 0.4209, 0.2818]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.4948, 0.2234, 0.2049,  ..., 0.0447, 0.7948, 0.3022])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.42077898979187 seconds
+
+[19.52, 19.27, 18.28, 17.93, 17.92, 18.18, 17.97, 18.17, 18.27, 18.17]
+[87.39]
+13.898266792297363
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 45510, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.42077898979187, 'TIME_S_1KI': 0.22897778487787013, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1214.5695349788666, 'W': 87.39}
+[19.52, 19.27, 18.28, 17.93, 17.92, 18.18, 17.97, 18.17, 18.27, 18.17, 18.18, 17.95, 17.9, 18.1, 18.19, 18.03, 17.88, 17.96, 18.05, 17.66]
+326.815
+16.34075
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 45510, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.42077898979187, 'TIME_S_1KI': 0.22897778487787013, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1214.5695349788666, 'W': 87.39, 'J_1KI': 26.687970445591443, 'W_1KI': 1.920237310481213, 'W_D': 71.04925, 'J_D': 987.4614318926334, 'W_D_1KI': 1.561178861788618, 'J_D_1KI': 0.03430408397689778}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..df907c2
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 251263, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.831458568572998, "TIME_S_1KI": 0.04310805239359952, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1035.0438996100424, "W": 74.32999999999998, "J_1KI": 4.119364568639403, "W_1KI": 0.2958254896264073, "W_D": 58.06174999999998, "J_D": 808.5088139134047, "W_D_1KI": 0.2310795859318721, "J_D_1KI": 0.0009196721599752933}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..bb6d734
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.019912242889404297}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([1836, 4826, 4334,  ..., 9720, 1658, 3253]),
+       values=tensor([0.6220, 0.1290, 0.9015,  ..., 0.3260, 0.3650, 0.7979]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.5720, 0.7293, 0.6280,  ..., 0.0388, 0.6575, 0.1842])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.019912242889404297 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52731', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.203561544418335}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     5,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([1066, 3027, 5018,  ...,  516, 4404, 8191]),
+       values=tensor([0.9408, 0.5840, 0.0232,  ..., 0.8231, 0.8506, 0.7636]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.2181, 0.5672, 0.6634,  ..., 0.2110, 0.3174, 0.6218])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 2.203561544418335 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '251263', '-ss', '10000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.831458568572998}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([6180, 2035, 3071,  ...,  490, 6496, 2315]),
+       values=tensor([0.2073, 0.5439, 0.2551,  ..., 0.7953, 0.4550, 0.0057]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.4967, 0.8117, 0.4603,  ..., 0.8210, 0.9832, 0.0501])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.831458568572998 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([6180, 2035, 3071,  ...,  490, 6496, 2315]),
+       values=tensor([0.2073, 0.5439, 0.2551,  ..., 0.7953, 0.4550, 0.0057]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.4967, 0.8117, 0.4603,  ..., 0.8210, 0.9832, 0.0501])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.831458568572998 seconds
+
+[18.25, 18.21, 18.06, 17.61, 18.2, 17.92, 18.19, 17.85, 18.22, 17.78]
+[74.33]
+13.924981832504272
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 251263, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.831458568572998, 'TIME_S_1KI': 0.04310805239359952, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1035.0438996100424, 'W': 74.32999999999998}
+[18.25, 18.21, 18.06, 17.61, 18.2, 17.92, 18.19, 17.85, 18.22, 17.78, 19.64, 17.79, 18.1, 18.15, 18.23, 17.78, 18.02, 18.14, 18.11, 17.9]
+325.365
+16.268250000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 251263, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.831458568572998, 'TIME_S_1KI': 0.04310805239359952, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1035.0438996100424, 'W': 74.32999999999998, 'J_1KI': 4.119364568639403, 'W_1KI': 0.2958254896264073, 'W_D': 58.06174999999998, 'J_D': 808.5088139134047, 'W_D_1KI': 0.2310795859318721, 'J_D_1KI': 0.0009196721599752933}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..610ea48
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 195195, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.530660390853882, "TIME_S_1KI": 0.05394943718258092, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1115.5897302937508, "W": 79.81, "J_1KI": 5.7152577181472415, "W_1KI": 0.40887317810394735, "W_D": 63.323, "J_D": 885.1332977244854, "W_D_1KI": 0.3244089244089244, "J_D_1KI": 0.0016619735362531027}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..d29559f
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.02070331573486328}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     12,     18,  ...,  99984,  99988,
+                            100000]),
+       col_indices=tensor([ 729,  732,  881,  ..., 6002, 8211, 9107]),
+       values=tensor([0.1473, 0.0535, 0.1985,  ..., 0.7529, 0.2592, 0.5040]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7970, 0.9066, 0.2901,  ..., 0.5249, 0.8444, 0.0204])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.02070331573486328 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '50716', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.728132724761963}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     15,     22,  ...,  99985,  99992,
+                            100000]),
+       col_indices=tensor([ 992, 2241, 2699,  ..., 7485, 9702, 9755]),
+       values=tensor([0.1587, 0.3354, 0.8907,  ..., 0.7458, 0.3952, 0.4445]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7531, 0.7793, 0.1410,  ..., 0.7186, 0.3031, 0.2892])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 2.728132724761963 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '195195', '-ss', '10000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.530660390853882}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     11,  ...,  99979,  99991,
+                            100000]),
+       col_indices=tensor([  74,  913, 1678,  ..., 8042, 8094, 8596]),
+       values=tensor([0.3009, 0.6152, 0.9919,  ..., 0.0065, 0.7111, 0.2350]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1186, 0.1112, 0.0471,  ..., 0.5653, 0.6270, 0.7376])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.530660390853882 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     11,  ...,  99979,  99991,
+                            100000]),
+       col_indices=tensor([  74,  913, 1678,  ..., 8042, 8094, 8596]),
+       values=tensor([0.3009, 0.6152, 0.9919,  ..., 0.0065, 0.7111, 0.2350]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1186, 0.1112, 0.0471,  ..., 0.5653, 0.6270, 0.7376])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.530660390853882 seconds
+
+[18.85, 21.66, 18.61, 18.04, 18.26, 18.19, 18.11, 17.83, 18.59, 17.86]
+[79.81]
+13.978069543838501
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 195195, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.530660390853882, 'TIME_S_1KI': 0.05394943718258092, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1115.5897302937508, 'W': 79.81}
+[18.85, 21.66, 18.61, 18.04, 18.26, 18.19, 18.11, 17.83, 18.59, 17.86, 18.27, 17.86, 18.11, 17.94, 18.2, 17.78, 18.07, 17.82, 17.85, 18.66]
+329.74
+16.487000000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 195195, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.530660390853882, 'TIME_S_1KI': 0.05394943718258092, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1115.5897302937508, 'W': 79.81, 'J_1KI': 5.7152577181472415, 'W_1KI': 0.40887317810394735, 'W_D': 63.323, 'J_D': 885.1332977244854, 'W_D_1KI': 0.3244089244089244, 'J_D_1KI': 0.0016619735362531027}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..9a2b891
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 58200, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.524534702301025, "TIME_S_1KI": 0.18083392959280112, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1230.7857535743713, "W": 87.63, "J_1KI": 21.147521539078543, "W_1KI": 1.5056701030927835, "W_D": 71.18625, "J_D": 999.8290807986259, "W_D_1KI": 1.223131443298969, "J_D_1KI": 0.02101600418039466}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..b0a27e2
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.032953500747680664}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      87,     187,  ...,  999811,
+                             999895, 1000000]),
+       col_indices=tensor([  60,  162,  170,  ..., 9440, 9828, 9931]),
+       values=tensor([0.9691, 0.2545, 0.9233,  ..., 0.5616, 0.3084, 0.5234]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0498, 0.1923, 0.6628,  ..., 0.9993, 0.6267, 0.7810])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.032953500747680664 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '31863', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 5.748403549194336}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     110,     213,  ...,  999801,
+                             999901, 1000000]),
+       col_indices=tensor([  77,  119,  129,  ..., 9737, 9950, 9990]),
+       values=tensor([0.4475, 0.9311, 0.1906,  ..., 0.1630, 0.9417, 0.6731]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2219, 0.3377, 0.8817,  ..., 0.6372, 0.3631, 0.6898])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 5.748403549194336 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '58200', '-ss', '10000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.524534702301025}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     116,     213,  ...,  999804,
+                             999907, 1000000]),
+       col_indices=tensor([  96,  100,  135,  ..., 9713, 9783, 9969]),
+       values=tensor([0.2374, 0.5111, 0.2281,  ..., 0.8006, 0.1634, 0.0785]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4187, 0.6286, 0.6485,  ..., 0.1996, 0.5955, 0.8769])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.524534702301025 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     116,     213,  ...,  999804,
+                             999907, 1000000]),
+       col_indices=tensor([  96,  100,  135,  ..., 9713, 9783, 9969]),
+       values=tensor([0.2374, 0.5111, 0.2281,  ..., 0.8006, 0.1634, 0.0785]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4187, 0.6286, 0.6485,  ..., 0.1996, 0.5955, 0.8769])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.524534702301025 seconds
+
+[18.15, 17.92, 18.14, 18.3, 18.24, 17.98, 17.94, 18.28, 17.96, 17.99]
+[87.63]
+14.045255661010742
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58200, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.524534702301025, 'TIME_S_1KI': 0.18083392959280112, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1230.7857535743713, 'W': 87.63}
+[18.15, 17.92, 18.14, 18.3, 18.24, 17.98, 17.94, 18.28, 17.96, 17.99, 18.28, 18.07, 17.88, 17.88, 17.9, 18.02, 21.74, 18.02, 18.41, 17.97]
+328.875
+16.44375
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 58200, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.524534702301025, 'TIME_S_1KI': 0.18083392959280112, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1230.7857535743713, 'W': 87.63, 'J_1KI': 21.147521539078543, 'W_1KI': 1.5056701030927835, 'W_D': 71.18625, 'J_D': 999.8290807986259, 'W_D_1KI': 1.223131443298969, 'J_D_1KI': 0.02101600418039466}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..6f3f4c5
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8756, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.44620156288147, "TIME_S_1KI": 1.193033527053617, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1333.577045211792, "W": 83.47, "J_1KI": 152.30436788622566, "W_1KI": 9.532891731384193, "W_D": 66.97425, "J_D": 1070.0290214481354, "W_D_1KI": 7.648955002284148, "J_D_1KI": 0.8735672684198433}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..e9fd20d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.14447855949401855}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     507,    1042,  ..., 4998981,
+                            4999514, 5000000]),
+       col_indices=tensor([   8,   38,   72,  ..., 9951, 9971, 9980]),
+       values=tensor([0.6058, 0.5976, 0.8000,  ..., 0.1658, 0.6430, 0.8003]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.9071, 0.4156, 0.9536,  ..., 0.8291, 0.1377, 0.0392])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 0.14447855949401855 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7267', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 8.714413404464722}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     473,     945,  ..., 4999048,
+                            4999511, 5000000]),
+       col_indices=tensor([   4,   25,   47,  ..., 9937, 9967, 9993]),
+       values=tensor([0.2180, 0.8351, 0.6646,  ..., 0.1409, 0.2302, 0.7325]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.9797, 0.6380, 0.6196,  ..., 0.0914, 0.5364, 0.9534])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 8.714413404464722 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8756', '-ss', '10000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.44620156288147}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     484,    1014,  ..., 4998952,
+                            4999472, 5000000]),
+       col_indices=tensor([  28,   62,   89,  ..., 9928, 9935, 9940]),
+       values=tensor([0.3908, 0.2484, 0.4500,  ..., 0.3668, 0.3711, 0.8718]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.8601, 0.7620, 0.0732,  ..., 0.0545, 0.3750, 0.1934])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.44620156288147 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     484,    1014,  ..., 4998952,
+                            4999472, 5000000]),
+       col_indices=tensor([  28,   62,   89,  ..., 9928, 9935, 9940]),
+       values=tensor([0.3908, 0.2484, 0.4500,  ..., 0.3668, 0.3711, 0.8718]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.8601, 0.7620, 0.0732,  ..., 0.0545, 0.3750, 0.1934])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.44620156288147 seconds
+
+[18.13, 18.04, 18.48, 17.86, 18.0, 18.3, 18.04, 17.84, 17.94, 18.14]
+[83.47]
+15.976722717285156
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8756, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.44620156288147, 'TIME_S_1KI': 1.193033527053617, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1333.577045211792, 'W': 83.47}
+[18.13, 18.04, 18.48, 17.86, 18.0, 18.3, 18.04, 17.84, 17.94, 18.14, 18.29, 18.21, 17.99, 18.15, 21.9, 18.49, 18.31, 17.94, 18.08, 18.13]
+329.91499999999996
+16.495749999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8756, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.44620156288147, 'TIME_S_1KI': 1.193033527053617, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1333.577045211792, 'W': 83.47, 'J_1KI': 152.30436788622566, 'W_1KI': 9.532891731384193, 'W_D': 66.97425, 'J_D': 1070.0290214481354, 'W_D_1KI': 7.648955002284148, 'J_D_1KI': 0.8735672684198433}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..646ac12
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2958, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.525683641433716, "TIME_S_1KI": 3.558378512993143, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1502.5020074129104, "W": 79.35, "J_1KI": 507.94523577177495, "W_1KI": 26.825557809330626, "W_D": 62.803999999999995, "J_D": 1189.2014628047941, "W_D_1KI": 21.231913455037187, "J_D_1KI": 7.177793595347258}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..cd10b80
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.38048672676086426}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      978,     2001,  ...,  9997917,
+                             9998993, 10000000]),
+       col_indices=tensor([  10,   12,   31,  ..., 9968, 9976, 9993]),
+       values=tensor([0.1521, 0.7718, 0.5784,  ..., 0.3138, 0.0420, 0.0283]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8445, 0.5776, 0.6277,  ..., 0.5230, 0.9454, 0.0151])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 0.38048672676086426 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2759', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.790999174118042}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      961,     1957,  ...,  9997943,
+                             9998964, 10000000]),
+       col_indices=tensor([   0,    2,   15,  ..., 9987, 9990, 9997]),
+       values=tensor([0.8878, 0.8149, 0.0468,  ..., 0.0944, 0.2051, 0.2941]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8720, 0.1434, 0.3774,  ..., 0.9472, 0.6076, 0.2537])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 9.790999174118042 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2958', '-ss', '10000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.525683641433716}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1002,     2060,  ...,  9998101,
+                             9999084, 10000000]),
+       col_indices=tensor([   6,   12,   22,  ..., 9993, 9996, 9999]),
+       values=tensor([0.4647, 0.6377, 0.7581,  ..., 0.1422, 0.4549, 0.7257]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2726, 0.9874, 0.6365,  ..., 0.6635, 0.0461, 0.2273])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.525683641433716 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1002,     2060,  ...,  9998101,
+                             9999084, 10000000]),
+       col_indices=tensor([   6,   12,   22,  ..., 9993, 9996, 9999]),
+       values=tensor([0.4647, 0.6377, 0.7581,  ..., 0.1422, 0.4549, 0.7257]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2726, 0.9874, 0.6365,  ..., 0.6635, 0.0461, 0.2273])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.525683641433716 seconds
+
+[18.46, 22.29, 18.08, 17.85, 18.08, 18.14, 17.95, 17.97, 18.17, 18.1]
+[79.35]
+18.935122966766357
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2958, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.525683641433716, 'TIME_S_1KI': 3.558378512993143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.5020074129104, 'W': 79.35}
+[18.46, 22.29, 18.08, 17.85, 18.08, 18.14, 17.95, 17.97, 18.17, 18.1, 18.4, 17.86, 18.25, 18.05, 17.97, 17.82, 20.01, 18.07, 17.96, 17.84]
+330.91999999999996
+16.546
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2958, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.525683641433716, 'TIME_S_1KI': 3.558378512993143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.5020074129104, 'W': 79.35, 'J_1KI': 507.94523577177495, 'W_1KI': 26.825557809330626, 'W_D': 62.803999999999995, 'J_D': 1189.2014628047941, 'W_D_1KI': 21.231913455037187, 'J_D_1KI': 7.177793595347258}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..600b3de
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1431, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.125213623046875, "TIME_S_1KI": 7.07562098046602, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1986.3526240086555, "W": 63.01, "J_1KI": 1388.087088755175, "W_1KI": 44.032145352900066, "W_D": 46.759, "J_D": 1474.0495531823635, "W_D_1KI": 32.67575122292103, "J_D_1KI": 22.83420770294971}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..b3d4b08
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.7335808277130127}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1938,     3929,  ..., 19996007,
+                            19998018, 20000000]),
+       col_indices=tensor([  10,   23,   25,  ..., 9992, 9994, 9995]),
+       values=tensor([0.0730, 0.5628, 0.7699,  ..., 0.2806, 0.9097, 0.3889]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.2309, 0.6404, 0.8370,  ..., 0.6670, 0.0943, 0.6898])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 0.7335808277130127 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1431', '-ss', '10000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.125213623046875}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2012,     4019,  ..., 19995967,
+                            19998000, 20000000]),
+       col_indices=tensor([   4,    7,    8,  ..., 9979, 9980, 9988]),
+       values=tensor([0.3444, 0.9651, 0.7506,  ..., 0.6074, 0.5252, 0.1862]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.7493, 0.9404, 0.6976,  ..., 0.3307, 0.3774, 0.7329])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.125213623046875 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2012,     4019,  ..., 19995967,
+                            19998000, 20000000]),
+       col_indices=tensor([   4,    7,    8,  ..., 9979, 9980, 9988]),
+       values=tensor([0.3444, 0.9651, 0.7506,  ..., 0.6074, 0.5252, 0.1862]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.7493, 0.9404, 0.6976,  ..., 0.3307, 0.3774, 0.7329])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.125213623046875 seconds
+
+[18.03, 17.87, 18.44, 17.79, 18.02, 17.71, 17.95, 17.85, 17.83, 17.74]
+[63.01]
+31.524402856826782
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1431, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.125213623046875, 'TIME_S_1KI': 7.07562098046602, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1986.3526240086555, 'W': 63.01}
+[18.03, 17.87, 18.44, 17.79, 18.02, 17.71, 17.95, 17.85, 17.83, 17.74, 18.68, 17.68, 18.59, 19.66, 17.91, 17.67, 18.09, 18.04, 17.79, 17.81]
+325.02
+16.250999999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1431, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.125213623046875, 'TIME_S_1KI': 7.07562098046602, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1986.3526240086555, 'W': 63.01, 'J_1KI': 1388.087088755175, 'W_1KI': 44.032145352900066, 'W_D': 46.759, 'J_D': 1474.0495531823635, 'W_D_1KI': 32.67575122292103, 'J_D_1KI': 22.83420770294971}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..913ab31
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 887, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.198672533035278, "TIME_S_1KI": 11.497939721573031, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3521.5318365383146, "W": 51.94, "J_1KI": 3970.1599059056534, "W_1KI": 58.55693348365276, "W_D": 35.649249999999995, "J_D": 2417.019037807345, "W_D_1KI": 40.19081172491544, "J_D_1KI": 45.31094895706363}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..697edf6
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.9959039688110352}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2964,     5964,  ..., 29994054,
+                            29997049, 30000000]),
+       col_indices=tensor([   0,    6,    7,  ..., 9989, 9993, 9996]),
+       values=tensor([0.3352, 0.3012, 0.1376,  ..., 0.4634, 0.9038, 0.2157]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.3407, 0.2089, 0.1462,  ..., 0.7488, 0.0030, 0.5159])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 1.9959039688110352 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '526', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 7.3105573654174805}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2980,     5935,  ..., 29993957,
+                            29996987, 30000000]),
+       col_indices=tensor([   2,    4,    5,  ..., 9985, 9987, 9990]),
+       values=tensor([0.7275, 0.2529, 0.2202,  ..., 0.8048, 0.1786, 0.5578]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.3722, 0.8340, 0.1775,  ..., 0.2787, 0.3419, 0.3614])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 7.3105573654174805 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '755', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 8.937042713165283}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3082,     6089,  ..., 29994069,
+                            29996991, 30000000]),
+       col_indices=tensor([   3,    5,    7,  ..., 9989, 9990, 9999]),
+       values=tensor([0.1400, 0.4959, 0.3565,  ..., 0.5786, 0.8662, 0.8079]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.3795, 0.0549, 0.6386,  ..., 0.9156, 0.7490, 0.5099])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 8.937042713165283 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '887', '-ss', '10000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.198672533035278}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3035,     5994,  ..., 29994113,
+                            29997092, 30000000]),
+       col_indices=tensor([   5,   10,   14,  ..., 9987, 9996, 9999]),
+       values=tensor([0.8098, 0.4554, 0.6671,  ..., 0.4349, 0.8044, 0.2223]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.3423, 0.6279, 0.0055,  ..., 0.7400, 0.5417, 0.1422])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.198672533035278 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3035,     5994,  ..., 29994113,
+                            29997092, 30000000]),
+       col_indices=tensor([   5,   10,   14,  ..., 9987, 9996, 9999]),
+       values=tensor([0.8098, 0.4554, 0.6671,  ..., 0.4349, 0.8044, 0.2223]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.3423, 0.6279, 0.0055,  ..., 0.7400, 0.5417, 0.1422])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.198672533035278 seconds
+
+[18.62, 17.81, 18.39, 18.03, 17.98, 18.26, 18.01, 18.0, 17.9, 17.68]
+[51.94]
+67.79999685287476
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 887, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.198672533035278, 'TIME_S_1KI': 11.497939721573031, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3521.5318365383146, 'W': 51.94}
+[18.62, 17.81, 18.39, 18.03, 17.98, 18.26, 18.01, 18.0, 17.9, 17.68, 18.52, 17.91, 18.3, 18.31, 18.44, 18.04, 17.96, 18.17, 17.99, 17.81]
+325.815
+16.29075
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 887, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.198672533035278, 'TIME_S_1KI': 11.497939721573031, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3521.5318365383146, 'W': 51.94, 'J_1KI': 3970.1599059056534, 'W_1KI': 58.55693348365276, 'W_D': 35.649249999999995, 'J_D': 2417.019037807345, 'W_D_1KI': 40.19081172491544, 'J_D_1KI': 45.31094895706363}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..4a5c029
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 285101, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.263301372528076, "TIME_S_1KI": 0.03599882628446788, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1023.6102543663977, "W": 73.21, "J_1KI": 3.5903425605886956, "W_1KI": 0.2567861915601839, "W_D": 56.916999999999994, "J_D": 795.8041913368701, "W_D_1KI": 0.1996380230164047, "J_D_1KI": 0.0007002361374264022}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..aea26a1
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1307 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.019815444946289062}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([ 147,  628, 1125, 5287, 8823, 6934, 2121, 9045, 1741,
+                           1008,  777, 1781, 8765, 5338, 5590, 4011, 9135, 3712,
+                           8048, 6348, 8740, 9035,  822, 3133, 9984, 8122, 9554,
+                           3712, 3272, 5515, 8959, 6829,   83, 3899, 7199, 7801,
+                           5285, 8079, 5441, 7100, 7667, 5056, 6862, 2526, 7349,
+                           1728, 3499, 3354, 7526, 5044, 3630, 6886, 4310, 5869,
+                           2649, 6497, 3797, 8787, 1590, 3717,  600, 9128, 9514,
+                            219, 9480, 7496, 7514, 5942, 3564, 2833, 1156, 3271,
+                           9892, 8323, 4817, 8727, 4029, 1225,  635, 3756, 3854,
+                           1580, 9309, 2348, 2287, 6728, 2416, 8735, 1996, 9982,
+                           2260, 9743, 6241, 6259, 3363, 2128, 9721, 8593, 9287,
+                           6303, 6167, 7208, 4450, 1275, 2370, 2881, 3104, 7161,
+                           9133,  226, 5026, 8446, 4133, 9547, 9259, 5653, 4323,
+                           9414, 5933, 1536, 7990, 6794, 7612, 5173, 6568, 8498,
+                           2655, 2146, 8320, 4457, 6510,  276, 9447, 6969, 2617,
+                           7175, 1400, 1929,  524,  929,  472, 5246, 4656, 4620,
+                            489, 4946,  342,  261, 5266, 9154, 4961,  841, 6798,
+                           6887, 9276,  203, 2231, 9861, 5260, 5410, 4989, 3777,
+                           5518, 2156,  807, 5251, 7532, 8301, 8516, 2536, 7883,
+                           4202, 2101, 5422, 6588,  505, 5736, 1011, 9457,  514,
+                           9520,  284, 2054, 2592, 5274, 6220, 4951, 8010, 3966,
+                           4007, 1997, 1966, 7251, 3207, 5838, 9132, 4982, 4877,
+                           8655, 8284,  876, 5569,  757, 2680, 1245, 1459,   64,
+                            365, 4927, 7024, 7351,  643, 3222, 4840, 6222, 7194,
+                           1143, 9570, 6110,  514, 6176, 3515,  236,   14, 9751,
+                           3159, 5832, 1147, 6202, 9999, 7112, 2023, 1940, 2795,
+                           6251, 8906, 5208, 3243, 7604, 6854, 2354, 2890, 2740,
+                            454, 4997, 9488, 8203, 5802,   49, 4850, 8249, 7823,
+                           1150, 8721, 9200, 1431, 3413, 5962,  300, 7898, 4190,
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+                      3.0281e-01, 7.1212e-01, 7.5584e-01, 8.5415e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7030, 0.4856, 0.8302,  ..., 0.1030, 0.0064, 0.5187])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.019815444946289062 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52988', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.9514954090118408}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      7.5053e-02, 7.9242e-01, 2.9142e-01, 5.4645e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.6534, 0.1395, 0.5723,  ..., 0.2846, 0.6527, 0.4839])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 1.9514954090118408 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '285101', '-ss', '10000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.263301372528076}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.5084, 0.2519, 0.6662, 0.9841, 0.4845, 0.3961]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4905, 0.9858, 0.4305,  ..., 0.2183, 0.4525, 0.0992])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.263301372528076 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.3894, 0.5017, 0.4405, 0.3895, 0.3989, 0.4152, 0.3842,
+                      0.2309, 0.0256, 0.1505, 0.6988, 0.2384, 0.4692, 0.2583,
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+                      0.0102, 0.2013, 0.8005, 0.6998, 0.2945, 0.4318, 0.5007,
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+                      0.1399, 0.1801, 0.9233, 0.2772, 0.2445, 0.0531, 0.7452,
+                      0.0969, 0.7391, 0.2375, 0.8335, 0.2274, 0.5999, 0.0299,
+                      0.1018, 0.7622, 0.6825, 0.5068, 0.5262, 0.2390, 0.3450,
+                      0.7486, 0.6337, 0.3874, 0.1886, 0.2751, 0.8412, 0.0794,
+                      0.2398, 0.8937, 0.9405, 0.8703, 0.2503, 0.9532, 0.7538,
+                      0.3676, 0.7234, 0.4557, 0.9672, 0.5687, 0.6227, 0.7700,
+                      0.6581, 0.6226, 0.6058, 0.8459, 0.4844, 0.8195, 0.3510,
+                      0.5321, 0.7146, 0.4273, 0.4330, 0.8640, 0.8088, 0.5159,
+                      0.8375, 0.9589, 0.0615, 0.2675, 0.6890, 0.9951, 0.0718,
+                      0.5084, 0.2519, 0.6662, 0.9841, 0.4845, 0.3961]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4905, 0.9858, 0.4305,  ..., 0.2183, 0.4525, 0.0992])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.263301372528076 seconds
+
+[18.52, 17.73, 17.82, 17.87, 17.98, 18.03, 17.91, 19.05, 18.64, 18.02]
+[73.21]
+13.981836557388306
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 285101, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.263301372528076, 'TIME_S_1KI': 0.03599882628446788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1023.6102543663977, 'W': 73.21}
+[18.52, 17.73, 17.82, 17.87, 17.98, 18.03, 17.91, 19.05, 18.64, 18.02, 18.85, 18.33, 18.07, 17.99, 17.95, 17.9, 17.98, 18.04, 17.93, 17.89]
+325.86
+16.293
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 285101, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.263301372528076, 'TIME_S_1KI': 0.03599882628446788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1023.6102543663977, 'W': 73.21, 'J_1KI': 3.5903425605886956, 'W_1KI': 0.2567861915601839, 'W_D': 56.916999999999994, 'J_D': 795.8041913368701, 'W_D_1KI': 0.1996380230164047, 'J_D_1KI': 0.0007002361374264022}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..0ec114e
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 259324, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.257395267486572, "TIME_S_1KI": 0.03955436159972302, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1007.3763615226745, "W": 73.52, "J_1KI": 3.8846244910716883, "W_1KI": 0.28350634727213836, "W_D": 56.727999999999994, "J_D": 777.2911620845794, "W_D_1KI": 0.21875337415742466, "J_D_1KI": 0.000843552367530289}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..838ccfc
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.021072864532470703}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 4999, 4999, 5000]),
+       col_indices=tensor([6834, 1931, 4346,  ..., 6725, 2972, 1681]),
+       values=tensor([0.7465, 0.7749, 0.3553,  ..., 0.3449, 0.2710, 0.4644]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.4472, 0.0239, 0.4773,  ..., 0.7523, 0.1836, 0.2389])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.021072864532470703 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '49827', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.0174834728240967}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([6306, 9296, 8522,  ..., 5641, 7164, 5943]),
+       values=tensor([0.9605, 0.0866, 0.1892,  ..., 0.4816, 0.2836, 0.0365]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.7120, 0.8205, 0.0862,  ..., 0.5109, 0.7192, 0.7608])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 2.0174834728240967 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '259324', '-ss', '10000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.257395267486572}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([2130, 3883, 5256,  ..., 7000, 8219, 5132]),
+       values=tensor([0.8393, 0.8650, 0.4056,  ..., 0.4895, 0.0562, 0.7603]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.9495, 0.1374, 0.1837,  ..., 0.6231, 0.7099, 0.0387])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.257395267486572 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([2130, 3883, 5256,  ..., 7000, 8219, 5132]),
+       values=tensor([0.8393, 0.8650, 0.4056,  ..., 0.4895, 0.0562, 0.7603]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.9495, 0.1374, 0.1837,  ..., 0.6231, 0.7099, 0.0387])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.257395267486572 seconds
+
+[18.17, 18.06, 17.92, 18.22, 18.12, 18.42, 22.11, 19.11, 18.46, 18.0]
+[73.52]
+13.702072381973267
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 259324, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.257395267486572, 'TIME_S_1KI': 0.03955436159972302, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1007.3763615226745, 'W': 73.52}
+[18.17, 18.06, 17.92, 18.22, 18.12, 18.42, 22.11, 19.11, 18.46, 18.0, 18.1, 18.11, 18.16, 22.77, 18.06, 18.3, 17.88, 17.93, 18.0, 18.15]
+335.84000000000003
+16.792
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 259324, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.257395267486572, 'TIME_S_1KI': 0.03955436159972302, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1007.3763615226745, 'W': 73.52, 'J_1KI': 3.8846244910716883, 'W_1KI': 0.28350634727213836, 'W_D': 56.727999999999994, 'J_D': 777.2911620845794, 'W_D_1KI': 0.21875337415742466, 'J_D_1KI': 0.000843552367530289}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..da0856a
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 665, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.126577615737915, "TIME_S_1KI": 15.22793626426754, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3144.6024104833605, "W": 46.7, "J_1KI": 4728.725429298286, "W_1KI": 70.22556390977444, "W_D": 20.655250000000002, "J_D": 1390.846872358382, "W_D_1KI": 31.060526315789478, "J_D_1KI": 46.707558369608236}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..4beed36
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.0304152965545654}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       59,       94,  ..., 24999900,
+                            24999951, 25000000]),
+       col_indices=tensor([  1276,  15885,  34398,  ..., 446460, 484343,
+                           488114]),
+       values=tensor([0.3408, 0.7505, 0.4683,  ..., 0.6426, 0.2990, 0.1628]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.4984, 0.2348, 0.7546,  ..., 0.4897, 0.9555, 0.5266])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 2.0304152965545654 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '517', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.159608364105225}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       46,       98,  ..., 24999895,
+                            24999952, 25000000]),
+       col_indices=tensor([  1859,  10480,  11583,  ..., 471819, 483100,
+                           486034]),
+       values=tensor([0.5566, 0.4872, 0.1210,  ..., 0.2476, 0.9480, 0.3070]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.1091, 0.0853, 0.9295,  ..., 0.2076, 0.8766, 0.1664])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 8.159608364105225 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '665', '-ss', '500000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.126577615737915}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       66,      119,  ..., 24999902,
+                            24999949, 25000000]),
+       col_indices=tensor([  3829,  12709,  24306,  ..., 491038, 494248,
+                           495364]),
+       values=tensor([0.8354, 0.9747, 0.5569,  ..., 0.5257, 0.7884, 0.2877]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.8209, 0.7651, 0.4978,  ..., 0.6892, 0.4643, 0.4864])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.126577615737915 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       66,      119,  ..., 24999902,
+                            24999949, 25000000]),
+       col_indices=tensor([  3829,  12709,  24306,  ..., 491038, 494248,
+                           495364]),
+       values=tensor([0.8354, 0.9747, 0.5569,  ..., 0.5257, 0.7884, 0.2877]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.8209, 0.7651, 0.4978,  ..., 0.6892, 0.4643, 0.4864])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.126577615737915 seconds
+
+[18.36, 18.09, 18.07, 17.97, 17.94, 18.13, 18.13, 17.77, 17.92, 18.01]
+[46.7]
+67.33624005317688
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 665, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.126577615737915, 'TIME_S_1KI': 15.22793626426754, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3144.6024104833605, 'W': 46.7}
+[18.36, 18.09, 18.07, 17.97, 17.94, 18.13, 18.13, 17.77, 17.92, 18.01, 39.86, 40.69, 39.54, 40.28, 39.05, 39.85, 39.62, 39.36, 40.17, 40.4]
+520.895
+26.04475
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 665, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.126577615737915, 'TIME_S_1KI': 15.22793626426754, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3144.6024104833605, 'W': 46.7, 'J_1KI': 4728.725429298286, 'W_1KI': 70.22556390977444, 'W_D': 20.655250000000002, 'J_D': 1390.846872358382, 'W_D_1KI': 31.060526315789478, 'J_D_1KI': 46.707558369608236}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..ac8e083
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8088, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.521591186523438, "TIME_S_1KI": 1.30088911801724, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1272.5733924508095, "W": 87.25, "J_1KI": 157.34092389352244, "W_1KI": 10.787586547972305, "W_D": 70.764, "J_D": 1032.1190090932846, "W_D_1KI": 8.749258160237389, "J_D_1KI": 1.0817579327692124}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..a65a620
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.14249539375305176}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,       6,  ..., 2499988,
+                            2499994, 2500000]),
+       col_indices=tensor([159074, 199303, 338786,  ..., 336877, 376694,
+                           404714]),
+       values=tensor([0.7251, 0.9700, 0.9965,  ..., 0.4798, 0.8363, 0.5285]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5063, 0.7490, 0.8579,  ..., 0.3117, 0.7674, 0.7165])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 0.14249539375305176 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7368', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.564131021499634}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       7,      11,  ..., 2499988,
+                            2499994, 2500000]),
+       col_indices=tensor([ 45977,  46883, 132654,  ..., 283974, 337716,
+                           438050]),
+       values=tensor([0.6941, 0.4659, 0.2903,  ..., 0.1328, 0.8033, 0.9427]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8274, 0.6187, 0.7071,  ..., 0.9433, 0.5745, 0.3570])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 9.564131021499634 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8088', '-ss', '500000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.521591186523438}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      11,  ..., 2499988,
+                            2499995, 2500000]),
+       col_indices=tensor([ 55151,  55855, 262240,  ..., 129037, 280325,
+                           497898]),
+       values=tensor([0.5548, 0.3291, 0.4545,  ..., 0.6191, 0.2200, 0.6842]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8740, 0.8697, 0.8262,  ..., 0.4420, 0.1114, 0.8177])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.521591186523438 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      11,  ..., 2499988,
+                            2499995, 2500000]),
+       col_indices=tensor([ 55151,  55855, 262240,  ..., 129037, 280325,
+                           497898]),
+       values=tensor([0.5548, 0.3291, 0.4545,  ..., 0.6191, 0.2200, 0.6842]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8740, 0.8697, 0.8262,  ..., 0.4420, 0.1114, 0.8177])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.521591186523438 seconds
+
+[19.45, 17.88, 18.22, 21.5, 18.64, 18.15, 18.16, 17.8, 17.97, 17.95]
+[87.25]
+14.585368394851685
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8088, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.521591186523438, 'TIME_S_1KI': 1.30088911801724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1272.5733924508095, 'W': 87.25}
+[19.45, 17.88, 18.22, 21.5, 18.64, 18.15, 18.16, 17.8, 17.97, 17.95, 18.5, 17.88, 17.95, 18.22, 18.0, 18.18, 18.21, 18.1, 18.01, 17.8]
+329.72
+16.486
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8088, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.521591186523438, 'TIME_S_1KI': 1.30088911801724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1272.5733924508095, 'W': 87.25, 'J_1KI': 157.34092389352244, 'W_1KI': 10.787586547972305, 'W_D': 70.764, 'J_D': 1032.1190090932846, 'W_D_1KI': 8.749258160237389, 'J_D_1KI': 1.0817579327692124}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..17825c9
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1356, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.575294256210327, "TIME_S_1KI": 7.798889569476643, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1502.6908141350748, "W": 76.43, "J_1KI": 1108.179066471294, "W_1KI": 56.36430678466077, "W_D": 60.11625000000001, "J_D": 1181.9460507032277, "W_D_1KI": 44.333517699115056, "J_D_1KI": 32.694334586368036}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..b27b9d0
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.7741575241088867}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       27,       54,  ..., 12499941,
+                            12499975, 12500000]),
+       col_indices=tensor([ 19879,  19996,  22547,  ..., 457855, 459779,
+                           462945]),
+       values=tensor([0.0262, 0.3741, 0.0922,  ..., 0.5524, 0.1014, 0.8276]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.7209, 0.9008, 0.7814,  ..., 0.2206, 0.4926, 0.1534])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 0.7741575241088867 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1356', '-ss', '500000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.575294256210327}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       30,       59,  ..., 12499949,
+                            12499973, 12500000]),
+       col_indices=tensor([ 13892,  45461,  46784,  ..., 469557, 488276,
+                           489508]),
+       values=tensor([0.8469, 0.7554, 0.2394,  ..., 0.6309, 0.5261, 0.2516]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9763, 0.3043, 0.0965,  ..., 0.1822, 0.5455, 0.4604])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.575294256210327 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       30,       59,  ..., 12499949,
+                            12499973, 12500000]),
+       col_indices=tensor([ 13892,  45461,  46784,  ..., 469557, 488276,
+                           489508]),
+       values=tensor([0.8469, 0.7554, 0.2394,  ..., 0.6309, 0.5261, 0.2516]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9763, 0.3043, 0.0965,  ..., 0.1822, 0.5455, 0.4604])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.575294256210327 seconds
+
+[18.28, 17.9, 18.0, 17.69, 18.19, 17.78, 17.99, 17.71, 17.86, 17.72]
+[76.43]
+19.66100764274597
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1356, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.575294256210327, 'TIME_S_1KI': 7.798889569476643, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.6908141350748, 'W': 76.43}
+[18.28, 17.9, 18.0, 17.69, 18.19, 17.78, 17.99, 17.71, 17.86, 17.72, 22.73, 18.23, 18.33, 18.05, 18.19, 17.82, 18.25, 18.16, 17.83, 17.86]
+326.275
+16.31375
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1356, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.575294256210327, 'TIME_S_1KI': 7.798889569476643, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.6908141350748, 'W': 76.43, 'J_1KI': 1108.179066471294, 'W_1KI': 56.36430678466077, 'W_D': 60.11625000000001, 'J_D': 1181.9460507032277, 'W_D_1KI': 44.333517699115056, 'J_D_1KI': 32.694334586368036}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..8609bc2
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 80207, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.527606010437012, "TIME_S_1KI": 0.13125545164932004, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1181.3196375966072, "W": 83.35, "J_1KI": 14.728385771773127, "W_1KI": 1.039186105950852, "W_D": 67.15325, "J_D": 951.7630828246474, "W_D_1KI": 0.8372492425848118, "J_D_1KI": 0.010438605640216089}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..381203d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.03568005561828613}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      9,  ..., 249987, 249994,
+                            250000]),
+       col_indices=tensor([ 1312, 19953, 25282,  ..., 26652, 33001, 38879]),
+       values=tensor([0.3658, 0.9367, 0.4335,  ..., 0.7027, 0.8564, 0.9906]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4942, 0.6881, 0.2872,  ..., 0.3001, 0.6556, 0.7300])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.03568005561828613 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '29428', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 3.8524389266967773}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,      8,  ..., 249998, 250000,
+                            250000]),
+       col_indices=tensor([ 4346,  9215, 13661,  ..., 37674, 16332, 22572]),
+       values=tensor([0.5552, 0.4398, 0.7001,  ..., 0.6234, 0.7005, 0.0878]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5304, 0.8900, 0.0447,  ..., 0.3418, 0.1958, 0.2486])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 3.8524389266967773 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '80207', '-ss', '50000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.527606010437012}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     19,  ..., 249993, 249998,
+                            250000]),
+       col_indices=tensor([ 8045, 12111, 14477,  ..., 47402, 12160, 19361]),
+       values=tensor([0.9649, 0.3819, 0.5636,  ..., 0.2633, 0.1370, 0.0196]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0927, 0.4142, 0.0895,  ..., 0.8219, 0.5339, 0.8064])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.527606010437012 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     19,  ..., 249993, 249998,
+                            250000]),
+       col_indices=tensor([ 8045, 12111, 14477,  ..., 47402, 12160, 19361]),
+       values=tensor([0.9649, 0.3819, 0.5636,  ..., 0.2633, 0.1370, 0.0196]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0927, 0.4142, 0.0895,  ..., 0.8219, 0.5339, 0.8064])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.527606010437012 seconds
+
+[18.32, 17.98, 17.86, 18.86, 18.68, 17.81, 17.68, 18.11, 17.76, 17.85]
+[83.35]
+14.173001050949097
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 80207, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.527606010437012, 'TIME_S_1KI': 0.13125545164932004, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1181.3196375966072, 'W': 83.35}
+[18.32, 17.98, 17.86, 18.86, 18.68, 17.81, 17.68, 18.11, 17.76, 17.85, 18.01, 18.02, 17.88, 17.81, 17.97, 17.93, 17.96, 17.74, 17.82, 17.95]
+323.93499999999995
+16.196749999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 80207, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.527606010437012, 'TIME_S_1KI': 0.13125545164932004, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1181.3196375966072, 'W': 83.35, 'J_1KI': 14.728385771773127, 'W_1KI': 1.039186105950852, 'W_D': 67.15325, 'J_D': 951.7630828246474, 'W_D_1KI': 0.8372492425848118, 'J_D_1KI': 0.010438605640216089}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..1ef0389
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 17086, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.693793773651123, "TIME_S_1KI": 0.6258804737007564, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1274.9557392597198, "W": 87.4, "J_1KI": 74.61990748330328, "W_1KI": 5.1152990752663, "W_D": 70.92850000000001, "J_D": 1034.6761802297833, "W_D_1KI": 4.151264192906474, "J_D_1KI": 0.24296290488742092}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..168fd00
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,86 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.07488131523132324}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      64,     108,  ..., 2499910,
+                            2499955, 2500000]),
+       col_indices=tensor([  984,  1625,  1972,  ..., 46651, 48149, 48861]),
+       values=tensor([5.1121e-01, 5.6272e-01, 1.1145e-04,  ...,
+                      9.0355e-01, 4.1789e-01, 4.2355e-01]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3839, 0.9792, 0.8841,  ..., 0.7211, 0.2437, 0.3590])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.07488131523132324 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '14022', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.616644144058228}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      54,     105,  ..., 2499907,
+                            2499960, 2500000]),
+       col_indices=tensor([  369,  1157,  3425,  ..., 45077, 46820, 49764]),
+       values=tensor([0.6429, 0.4063, 0.5775,  ..., 0.7664, 0.6925, 0.8507]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4381, 0.1550, 0.0791,  ..., 0.0177, 0.9903, 0.0608])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 8.616644144058228 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '17086', '-ss', '50000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.693793773651123}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      52,      91,  ..., 2499883,
+                            2499936, 2500000]),
+       col_indices=tensor([  188,  2361,  2646,  ..., 48274, 48923, 49377]),
+       values=tensor([0.2734, 0.1056, 0.9298,  ..., 0.4005, 0.9270, 0.9473]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1965, 0.7813, 0.8576,  ..., 0.6695, 0.1581, 0.1443])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.693793773651123 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      52,      91,  ..., 2499883,
+                            2499936, 2500000]),
+       col_indices=tensor([  188,  2361,  2646,  ..., 48274, 48923, 49377]),
+       values=tensor([0.2734, 0.1056, 0.9298,  ..., 0.4005, 0.9270, 0.9473]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1965, 0.7813, 0.8576,  ..., 0.6695, 0.1581, 0.1443])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.693793773651123 seconds
+
+[18.43, 18.16, 17.96, 18.08, 18.33, 18.04, 18.36, 18.18, 18.22, 18.06]
+[87.4]
+14.587594270706177
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17086, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.693793773651123, 'TIME_S_1KI': 0.6258804737007564, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.9557392597198, 'W': 87.4}
+[18.43, 18.16, 17.96, 18.08, 18.33, 18.04, 18.36, 18.18, 18.22, 18.06, 18.48, 17.93, 19.19, 18.85, 18.16, 18.04, 18.63, 18.25, 18.43, 18.27]
+329.43
+16.4715
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17086, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.693793773651123, 'TIME_S_1KI': 0.6258804737007564, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.9557392597198, 'W': 87.4, 'J_1KI': 74.61990748330328, 'W_1KI': 5.1152990752663, 'W_D': 70.92850000000001, 'J_D': 1034.6761802297833, 'W_D_1KI': 4.151264192906474, 'J_D_1KI': 0.24296290488742092}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..6f4838a
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1159, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.524485111236572, "TIME_S_1KI": 9.080660147745101, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2883.053437728882, "W": 54.72, "J_1KI": 2487.5353215952387, "W_1KI": 47.21311475409836, "W_D": 38.2975, "J_D": 2017.7949384397268, "W_D_1KI": 33.043572044866266, "J_D_1KI": 28.51041591446615}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..c966aa4
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 1.2965989112854004}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      462,      963,  ..., 24999009,
+                            24999507, 25000000]),
+       col_indices=tensor([   19,    54,    59,  ..., 49770, 49789, 49840]),
+       values=tensor([0.0062, 0.3047, 0.0339,  ..., 0.6533, 0.8264, 0.4065]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.0572, 0.3375, 0.5398,  ..., 0.2388, 0.0349, 0.7555])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 1.2965989112854004 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '809', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.848743677139282}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      486,      980,  ..., 24998984,
+                            24999490, 25000000]),
+       col_indices=tensor([   15,   197,   386,  ..., 49782, 49793, 49889]),
+       values=tensor([0.3923, 0.3887, 0.8681,  ..., 0.2288, 0.1762, 0.4981]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.2965, 0.2935, 0.4053,  ..., 0.9117, 0.1428, 0.4127])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 7.848743677139282 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1082', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.800410747528076}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      497,      982,  ..., 24999036,
+                            24999517, 25000000]),
+       col_indices=tensor([   46,   129,   426,  ..., 49653, 49766, 49830]),
+       values=tensor([0.6195, 0.6207, 0.9497,  ..., 0.4637, 0.0557, 0.5508]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.1589, 0.3084, 0.3696,  ..., 0.3780, 0.7461, 0.4084])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 9.800410747528076 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1159', '-ss', '50000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.524485111236572}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      513,     1013,  ..., 24998968,
+                            24999473, 25000000]),
+       col_indices=tensor([   48,   124,   131,  ..., 49410, 49843, 49893]),
+       values=tensor([0.3835, 0.3241, 0.0409,  ..., 0.5767, 0.7491, 0.8402]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3332, 0.0589, 0.5895,  ..., 0.7200, 0.0490, 0.9504])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.524485111236572 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      513,     1013,  ..., 24998968,
+                            24999473, 25000000]),
+       col_indices=tensor([   48,   124,   131,  ..., 49410, 49843, 49893]),
+       values=tensor([0.3835, 0.3241, 0.0409,  ..., 0.5767, 0.7491, 0.8402]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3332, 0.0589, 0.5895,  ..., 0.7200, 0.0490, 0.9504])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.524485111236572 seconds
+
+[18.54, 17.98, 18.07, 17.85, 18.22, 17.93, 18.48, 18.6, 18.31, 18.9]
+[54.72]
+52.68738007545471
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1159, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.524485111236572, 'TIME_S_1KI': 9.080660147745101, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2883.053437728882, 'W': 54.72}
+[18.54, 17.98, 18.07, 17.85, 18.22, 17.93, 18.48, 18.6, 18.31, 18.9, 18.48, 18.15, 18.06, 18.06, 17.98, 17.84, 18.37, 17.81, 17.96, 21.64]
+328.45
+16.4225
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1159, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.524485111236572, 'TIME_S_1KI': 9.080660147745101, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2883.053437728882, 'W': 54.72, 'J_1KI': 2487.5353215952387, 'W_1KI': 47.21311475409836, 'W_D': 38.2975, 'J_D': 2017.7949384397268, 'W_D_1KI': 33.043572044866266, 'J_D_1KI': 28.51041591446615}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..be4ba65
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 113077, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.7483811378479, "TIME_S_1KI": 0.09505364608052831, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1073.5031987619402, "W": 76.43000000000002, "J_1KI": 9.49355924513332, "W_1KI": 0.6759111048223778, "W_D": 60.05525000000002, "J_D": 843.5104406312707, "W_D_1KI": 0.5311004890472866, "J_D_1KI": 0.004696803850891751}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..8dbecc1
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.02812027931213379}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([42362, 37248, 40868,  ..., 37764, 10134, 17711]),
+       values=tensor([0.4763, 0.9715, 0.1475,  ..., 0.3126, 0.8815, 0.0115]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9973, 0.6491, 0.6388,  ..., 0.7622, 0.1974, 0.5505])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.02812027931213379 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '37339', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.4671623706817627}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([  629, 39590,  3531,  ..., 29068, 27842, 31077]),
+       values=tensor([0.8879, 0.6863, 0.1252,  ..., 0.4874, 0.7763, 0.0925]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1798, 0.4085, 0.6464,  ..., 0.4512, 0.1603, 0.8018])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 3.4671623706817627 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '113077', '-ss', '50000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.7483811378479}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     3,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([ 3755, 45041, 41651,  ..., 28239, 26624, 23506]),
+       values=tensor([0.7255, 0.9443, 0.1927,  ..., 0.4549, 0.6422, 0.3790]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1750, 0.3712, 0.8832,  ..., 0.2728, 0.8510, 0.3193])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.7483811378479 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     3,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([ 3755, 45041, 41651,  ..., 28239, 26624, 23506]),
+       values=tensor([0.7255, 0.9443, 0.1927,  ..., 0.4549, 0.6422, 0.3790]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1750, 0.3712, 0.8832,  ..., 0.2728, 0.8510, 0.3193])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.7483811378479 seconds
+
+[18.23, 17.8, 18.68, 17.79, 18.26, 17.97, 18.22, 17.87, 18.18, 18.24]
+[76.43]
+14.045573711395264
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 113077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.7483811378479, 'TIME_S_1KI': 0.09505364608052831, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1073.5031987619402, 'W': 76.43000000000002}
+[18.23, 17.8, 18.68, 17.79, 18.26, 17.97, 18.22, 17.87, 18.18, 18.24, 18.73, 17.83, 18.03, 17.81, 18.07, 18.07, 17.89, 17.96, 20.53, 17.87]
+327.495
+16.37475
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 113077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.7483811378479, 'TIME_S_1KI': 0.09505364608052831, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1073.5031987619402, 'W': 76.43000000000002, 'J_1KI': 9.49355924513332, 'W_1KI': 0.6759111048223778, 'W_D': 60.05525000000002, 'J_D': 843.5104406312707, 'W_D_1KI': 0.5311004890472866, 'J_D_1KI': 0.004696803850891751}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..e084c9d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 88156, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.868364572525024, "TIME_S_1KI": 0.12328559113985461, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1132.9122091054917, "W": 79.86, "J_1KI": 12.851220666834834, "W_1KI": 0.9058940968283498, "W_D": 63.193749999999994, "J_D": 896.4809781387447, "W_D_1KI": 0.716840033576841, "J_D_1KI": 0.00813149455030674}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..a0c02dc
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.028945207595825195}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      5,  ..., 124995, 124996,
+                            125000]),
+       col_indices=tensor([ 6853, 13029,  2913,  ..., 16543, 38720, 48018]),
+       values=tensor([0.0310, 0.8074, 0.8860,  ..., 0.7640, 0.7803, 0.3703]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.5471, 0.4507, 0.3833,  ..., 0.1733, 0.0716, 0.3889])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.028945207595825195 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '36275', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.320595741271973}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      6,  ..., 124997, 124999,
+                            125000]),
+       col_indices=tensor([26872,  2155, 12844,  ..., 14460, 31839, 14088]),
+       values=tensor([0.4897, 0.3509, 0.0171,  ..., 0.2036, 0.0300, 0.0283]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.1984, 0.6261, 0.3986,  ..., 0.2975, 0.8868, 0.2739])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 4.320595741271973 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '88156', '-ss', '50000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.868364572525024}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      6,  ..., 124997, 124998,
+                            125000]),
+       col_indices=tensor([17427, 27540, 29335,  ..., 48451,  4975, 32778]),
+       values=tensor([0.3820, 0.7973, 0.1142,  ..., 0.5863, 0.3733, 0.5873]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7507, 0.0731, 0.4946,  ..., 0.5316, 0.6298, 0.9971])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.868364572525024 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      6,  ..., 124997, 124998,
+                            125000]),
+       col_indices=tensor([17427, 27540, 29335,  ..., 48451,  4975, 32778]),
+       values=tensor([0.3820, 0.7973, 0.1142,  ..., 0.5863, 0.3733, 0.5873]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7507, 0.0731, 0.4946,  ..., 0.5316, 0.6298, 0.9971])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.868364572525024 seconds
+
+[18.49, 17.88, 18.37, 17.92, 18.86, 17.84, 18.14, 20.87, 18.57, 18.13]
+[79.86]
+14.186228513717651
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 88156, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.868364572525024, 'TIME_S_1KI': 0.12328559113985461, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1132.9122091054917, 'W': 79.86}
+[18.49, 17.88, 18.37, 17.92, 18.86, 17.84, 18.14, 20.87, 18.57, 18.13, 18.45, 17.94, 18.01, 21.62, 18.32, 18.14, 18.03, 18.0, 18.36, 17.84]
+333.32500000000005
+16.66625
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 88156, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.868364572525024, 'TIME_S_1KI': 0.12328559113985461, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1132.9122091054917, 'W': 79.86, 'J_1KI': 12.851220666834834, 'W_1KI': 0.9058940968283498, 'W_D': 63.193749999999994, 'J_D': 896.4809781387447, 'W_D_1KI': 0.716840033576841, 'J_D_1KI': 0.00813149455030674}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..63a6b21
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 339415, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.661308526992798, "TIME_S_1KI": 0.031410834898259646, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1042.263754928112, "W": 73.63, "J_1KI": 3.0707651545397576, "W_1KI": 0.2169320743043177, "W_D": 56.882, "J_D": 805.1887397503853, "W_D_1KI": 0.1675883505443189, "J_D_1KI": 0.0004937564649303033}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..4acadea
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,82 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.02093958854675293}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([ 145,  181,  299,  ..., 1340, 4416,  166]),
+       values=tensor([0.2713, 0.7441, 0.5681,  ..., 0.3863, 0.3329, 0.3299]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([6.8436e-01, 7.7662e-01, 5.3826e-02,  ..., 2.4557e-01, 1.4818e-01,
+        6.5619e-04])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.02093958854675293 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '50144', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.5512330532073975}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([ 261, 2124, 2825,  ..., 2342, 1684, 3815]),
+       values=tensor([0.1132, 0.9807, 0.3410,  ..., 0.0783, 0.3569, 0.0713]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.8182, 0.0907, 0.1359,  ..., 0.4059, 0.0754, 0.0727])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 1.5512330532073975 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '339415', '-ss', '5000', '-sd', '0.0001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.661308526992798}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2497, 2498, 2500]),
+       col_indices=tensor([  91, 2131, 2855,  ..., 2446,  470, 1581]),
+       values=tensor([0.9229, 0.3729, 0.6792,  ..., 0.1416, 0.2267, 0.3921]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.5750, 0.0749, 0.6665,  ..., 0.8045, 0.0578, 0.3106])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.661308526992798 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2497, 2498, 2500]),
+       col_indices=tensor([  91, 2131, 2855,  ..., 2446,  470, 1581]),
+       values=tensor([0.9229, 0.3729, 0.6792,  ..., 0.1416, 0.2267, 0.3921]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.5750, 0.0749, 0.6665,  ..., 0.8045, 0.0578, 0.3106])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.661308526992798 seconds
+
+[18.68, 17.92, 18.02, 18.26, 18.14, 18.26, 18.3, 17.84, 18.15, 17.87]
+[73.63]
+14.155422449111938
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 339415, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.661308526992798, 'TIME_S_1KI': 0.031410834898259646, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1042.263754928112, 'W': 73.63}
+[18.68, 17.92, 18.02, 18.26, 18.14, 18.26, 18.3, 17.84, 18.15, 17.87, 18.33, 18.37, 18.02, 18.02, 22.34, 18.22, 18.25, 22.38, 17.9, 18.26]
+334.9599999999999
+16.747999999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 339415, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.661308526992798, 'TIME_S_1KI': 0.031410834898259646, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1042.263754928112, 'W': 73.63, 'J_1KI': 3.0707651545397576, 'W_1KI': 0.2169320743043177, 'W_D': 56.882, 'J_D': 805.1887397503853, 'W_D_1KI': 0.1675883505443189, 'J_D_1KI': 0.0004937564649303033}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..4f7c98c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 242735, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.6920804977417, "TIME_S_1KI": 0.044048367552028754, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1022.2163880014419, "W": 74.91, "J_1KI": 4.211244311703883, "W_1KI": 0.30860815292397054, "W_D": 58.500499999999995, "J_D": 798.2935496766567, "W_D_1KI": 0.241005623416483, "J_D_1KI": 0.000992875454369922}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..6818997
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.019707918167114258}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     9,  ..., 24992, 24998, 25000]),
+       col_indices=tensor([2443, 3271, 4233,  ..., 3520, 3792, 4350]),
+       values=tensor([0.8426, 0.5824, 0.3389,  ..., 0.9840, 0.1147, 0.8239]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5471, 0.6226, 0.3278,  ..., 0.2451, 0.7959, 0.6112])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.019707918167114258 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53278', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.304640293121338}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     9,    13,  ..., 24990, 24994, 25000]),
+       col_indices=tensor([ 876,  897, 2274,  ..., 2103, 3712, 4740]),
+       values=tensor([0.9508, 0.2626, 0.2379,  ..., 0.2341, 0.9066, 0.6182]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8511, 0.0306, 0.7639,  ..., 0.5025, 0.8599, 0.9690])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 2.304640293121338 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '242735', '-ss', '5000', '-sd', '0.001', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.6920804977417}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     6,    10,  ..., 24991, 24997, 25000]),
+       col_indices=tensor([1152, 1177, 2929,  ..., 1264, 2609, 4571]),
+       values=tensor([0.4587, 0.9835, 0.0653,  ..., 0.8655, 0.2110, 0.7343]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5612, 0.6517, 0.4101,  ..., 0.1691, 0.2414, 0.3717])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.6920804977417 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     6,    10,  ..., 24991, 24997, 25000]),
+       col_indices=tensor([1152, 1177, 2929,  ..., 1264, 2609, 4571]),
+       values=tensor([0.4587, 0.9835, 0.0653,  ..., 0.8655, 0.2110, 0.7343]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5612, 0.6517, 0.4101,  ..., 0.1691, 0.2414, 0.3717])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.6920804977417 seconds
+
+[18.79, 17.83, 18.25, 17.86, 18.02, 18.39, 18.09, 17.82, 18.07, 17.75]
+[74.91]
+13.64592695236206
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 242735, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.6920804977417, 'TIME_S_1KI': 0.044048367552028754, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1022.2163880014419, 'W': 74.91}
+[18.79, 17.83, 18.25, 17.86, 18.02, 18.39, 18.09, 17.82, 18.07, 17.75, 18.54, 18.01, 18.05, 19.12, 19.51, 18.12, 18.41, 17.83, 18.29, 17.96]
+328.19
+16.4095
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 242735, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.6920804977417, 'TIME_S_1KI': 0.044048367552028754, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1022.2163880014419, 'W': 74.91, 'J_1KI': 4.211244311703883, 'W_1KI': 0.30860815292397054, 'W_D': 58.500499999999995, 'J_D': 798.2935496766567, 'W_D_1KI': 0.241005623416483, 'J_D_1KI': 0.000992875454369922}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..64ab37a
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 161950, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.493181943893433, "TIME_S_1KI": 0.06479272580360255, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1161.5640621185303, "W": 84.0, "J_1KI": 7.172362223640199, "W_1KI": 0.5186786045075641, "W_D": 67.18725, "J_D": 929.0749408639671, "W_D_1KI": 0.41486415560358136, "J_D_1KI": 0.0025616804915318393}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..f4d043e
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.022989749908447266}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     53,    112,  ..., 249913, 249957,
+                            250000]),
+       col_indices=tensor([  48,   54,  234,  ..., 4368, 4853, 4864]),
+       values=tensor([0.3452, 0.1008, 0.0125,  ..., 0.4983, 0.8936, 0.7126]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5619, 0.6285, 0.8433,  ..., 0.7149, 0.5039, 0.6932])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.022989749908447266 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '45672', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.961124897003174}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     53,     98,  ..., 249913, 249948,
+                            250000]),
+       col_indices=tensor([ 202,  295,  369,  ..., 4836, 4929, 4943]),
+       values=tensor([0.0950, 0.5576, 0.0327,  ..., 0.3192, 0.9052, 0.1110]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6070, 0.1874, 0.1819,  ..., 0.4756, 0.1999, 0.3064])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 2.961124897003174 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '161950', '-ss', '5000', '-sd', '0.01', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.493181943893433}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     48,    111,  ..., 249911, 249964,
+                            250000]),
+       col_indices=tensor([  19,   43,  144,  ..., 4843, 4924, 4947]),
+       values=tensor([0.0652, 0.5238, 0.9360,  ..., 0.5118, 0.2782, 0.1343]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4213, 0.5109, 0.7737,  ..., 0.0670, 0.5810, 0.9899])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.493181943893433 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     48,    111,  ..., 249911, 249964,
+                            250000]),
+       col_indices=tensor([  19,   43,  144,  ..., 4843, 4924, 4947]),
+       values=tensor([0.0652, 0.5238, 0.9360,  ..., 0.5118, 0.2782, 0.1343]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4213, 0.5109, 0.7737,  ..., 0.0670, 0.5810, 0.9899])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.493181943893433 seconds
+
+[18.86, 18.02, 19.18, 18.0, 18.43, 21.89, 19.39, 17.96, 18.27, 17.81]
+[84.0]
+13.82814359664917
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 161950, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.493181943893433, 'TIME_S_1KI': 0.06479272580360255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1161.5640621185303, 'W': 84.0}
+[18.86, 18.02, 19.18, 18.0, 18.43, 21.89, 19.39, 17.96, 18.27, 17.81, 18.1, 20.23, 20.02, 18.19, 17.98, 17.88, 18.2, 17.87, 18.32, 18.08]
+336.255
+16.81275
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 161950, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.493181943893433, 'TIME_S_1KI': 0.06479272580360255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1161.5640621185303, 'W': 84.0, 'J_1KI': 7.172362223640199, 'W_1KI': 0.5186786045075641, 'W_D': 67.18725, 'J_D': 929.0749408639671, 'W_D_1KI': 0.41486415560358136, 'J_D_1KI': 0.0025616804915318393}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..c0ca085
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 46969, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.723698854446411, "TIME_S_1KI": 0.22831439575989293, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1262.0827813720703, "W": 87.97, "J_1KI": 26.87054826315379, "W_1KI": 1.8729374693947072, "W_D": 71.6635, "J_D": 1028.1376537780761, "W_D_1KI": 1.5257616725925611, "J_D_1KI": 0.03248444021785776}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..62668bd
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03811240196228027}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     252,     486,  ..., 1249499,
+                            1249768, 1250000]),
+       col_indices=tensor([  15,   65,   81,  ..., 4947, 4948, 4952]),
+       values=tensor([0.2497, 0.0794, 0.3182,  ..., 0.5399, 0.7483, 0.2341]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6929, 0.1024, 0.7145,  ..., 0.7803, 0.6014, 0.5585])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 0.03811240196228027 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '27550', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 6.158770799636841}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     250,     496,  ..., 1249504,
+                            1249751, 1250000]),
+       col_indices=tensor([  13,   88,   93,  ..., 4888, 4919, 4936]),
+       values=tensor([0.3293, 0.4307, 0.3782,  ..., 0.2045, 0.2965, 0.3765]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.2742, 0.5184, 0.9225,  ..., 0.0830, 0.2762, 0.4744])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 6.158770799636841 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '46969', '-ss', '5000', '-sd', '0.05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.723698854446411}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     267,     518,  ..., 1249533,
+                            1249757, 1250000]),
+       col_indices=tensor([   4,   12,   26,  ..., 4948, 4976, 4977]),
+       values=tensor([0.8262, 0.2304, 0.8718,  ..., 0.9371, 0.9418, 0.0811]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.8290, 0.5732, 0.0178,  ..., 0.9134, 0.7238, 0.4621])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.723698854446411 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     267,     518,  ..., 1249533,
+                            1249757, 1250000]),
+       col_indices=tensor([   4,   12,   26,  ..., 4948, 4976, 4977]),
+       values=tensor([0.8262, 0.2304, 0.8718,  ..., 0.9371, 0.9418, 0.0811]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.8290, 0.5732, 0.0178,  ..., 0.9134, 0.7238, 0.4621])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.723698854446411 seconds
+
+[18.22, 17.77, 18.1, 17.81, 18.08, 18.08, 18.29, 17.93, 18.14, 17.97]
+[87.97]
+14.34674072265625
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46969, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.723698854446411, 'TIME_S_1KI': 0.22831439575989293, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1262.0827813720703, 'W': 87.97}
+[18.22, 17.77, 18.1, 17.81, 18.08, 18.08, 18.29, 17.93, 18.14, 17.97, 18.49, 18.0, 18.1, 18.25, 18.1, 17.98, 17.87, 18.07, 17.92, 20.6]
+326.13
+16.3065
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46969, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.723698854446411, 'TIME_S_1KI': 0.22831439575989293, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1262.0827813720703, 'W': 87.97, 'J_1KI': 26.87054826315379, 'W_1KI': 1.8729374693947072, 'W_D': 71.6635, 'J_D': 1028.1376537780761, 'W_D_1KI': 1.5257616725925611, 'J_D_1KI': 0.03248444021785776}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..fe585f9
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 19220, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.306657314300537, "TIME_S_1KI": 0.536246478371516, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1258.2980075454711, "W": 87.22999999999999, "J_1KI": 65.46815856115875, "W_1KI": 4.538501560874089, "W_D": 70.87349999999999, "J_D": 1022.354509202957, "W_D_1KI": 3.6874869927159204, "J_D_1KI": 0.19185676340873678}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..2db63b3
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.06937050819396973}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     517,    1013,  ..., 2498965,
+                            2499518, 2500000]),
+       col_indices=tensor([  16,   31,   39,  ..., 4973, 4987, 4996]),
+       values=tensor([0.8092, 0.6907, 0.1859,  ..., 0.6156, 0.1820, 0.0827]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0341, 0.1297, 0.9553,  ..., 0.9560, 0.1422, 0.0438])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 0.06937050819396973 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '15136', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.268742084503174}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     498,    1015,  ..., 2498995,
+                            2499510, 2500000]),
+       col_indices=tensor([   1,    5,   12,  ..., 4987, 4992, 4994]),
+       values=tensor([0.2706, 0.5291, 0.0606,  ..., 0.9998, 0.6766, 0.8077]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5238, 0.3873, 0.9372,  ..., 0.7751, 0.3587, 0.1743])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 8.268742084503174 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '19220', '-ss', '5000', '-sd', '0.1', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.306657314300537}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     478,     976,  ..., 2498986,
+                            2499483, 2500000]),
+       col_indices=tensor([   0,   15,   20,  ..., 4988, 4995, 4997]),
+       values=tensor([0.9696, 0.2544, 0.3304,  ..., 0.5139, 0.4686, 0.4850]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8873, 0.6521, 0.3260,  ..., 0.9177, 0.3863, 0.5956])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.306657314300537 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     478,     976,  ..., 2498986,
+                            2499483, 2500000]),
+       col_indices=tensor([   0,   15,   20,  ..., 4988, 4995, 4997]),
+       values=tensor([0.9696, 0.2544, 0.3304,  ..., 0.5139, 0.4686, 0.4850]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8873, 0.6521, 0.3260,  ..., 0.9177, 0.3863, 0.5956])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.306657314300537 seconds
+
+[18.67, 18.15, 17.98, 18.09, 18.08, 18.12, 18.09, 17.98, 17.86, 18.03]
+[87.23]
+14.425060272216797
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19220, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.306657314300537, 'TIME_S_1KI': 0.536246478371516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1258.2980075454711, 'W': 87.22999999999999}
+[18.67, 18.15, 17.98, 18.09, 18.08, 18.12, 18.09, 17.98, 17.86, 18.03, 18.76, 17.96, 18.22, 17.88, 18.31, 18.19, 18.22, 17.8, 18.27, 20.4]
+327.13
+16.3565
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19220, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.306657314300537, 'TIME_S_1KI': 0.536246478371516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1258.2980075454711, 'W': 87.22999999999999, 'J_1KI': 65.46815856115875, 'W_1KI': 4.538501560874089, 'W_D': 70.87349999999999, 'J_D': 1022.354509202957, 'W_D_1KI': 3.6874869927159204, 'J_D_1KI': 0.19185676340873678}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..5da43df
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 9074, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.487424612045288, "TIME_S_1KI": 1.1557664328901573, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1301.6191907930374, "W": 85.83, "J_1KI": 143.44491853571054, "W_1KI": 9.458893541988099, "W_D": 69.53375, "J_D": 1054.4851847583054, "W_D_1KI": 7.662965616045845, "J_D_1KI": 0.8444969821518454}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..4c63e35
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.12977051734924316}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1037,    2033,  ..., 4997947,
+                            4998969, 5000000]),
+       col_indices=tensor([   0,    3,   11,  ..., 4985, 4988, 4990]),
+       values=tensor([0.1539, 0.2882, 0.0917,  ..., 0.8336, 0.9260, 0.3814]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.9778, 0.3097, 0.5480,  ..., 0.9590, 0.3024, 0.0294])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 0.12977051734924316 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8091', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.36232042312622}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     972,    1989,  ..., 4997978,
+                            4998985, 5000000]),
+       col_indices=tensor([   0,    9,   19,  ..., 4989, 4992, 4995]),
+       values=tensor([0.2585, 0.0110, 0.4823,  ..., 0.4314, 0.8099, 0.9487]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7513, 0.6352, 0.8184,  ..., 0.0273, 0.2479, 0.5631])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 9.36232042312622 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9074', '-ss', '5000', '-sd', '0.2', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.487424612045288}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1061,    2118,  ..., 4997993,
+                            4998997, 5000000]),
+       col_indices=tensor([   3,    6,   10,  ..., 4996, 4998, 4999]),
+       values=tensor([0.5066, 0.7039, 0.5374,  ..., 0.1064, 0.9581, 0.5937]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1982, 0.1257, 0.6934,  ..., 0.0401, 0.8872, 0.0311])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.487424612045288 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1061,    2118,  ..., 4997993,
+                            4998997, 5000000]),
+       col_indices=tensor([   3,    6,   10,  ..., 4996, 4998, 4999]),
+       values=tensor([0.5066, 0.7039, 0.5374,  ..., 0.1064, 0.9581, 0.5937]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1982, 0.1257, 0.6934,  ..., 0.0401, 0.8872, 0.0311])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.487424612045288 seconds
+
+[18.43, 18.11, 17.89, 18.52, 18.92, 18.0, 18.0, 17.79, 18.16, 17.76]
+[85.83]
+15.16508436203003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9074, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.487424612045288, 'TIME_S_1KI': 1.1557664328901573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.6191907930374, 'W': 85.83}
+[18.43, 18.11, 17.89, 18.52, 18.92, 18.0, 18.0, 17.79, 18.16, 17.76, 18.23, 17.97, 18.42, 17.87, 17.92, 18.08, 18.11, 17.82, 17.87, 18.53]
+325.925
+16.29625
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9074, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.487424612045288, 'TIME_S_1KI': 1.1557664328901573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1301.6191907930374, 'W': 85.83, 'J_1KI': 143.44491853571054, 'W_1KI': 9.458893541988099, 'W_D': 69.53375, 'J_D': 1054.4851847583054, 'W_D_1KI': 7.662965616045845, 'J_D_1KI': 0.8444969821518454}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..c0e5e12
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5607, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.746073484420776, "TIME_S_1KI": 1.9165460111326513, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1333.0484231472017, "W": 84.51, "J_1KI": 237.7471773046552, "W_1KI": 15.07223113964687, "W_D": 68.30000000000001, "J_D": 1077.3542456626894, "W_D_1KI": 12.18120206884252, "J_D_1KI": 2.1724990313612484}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..4501c06
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.20109891891479492}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1526,    3035,  ..., 7496941,
+                            7498453, 7500000]),
+       col_indices=tensor([   2,    6,   10,  ..., 4993, 4994, 4996]),
+       values=tensor([0.3579, 0.5981, 0.5931,  ..., 0.3837, 0.5123, 0.1240]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.0391, 0.3406, 0.5361,  ..., 0.5039, 0.0205, 0.2852])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 0.20109891891479492 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5221', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.77708387374878}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1505,    2964,  ..., 7496939,
+                            7498442, 7500000]),
+       col_indices=tensor([   2,    4,    5,  ..., 4996, 4997, 4999]),
+       values=tensor([0.1729, 0.9195, 0.5163,  ..., 0.0436, 0.1803, 0.9350]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.3955, 0.7196, 0.4505,  ..., 0.8904, 0.4770, 0.0844])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 9.77708387374878 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5607', '-ss', '5000', '-sd', '0.3', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.746073484420776}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1451,    2905,  ..., 7497061,
+                            7498533, 7500000]),
+       col_indices=tensor([   4,    9,   12,  ..., 4994, 4998, 4999]),
+       values=tensor([0.3703, 0.3823, 0.6108,  ..., 0.0984, 0.8524, 0.0373]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.6688, 0.5149, 0.3458,  ..., 0.1151, 0.9310, 0.8037])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.746073484420776 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1451,    2905,  ..., 7497061,
+                            7498533, 7500000]),
+       col_indices=tensor([   4,    9,   12,  ..., 4994, 4998, 4999]),
+       values=tensor([0.3703, 0.3823, 0.6108,  ..., 0.0984, 0.8524, 0.0373]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.6688, 0.5149, 0.3458,  ..., 0.1151, 0.9310, 0.8037])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.746073484420776 seconds
+
+[18.36, 17.95, 18.08, 17.67, 18.17, 17.84, 18.12, 17.84, 18.09, 18.06]
+[84.51]
+15.77385425567627
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5607, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.746073484420776, 'TIME_S_1KI': 1.9165460111326513, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1333.0484231472017, 'W': 84.51}
+[18.36, 17.95, 18.08, 17.67, 18.17, 17.84, 18.12, 17.84, 18.09, 18.06, 18.35, 17.89, 17.87, 18.07, 18.23, 17.83, 18.12, 17.92, 18.19, 17.87]
+324.20000000000005
+16.21
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5607, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.746073484420776, 'TIME_S_1KI': 1.9165460111326513, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1333.0484231472017, 'W': 84.51, 'J_1KI': 237.7471773046552, 'W_1KI': 15.07223113964687, 'W_D': 68.30000000000001, 'J_D': 1077.3542456626894, 'W_D_1KI': 12.18120206884252, 'J_D_1KI': 2.1724990313612484}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..363edc0
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2843, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.510854721069336, "TIME_S_1KI": 3.6970997963662806, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1362.9070807647706, "W": 82.31, "J_1KI": 479.39046104986653, "W_1KI": 28.951811466760464, "W_D": 66.04650000000001, "J_D": 1093.6124712638857, "W_D_1KI": 23.23126978543792, "J_D_1KI": 8.171392819359099}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..26353c7
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.3692905902862549}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1942,     3937,  ...,  9996021,
+                             9998014, 10000000]),
+       col_indices=tensor([   1,    6,   11,  ..., 4995, 4996, 4997]),
+       values=tensor([0.9006, 0.6515, 0.7177,  ..., 0.3221, 0.1090, 0.6573]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3731, 0.3806, 0.5183,  ..., 0.1665, 0.0476, 0.3514])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 0.3692905902862549 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2843', '-ss', '5000', '-sd', '0.4', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.510854721069336}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2016,     4009,  ...,  9996028,
+                             9998016, 10000000]),
+       col_indices=tensor([   0,    3,    5,  ..., 4991, 4992, 4997]),
+       values=tensor([0.1122, 0.1212, 0.4120,  ..., 0.9869, 0.5095, 0.1756]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1243, 0.0753, 0.2426,  ..., 0.5390, 0.1485, 0.6469])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.510854721069336 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2016,     4009,  ...,  9996028,
+                             9998016, 10000000]),
+       col_indices=tensor([   0,    3,    5,  ..., 4991, 4992, 4997]),
+       values=tensor([0.1122, 0.1212, 0.4120,  ..., 0.9869, 0.5095, 0.1756]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1243, 0.0753, 0.2426,  ..., 0.5390, 0.1485, 0.6469])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.510854721069336 seconds
+
+[18.34, 18.07, 17.87, 17.95, 18.1, 18.25, 18.13, 17.86, 18.35, 17.99]
+[82.31]
+16.55821990966797
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2843, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.510854721069336, 'TIME_S_1KI': 3.6970997963662806, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1362.9070807647706, 'W': 82.31}
+[18.34, 18.07, 17.87, 17.95, 18.1, 18.25, 18.13, 17.86, 18.35, 17.99, 18.23, 18.01, 18.07, 17.94, 18.3, 17.93, 18.17, 17.9, 18.02, 18.14]
+325.2699999999999
+16.263499999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2843, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.510854721069336, 'TIME_S_1KI': 3.6970997963662806, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1362.9070807647706, 'W': 82.31, 'J_1KI': 479.39046104986653, 'W_1KI': 28.951811466760464, 'W_D': 66.04650000000001, 'J_D': 1093.6124712638857, 'W_D_1KI': 23.23126978543792, 'J_D_1KI': 8.171392819359099}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..5cc798d
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2367, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.31517243385315, "TIME_S_1KI": 4.35790977349098, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1435.467025976181, "W": 77.42, "J_1KI": 606.4499476029494, "W_1KI": 32.708069286016055, "W_D": 60.97375, "J_D": 1130.5322600764036, "W_D_1KI": 25.7599281791297, "J_D_1KI": 10.882943886408832}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..9c08cbf
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.4716074466705322}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2514,     4962,  ..., 12495038,
+                            12497495, 12500000]),
+       col_indices=tensor([   0,    2,    3,  ..., 4994, 4998, 4999]),
+       values=tensor([0.6154, 0.9669, 0.2665,  ..., 0.0694, 0.4098, 0.1560]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.5763, 0.6030, 0.9940,  ..., 0.2125, 0.0764, 0.1654])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 0.4716074466705322 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2226', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.872556447982788}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2494,     5060,  ..., 12495015,
+                            12497540, 12500000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4996, 4997, 4998]),
+       values=tensor([0.6641, 0.1400, 0.8724,  ..., 0.1588, 0.2497, 0.0435]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4856, 0.9202, 0.9997,  ..., 0.6539, 0.7245, 0.6538])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 9.872556447982788 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2367', '-ss', '5000', '-sd', '0.5', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.31517243385315}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2524,     5066,  ..., 12495116,
+                            12497547, 12500000]),
+       col_indices=tensor([   0,    3,    4,  ..., 4997, 4998, 4999]),
+       values=tensor([0.7920, 0.5049, 0.7981,  ..., 0.7837, 0.6861, 0.6359]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9717, 0.2840, 0.2667,  ..., 0.4259, 0.7903, 0.5279])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.31517243385315 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2524,     5066,  ..., 12495116,
+                            12497547, 12500000]),
+       col_indices=tensor([   0,    3,    4,  ..., 4997, 4998, 4999]),
+       values=tensor([0.7920, 0.5049, 0.7981,  ..., 0.7837, 0.6861, 0.6359]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9717, 0.2840, 0.2667,  ..., 0.4259, 0.7903, 0.5279])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.31517243385315 seconds
+
+[18.22, 18.01, 18.1, 18.03, 17.97, 17.83, 18.06, 17.94, 17.8, 18.08]
+[77.42]
+18.54129457473755
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2367, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.31517243385315, 'TIME_S_1KI': 4.35790977349098, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1435.467025976181, 'W': 77.42}
+[18.22, 18.01, 18.1, 18.03, 17.97, 17.83, 18.06, 17.94, 17.8, 18.08, 18.25, 18.11, 18.19, 18.18, 17.9, 17.94, 21.89, 18.55, 18.24, 17.82]
+328.925
+16.44625
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2367, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.31517243385315, 'TIME_S_1KI': 4.35790977349098, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1435.467025976181, 'W': 77.42, 'J_1KI': 606.4499476029494, 'W_1KI': 32.708069286016055, 'W_D': 60.97375, 'J_D': 1130.5322600764036, 'W_D_1KI': 25.7599281791297, 'J_D_1KI': 10.882943886408832}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..f03b074
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 352628, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.273355960845947, "TIME_S_1KI": 0.029133693186150694, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 997.6947084188461, "W": 73.02, "J_1KI": 2.8293122168938547, "W_1KI": 0.20707374343500798, "W_D": 56.76349999999999, "J_D": 775.5771512097119, "W_D_1KI": 0.16097275315630066, "J_D_1KI": 0.00045649453008921774}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..a48462c
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,356 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.03506755828857422}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([2913, 1648, 2644, 3381, 1372, 3923, 4637, 4740, 3605,
+                           3437,  243,  589, 1110, 3938, 3652, 4390,  556, 1399,
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+                           2040, 2532, 4933, 2387, 3999, 2398, 1862,   25, 1623,
+                           3988, 4385,  855, 1469, 4176, 2016, 3588, 1388, 4820,
+                            138, 2359, 3521,  918, 3462, 3885, 3534]),
+       values=tensor([8.7230e-01, 2.2609e-01, 1.4440e-01, 9.6081e-01,
+                      3.3014e-01, 2.6737e-01, 1.3690e-01, 4.9883e-01,
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+                      1.8578e-02, 9.9935e-01, 2.7602e-01, 1.0075e-01,
+                      6.3752e-01, 2.7320e-01, 1.0876e-01, 8.9150e-01,
+                      5.5601e-01, 4.9492e-01, 5.2617e-01, 3.1376e-01,
+                      6.2485e-02, 6.1369e-01, 8.9174e-01, 2.5796e-01,
+                      3.3213e-01, 2.3179e-01, 9.8658e-01, 2.3060e-01,
+                      9.3135e-01, 5.2215e-02, 9.7039e-01, 5.8413e-01,
+                      1.2826e-01, 4.7981e-01, 9.6357e-01, 3.2682e-02,
+                      1.6298e-02, 1.5945e-01, 9.8437e-01, 2.0032e-01,
+                      9.5029e-01, 6.2119e-01, 2.5447e-01, 3.9302e-01,
+                      7.6479e-01, 4.3998e-01, 4.2741e-02, 8.0712e-01,
+                      7.5958e-01, 9.4342e-01, 6.1220e-02, 8.5277e-01,
+                      8.0569e-01, 8.5113e-02, 3.3815e-01, 2.2293e-01,
+                      7.4069e-01, 9.6487e-01, 9.2541e-01, 4.0658e-01,
+                      7.8100e-01, 1.8005e-01, 7.5104e-01, 2.0211e-01,
+                      1.3360e-01, 5.3522e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.6988, 0.9907, 0.6239,  ..., 0.8127, 0.8805, 0.8314])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.03506755828857422 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '29942', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.8915646076202393}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([3138, 4671, 1014, 2355, 4387, 3668, 1195, 4247,  491,
+                           1252, 2094, 1714, 1299, 3079,  802, 3268, 2381, 2379,
+                           4459, 4147, 1428, 4131,  184, 2357, 1540, 3877, 1899,
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+                           1256, 4264, 2072,  151, 2943, 1612, 2224, 2929,  923,
+                           3069, 3516, 3474,   16, 4892, 1006, 3067]),
+       values=tensor([0.1423, 0.3764, 0.8726, 0.6883, 0.7846, 0.4459, 0.0452,
+                      0.1595, 0.8993, 0.6993, 0.8582, 0.8926, 0.6386, 0.2255,
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+                      0.0621, 0.9856, 0.0778, 0.2539, 0.3235, 0.9242, 0.1079,
+                      0.9852, 0.7752, 0.1954, 0.3552, 0.8036, 0.4824, 0.2198,
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+                      0.4489, 0.6315, 0.2327, 0.4513, 0.7262, 0.7754, 0.6206,
+                      0.4823, 0.8933, 0.7206, 0.5757, 0.6875]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4051, 0.4452, 0.8286,  ..., 0.6416, 0.7748, 0.9825])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.8915646076202393 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '352628', '-ss', '5000', '-sd', '1e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.273355960845947}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1604,    4,  806,  370, 4659, 2266,  385, 4480, 1740,
+                           2477, 1011, 4368, 1436, 1511,  582, 2881, 3146,  679,
+                           1335,  340, 2368, 3531, 2793, 4894, 1704,  800, 4449,
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+                           2085, 4458, 4110, 3726,  941, 4345, 4377, 4491, 3791,
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+                           4592, 3120, 1244, 3477, 1623,   73, 4441, 1447, 3927,
+                           4954, 4072, 1985,  625, 3210, 3147, 4908, 2800, 1924,
+                            230, 2107, 2981, 3816, 1923, 3645, 3133, 4236, 4114,
+                           2851, 2177, 1793, 4717,  666, 1768, 4852]),
+       values=tensor([0.8340, 0.7919, 0.0199, 0.7955, 0.8390, 0.2112, 0.2062,
+                      0.2416, 0.0078, 0.1110, 0.8766, 0.2461, 0.5766, 0.2522,
+                      0.8938, 0.7197, 0.9623, 0.5758, 0.3379, 0.5611, 0.1986,
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+                      0.9811, 0.7838, 0.9261, 0.3312, 0.1662, 0.7114, 0.8864,
+                      0.9809, 0.1390, 0.6532, 0.2965, 0.0298, 0.8840, 0.9398,
+                      0.6219, 0.4181, 0.3747, 0.5146, 0.8402, 0.0806, 0.9003,
+                      0.4097, 0.4861, 0.8634, 0.8848, 0.4692, 0.4523, 0.5039,
+                      0.7094, 0.3166, 0.2806, 0.4769, 0.9739, 0.8634, 0.3699,
+                      0.8453, 0.0189, 0.8787, 0.8196, 0.8724, 0.2325, 0.0224,
+                      0.5326, 0.1429, 0.6605, 0.4303, 0.9331, 0.8262, 0.4714,
+                      0.3810, 0.9149, 0.4305, 0.2891, 0.7127, 0.3828, 0.4241,
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+                      0.3284, 0.6812, 0.4592, 0.8170, 0.4218, 0.2545, 0.2861,
+                      0.1807, 0.3784, 0.2316, 0.6484, 0.4370, 0.4606, 0.6060,
+                      0.1427, 0.6182, 0.8321, 0.4963, 0.9467, 0.6222, 0.0087,
+                      0.8644, 0.1970, 0.6141, 0.5044, 0.8825, 0.7629, 0.0116,
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+                      0.2186, 0.5556, 0.9770, 0.6002, 0.0280, 0.6160, 0.1589,
+                      0.7241, 0.2905, 0.4033, 0.4301, 0.8521, 0.8618, 0.5604,
+                      0.1077, 0.2810, 0.1105, 0.5637, 0.8228, 0.0305, 0.7660,
+                      0.3373, 0.7652, 0.7287, 0.6077, 0.2858, 0.4001, 0.8614,
+                      0.8105, 0.5021, 0.3182, 0.2015, 0.3600, 0.7160, 0.9874,
+                      0.0572, 0.8754, 0.4725, 0.5233, 0.0364, 0.1500, 0.2431,
+                      0.3915, 0.8270, 0.2064, 0.5104, 0.9129, 0.4413, 0.8801,
+                      0.9179, 0.9739, 0.2250, 0.5404, 0.9261, 0.5735, 0.2090,
+                      0.7470, 0.4131, 0.1494, 0.0532, 0.6628]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.3633, 0.6430, 0.8109,  ..., 0.6589, 0.7112, 0.9999])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.273355960845947 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1604,    4,  806,  370, 4659, 2266,  385, 4480, 1740,
+                           2477, 1011, 4368, 1436, 1511,  582, 2881, 3146,  679,
+                           1335,  340, 2368, 3531, 2793, 4894, 1704,  800, 4449,
+                           2819, 3830,  944,  715,  291, 1651, 2756, 3425, 4366,
+                           1387, 4461, 4123, 3059,   45,   97,  307, 3123, 2010,
+                           4861, 3262,  819, 2940, 4148, 3668, 1416, 2946,  250,
+                           2020, 1865, 1972, 4176, 4993, 3807,  275, 1852, 2676,
+                           3641, 2214, 2133, 3702, 1587,   40, 2796, 4522, 2611,
+                           2391,  408, 3928, 2051, 4768, 4967,  847, 3011,  916,
+                           2658, 2737, 3985,  653, 1850, 4982, 4426, 3558, 4287,
+                           1078, 1321, 2196, 3153, 3474, 1886, 4386, 4813, 4479,
+                           1247, 1097, 4368, 3496, 4390, 2441,   28, 3845, 3018,
+                            574, 3154, 4908, 4477, 1259, 4186, 1078, 3130, 3163,
+                           3314, 3378, 1369, 3074,  965, 3176, 3034, 4337, 2593,
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+                           3431, 3473, 1919,  368, 2943, 1836, 2897, 4091, 4055,
+                           2042,  694,  518, 3464,  437, 2319, 2327, 4527, 3332,
+                            286, 2756, 1769,  821, 2234, 2362, 3901, 2835, 3532,
+                             56, 1262, 1926, 2816,  573, 4537,  612,  849, 3556,
+                           1060, 4100, 3259, 4604, 1644,  551,  216, 1429, 4706,
+                           4000, 2046,   67, 4772, 4808, 2103, 2457,  770,   19,
+                           3752, 4627, 3183, 2351, 2290,  676, 4693, 3832, 2391,
+                           2085, 4458, 4110, 3726,  941, 4345, 4377, 4491, 3791,
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+                           4592, 3120, 1244, 3477, 1623,   73, 4441, 1447, 3927,
+                           4954, 4072, 1985,  625, 3210, 3147, 4908, 2800, 1924,
+                            230, 2107, 2981, 3816, 1923, 3645, 3133, 4236, 4114,
+                           2851, 2177, 1793, 4717,  666, 1768, 4852]),
+       values=tensor([0.8340, 0.7919, 0.0199, 0.7955, 0.8390, 0.2112, 0.2062,
+                      0.2416, 0.0078, 0.1110, 0.8766, 0.2461, 0.5766, 0.2522,
+                      0.8938, 0.7197, 0.9623, 0.5758, 0.3379, 0.5611, 0.1986,
+                      0.0227, 0.4551, 0.8241, 0.0932, 0.4545, 0.5055, 0.7378,
+                      0.9811, 0.7838, 0.9261, 0.3312, 0.1662, 0.7114, 0.8864,
+                      0.9809, 0.1390, 0.6532, 0.2965, 0.0298, 0.8840, 0.9398,
+                      0.6219, 0.4181, 0.3747, 0.5146, 0.8402, 0.0806, 0.9003,
+                      0.4097, 0.4861, 0.8634, 0.8848, 0.4692, 0.4523, 0.5039,
+                      0.7094, 0.3166, 0.2806, 0.4769, 0.9739, 0.8634, 0.3699,
+                      0.8453, 0.0189, 0.8787, 0.8196, 0.8724, 0.2325, 0.0224,
+                      0.5326, 0.1429, 0.6605, 0.4303, 0.9331, 0.8262, 0.4714,
+                      0.3810, 0.9149, 0.4305, 0.2891, 0.7127, 0.3828, 0.4241,
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+                      0.3915, 0.8270, 0.2064, 0.5104, 0.9129, 0.4413, 0.8801,
+                      0.9179, 0.9739, 0.2250, 0.5404, 0.9261, 0.5735, 0.2090,
+                      0.7470, 0.4131, 0.1494, 0.0532, 0.6628]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.3633, 0.6430, 0.8109,  ..., 0.6589, 0.7112, 0.9999])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.273355960845947 seconds
+
+[18.62, 17.97, 17.77, 17.77, 18.2, 18.07, 17.98, 17.9, 18.36, 17.75]
+[73.02]
+13.663307428359985
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 352628, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.273355960845947, 'TIME_S_1KI': 0.029133693186150694, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.6947084188461, 'W': 73.02}
+[18.62, 17.97, 17.77, 17.77, 18.2, 18.07, 17.98, 17.9, 18.36, 17.75, 18.68, 18.07, 18.34, 18.06, 18.18, 17.85, 18.03, 18.15, 17.94, 17.93]
+325.13
+16.2565
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 352628, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.273355960845947, 'TIME_S_1KI': 0.029133693186150694, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 997.6947084188461, 'W': 73.02, 'J_1KI': 2.8293122168938547, 'W_1KI': 0.20707374343500798, 'W_D': 56.76349999999999, 'J_D': 775.5771512097119, 'W_D_1KI': 0.16097275315630066, 'J_D_1KI': 0.00045649453008921774}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..f8ae472
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 348362, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.331048488616943, "TIME_S_1KI": 0.029656071812129176, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1019.3632760500908, "W": 73.39, "J_1KI": 2.9261609361815895, "W_1KI": 0.2106716576434858, "W_D": 57.056, "J_D": 792.4893184127807, "W_D_1KI": 0.1637836503407375, "J_D_1KI": 0.0004701536055618509}
diff --git a/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..7fb17e1
--- /dev/null
+++ b/pytorch/output_synthetic_16core_old2/xeon_4216_16_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.01948690414428711}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([3543, 2601, 4811,  ..., 3171, 1181, 2171]),
+       values=tensor([0.9467, 0.6961, 0.1720,  ..., 0.4974, 0.2968, 0.3956]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.9876, 0.5815, 0.6649,  ..., 0.6796, 0.2344, 0.7286])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.01948690414428711 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53882', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.6240589618682861}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([ 267,  783, 3915,  ..., 3618, 4520, 1464]),
+       values=tensor([0.2837, 0.8920, 0.5250,  ..., 0.9331, 0.1091, 0.1041]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.6941, 0.6065, 0.9857,  ..., 0.4180, 0.3910, 0.2569])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 1.6240589618682861 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:16}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '348362', '-ss', '5000', '-sd', '5e-05', '-c', '16']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.331048488616943}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([2866, 1356, 2436,  ...,  421, 1796, 3666]),
+       values=tensor([0.0261, 0.5356, 0.3907,  ..., 0.0828, 0.6288, 0.2100]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.2739, 0.9422, 0.5483,  ..., 0.7719, 0.1377, 0.3851])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.331048488616943 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([2866, 1356, 2436,  ...,  421, 1796, 3666]),
+       values=tensor([0.0261, 0.5356, 0.3907,  ..., 0.0828, 0.6288, 0.2100]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..947b6c6
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 369, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.636317253112793, "TIME_S_1KI": 28.8247080030157, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 287.8413551712036, "W": 19.688717909745684, "J_1KI": 780.0578730926927, "W_1KI": 53.35695910500185, "W_D": 4.731717909745685, "J_D": 69.17586516666414, "W_D_1KI": 12.823083766248468, "J_D_1KI": 34.750904515578505}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..5ebe6c0
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.0001.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 100000 -sd 0.0001 -c 1']
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+       values=tensor([0.6051, 0.0438, 0.3131,  ..., 0.2534, 0.4034, 0.8759]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.2165, 0.3396, 0.3912,  ..., 0.3966, 0.2262, 0.3988])
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+Shape: torch.Size([100000, 100000])
+Rows: 100000
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+NNZ: 1000000
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+Time: 0.2987680435180664 seconds
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..577e93b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 35, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.216949462890625, "TIME_S_1KI": 291.912841796875, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.8894660186768, "W": 19.469103396520016, "J_1KI": 9311.127600533622, "W_1KI": 556.260097043429, "W_D": 4.649103396520017, "J_D": 77.82042102813728, "W_D_1KI": 132.83152561485764, "J_D_1KI": 3795.1864461387895}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..4053eb6
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 100000 -sd 0.001 -c 1']
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..f8f53b9
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.01.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..57b28d4
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_0.01.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..4548586
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_1e-05.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..68f3479
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_1e-05.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..f628df3
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_5e-05.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..9e47d69
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_100000_5e-05.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..c4c82c8
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.0001.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..11eb6e5
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.0001.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..7ef7193
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4656, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.50959324836731, "TIME_S_1KI": 2.2572150447524293, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 251.83274530410765, "W": 18.665438169905315, "J_1KI": 54.08778893988566, "W_1KI": 4.008899950581038, "W_D": 3.9154381699053147, "J_D": 52.82680933713909, "W_D_1KI": 0.8409446241205573, "J_D_1KI": 0.18061525432142553}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..2fc6b06
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.001.output
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+       values=tensor([0.0473, 0.1604, 0.9975,  ..., 0.9859, 0.8513, 0.0846]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_coo)
+tensor([0.9855, 0.2654, 0.8651,  ..., 0.1300, 0.7609, 0.5597])
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..653e208
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.01.json
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+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 477, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.350482940673828, "TIME_S_1KI": 21.69912566179, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 273.24414005279544, "W": 18.805205722391392, "J_1KI": 572.8388680352106, "W_1KI": 39.42391136769684, "W_D": 3.9402057223913936, "J_D": 57.25213220953946, "W_D_1KI": 8.260389355118225, "J_D_1KI": 17.31737810297322}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..6f424ff
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.01.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.01 -c 1']
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+                       [7291, 8085, 1071,  ..., 5790, 8982, 2696]]),
+       values=tensor([0.5875, 0.4754, 0.2800,  ..., 0.9318, 0.8148, 0.1899]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.9086, 0.4261, 0.9149,  ..., 0.3266, 0.8572, 0.1589])
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+Shape: torch.Size([10000, 10000])
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+Size: 100000000
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 477 -ss 10000 -sd 0.01 -c 1']
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..8549b34
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 96, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.743469476699829, "TIME_S_1KI": 111.91114038228989, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 304.21499929428103, "W": 19.37542273156749, "J_1KI": 3168.906242648761, "W_1KI": 201.82732012049468, "W_D": 4.35542273156749, "J_D": 68.38482656955723, "W_D_1KI": 45.368986787161354, "J_D_1KI": 472.59361236626415}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..81af6dd
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.05.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.05 -c 1']
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+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_coo)
+tensor([0.1199, 0.8136, 0.1887,  ..., 0.2949, 0.2338, 0.4082])
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..76b6015
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.1.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..45037c3
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.1.output
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+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.7041, 0.1272, 0.3495,  ..., 0.0494, 0.8938, 0.9263])
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..a4dacc1
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 24, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.46686863899231, "TIME_S_1KI": 436.11952662467957, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 399.554963350296, "W": 20.111512880144534, "J_1KI": 16648.123472929, "W_1KI": 837.9797033393555, "W_D": 5.111512880144534, "J_D": 101.55030870437619, "W_D_1KI": 212.97970333935558, "J_D_1KI": 8874.154305806484}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..1daf88e
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.2.output
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+                       [4188, 3998, 9847,  ...,  123, 7399,  474]]),
+       values=tensor([0.5058, 0.7166, 0.6001,  ..., 0.1092, 0.3759, 0.5726]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_coo)
+tensor([0.2267, 0.1952, 0.5201,  ..., 0.2235, 0.6837, 0.0426])
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
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+Time: 4.359426021575928 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 24 -ss 10000 -sd 0.2 -c 1']
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..b68356d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 16, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.419291257858276, "TIME_S_1KI": 651.2057036161423, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 465.23378977775576, "W": 20.16260305783142, "J_1KI": 29077.111861109734, "W_1KI": 1260.1626911144638, "W_D": 5.332603057831417, "J_D": 123.04498198270794, "W_D_1KI": 333.28769111446354, "J_D_1KI": 20830.480694653972}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..c393ebd
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.3.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.3 -c 1']
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+tensor([0.8512, 0.6856, 0.8124,  ..., 0.2420, 0.1613, 0.1403])
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+Shape: torch.Size([10000, 10000])
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 16 -ss 10000 -sd 0.3 -c 1']
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..b44a83e
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 12, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.442309617996216, "TIME_S_1KI": 870.1924681663513, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 529.9962892150882, "W": 20.250347694110115, "J_1KI": 44166.35743459068, "W_1KI": 1687.5289745091761, "W_D": 5.387347694110115, "J_D": 140.9987783775332, "W_D_1KI": 448.945641175843, "J_D_1KI": 37412.13676465358}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..337cbf6
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.4.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.4 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 8.713038206100464}
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+                       [4324, 5636, 3501,  ..., 7942, 1067, 6520]]),
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+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_coo)
+tensor([0.4345, 0.4222, 0.9610,  ..., 0.8204, 0.1992, 0.8862])
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+Shape: torch.Size([10000, 10000])
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..fa0f9ba
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.5.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..e94cb40
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_0.5.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..0a0b522
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..a264d3c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_1e-05.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..2f81ef8
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 88829, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.388752937316895, "TIME_S_1KI": 0.11695226713479714, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 247.13048832893372, "W": 18.320263868681366, "J_1KI": 2.7820924284741886, "W_1KI": 0.20624192401897315, "W_D": 3.265263868681364, "J_D": 44.04665021061895, "W_D_1KI": 0.03675898488873413, "J_D_1KI": 0.0004138173894644106}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..c9fcfcb
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,73 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.0076372623443603516}
+
+tensor(indices=tensor([[6127, 8808, 7987,  ..., 9863, 7301, 2093],
+                       [7296, 2755, 3923,  ..., 2397, 6103, 8152]]),
+       values=tensor([0.5892, 0.5981, 0.8168,  ..., 0.7578, 0.3505, 0.3535]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_coo)
+tensor([0.6753, 0.1273, 0.1843,  ..., 0.6313, 0.9814, 0.0462])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.0076372623443603516 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 13748 -ss 10000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.6250598430633545}
+
+tensor(indices=tensor([[8545, 9615, 6776,  ..., 4541, 7389, 1783],
+                       [2725, 2801, 7177,  ..., 4412, 1297, 2977]]),
+       values=tensor([0.4451, 0.0263, 0.6494,  ..., 0.7710, 0.9307, 0.1457]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_coo)
+tensor([0.6714, 0.0844, 0.0355,  ..., 0.7557, 0.6199, 0.2211])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 1.6250598430633545 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 88829 -ss 10000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.388752937316895}
+
+tensor(indices=tensor([[7123, 9581,  502,  ..., 8292, 1811, 1740],
+                       [2309,  180, 1199,  ..., 9453,  142, 9522]]),
+       values=tensor([0.2607, 0.4465, 0.9826,  ..., 0.6284, 0.3000, 0.9777]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_coo)
+tensor([0.8554, 0.1490, 0.6747,  ..., 0.4174, 0.0830, 0.5143])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.388752937316895 seconds
+
+tensor(indices=tensor([[7123, 9581,  502,  ..., 8292, 1811, 1740],
+                       [2309,  180, 1199,  ..., 9453,  142, 9522]]),
+       values=tensor([0.2607, 0.4465, 0.9826,  ..., 0.6284, 0.3000, 0.9777]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_coo)
+tensor([0.8554, 0.1490, 0.6747,  ..., 0.4174, 0.0830, 0.5143])
+Matrix Type: synthetic
+Matrix Format: coo
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..8146996
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_0.0001.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..83c964d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_0.0001.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..74ba008
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_1e-05.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..3348752
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_1e-05.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..366d210
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_5e-05.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..b494b3e
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_500000_5e-05.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..17a88bd
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.0001.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..e769a27
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.0001.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..f559a71
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.001.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..0a36e41
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.001.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..12a9f16
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.01.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..3f1acc5
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_0.01.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..4160e7b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 16590, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.300405502319336, "TIME_S_1KI": 0.6811576553537876, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 261.3678216838837, "W": 19.315252430576436, "J_1KI": 15.754540185888104, "W_1KI": 1.164270791475373, "W_D": 4.295252430576436, "J_D": 58.121982893943795, "W_D_1KI": 0.25890611395879665, "J_D_1KI": 0.015606155151223428}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..8d0cf47
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_1e-05.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 50000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.013538360595703125}
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+       size=(50000, 50000), nnz=25000, layout=torch.sparse_coo)
+tensor([0.0873, 0.8444, 0.0285,  ..., 0.4774, 0.7606, 0.1091])
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 7755 -ss 50000 -sd 1e-05 -c 1']
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..2566adb
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3648, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.625307321548462, "TIME_S_1KI": 2.912639068406925, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 279.26390142440795, "W": 19.1978158057312, "J_1KI": 76.55260455712937, "W_1KI": 5.262559157272807, "W_D": 4.271815805731201, "J_D": 62.14060808515548, "W_D_1KI": 1.1710021397289476, "J_D_1KI": 0.32099839356604926}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..0a1510e
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,73 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 50000 -sd 5e-05 -c 1']
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+tensor([0.8998, 0.8322, 0.8839,  ..., 0.9070, 0.7807, 0.8612])
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..da426ad
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.0001.json
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+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 169951, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.636537790298462, "TIME_S_1KI": 0.06258590882253393, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 277.52449954032903, "W": 19.073681929489776, "J_1KI": 1.6329677350549807, "W_1KI": 0.11223047778177109, "W_D": 3.9656819294897776, "J_D": 57.70117677783974, "W_D_1KI": 0.02333426652087824, "J_D_1KI": 0.00013729996599536477}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..8370a4a
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.0001.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.00698089599609375}
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+                       [4828, 1803, 3833,  ..., 4200, 4607, 3969]]),
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+tensor([0.2699, 0.1259, 0.2902,  ..., 0.5608, 0.2120, 0.6284])
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..5e87b9a
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.001.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..c990150
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.001.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..3913384
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.01.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..0f764de
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.01.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..8ba4eab
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.05.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..7ad567c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.05.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..5c43705
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.1.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..cbc69f8
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.1.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..5dfbfa3
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.2.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..0f48fc2
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.2.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..4ea350f
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.3.json
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..acc55ea
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.3.output
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..2247a4d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 49, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.454669713973999, "TIME_S_1KI": 213.36060640763262, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 328.44201911926274, "W": 19.654567870656535, "J_1KI": 6702.898349372709, "W_1KI": 401.1136300133987, "W_D": 4.827567870656537, "J_D": 80.67214447593697, "W_D_1KI": 98.52179327870485, "J_D_1KI": 2010.648842422548}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..e86bcbe
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.4.output
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+                       [ 320, 1844, 4039,  ...,  199, 1958,  682]]),
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+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.4868, 0.1533, 0.6166,  ..., 0.1064, 0.1384, 0.4995])
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+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 2.131768226623535 seconds
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..73a1354
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.5.json
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+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 39, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.323477506637573, "TIME_S_1KI": 264.7045514522455, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 353.88449090957636, "W": 19.947987313164067, "J_1KI": 9073.961305373754, "W_1KI": 511.486854183694, "W_D": 5.0699873131640665, "J_D": 89.94340386676784, "W_D_1KI": 129.99967469651452, "J_D_1KI": 3333.324992218321}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..872645b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_0.5.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.5 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 2.67789626121521}
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+tensor(indices=tensor([[1170, 1455, 3029,  ..., 4918, 3614, 4085],
+                       [2865, 2412,  272,  ..., 2800, 4482, 2090]]),
+       values=tensor([0.0035, 0.7337, 0.9019,  ..., 0.6107, 0.8466, 0.6679]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_coo)
+tensor([0.2336, 0.9524, 0.0619,  ..., 0.3164, 0.0642, 0.1253])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 2.67789626121521 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 39 -ss 5000 -sd 0.5 -c 1']
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..0a64b2c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 644136, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.078837871551514, "TIME_S_1KI": 0.015647065016629276, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 245.636281709671, "W": 18.157029640045646, "J_1KI": 0.3813422657787657, "W_1KI": 0.028188192617778926, "W_D": 3.3670296400456454, "J_D": 45.550657656192776, "W_D_1KI": 0.005227203013099167, "J_D_1KI": 8.115061125444266e-06}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..1564166
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,456 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.008202552795410156}
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diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..0c86915
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 287374, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.518575429916382, "TIME_S_1KI": 0.0366023907170321, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 276.1757572364807, "W": 18.95351584212515, "J_1KI": 0.9610325124627862, "W_1KI": 0.06595417762958775, "W_D": 4.070515842125149, "J_D": 59.31236211824414, "W_D_1KI": 0.014164523729095702, "J_D_1KI": 4.928951028658021e-05}
diff --git a/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..f14bea5
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_coo_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,73 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.006743192672729492}
+
+tensor(indices=tensor([[3198, 1885, 1309,  ..., 2676, 1455, 1063],
+                       [ 524, 2229, 4905,  ..., 1515,  192, 4926]]),
+       values=tensor([0.2679, 0.5733, 0.1409,  ..., 0.2159, 0.8027, 0.0882]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.3135, 0.4870, 0.4806,  ..., 0.5781, 0.1076, 0.9212])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.006743192672729492 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 15571 -ss 5000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.568927526473999}
+
+tensor(indices=tensor([[1908, 3569, 1137,  ..., 1588,  848, 1956],
+                       [2393, 1784, 2395,  ...,  970,  479, 2573]]),
+       values=tensor([0.6049, 0.0335, 0.2526,  ..., 0.1747, 0.3877, 0.9979]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.2894, 0.0542, 0.1429,  ..., 0.0093, 0.6267, 0.8974])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.568927526473999 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 287374 -ss 5000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.518575429916382}
+
+tensor(indices=tensor([[4672, 3564, 2873,  ..., 1014, 1640, 4822],
+                       [3531, 3077, 2005,  ..., 4945, 1554, 4576]]),
+       values=tensor([0.2925, 0.5202, 0.6644,  ..., 0.2251, 0.7779, 0.3865]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.3673, 0.6026, 0.5524,  ..., 0.9329, 0.4025, 0.0452])
+Matrix Type: synthetic
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+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.518575429916382 seconds
+
+tensor(indices=tensor([[4672, 3564, 2873,  ..., 1014, 1640, 4822],
+                       [3531, 3077, 2005,  ..., 4945, 1554, 4576]]),
+       values=tensor([0.2925, 0.5202, 0.6644,  ..., 0.2251, 0.7779, 0.3865]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.3673, 0.6026, 0.5524,  ..., 0.9329, 0.4025, 0.0452])
+Matrix Type: synthetic
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+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.518575429916382 seconds
+
+[16.36, 16.6, 16.72, 16.76, 16.84, 16.76, 16.72, 16.68, 16.52, 16.6]
+[16.64, 16.56, 16.56, 20.08, 20.92, 22.72, 23.6, 24.28, 21.56, 20.32, 20.4, 19.84, 19.72, 19.88]
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+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 287374, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.518575429916382, 'TIME_S_1KI': 0.0366023907170321, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 276.1757572364807, 'W': 18.95351584212515}
+[16.36, 16.6, 16.72, 16.76, 16.84, 16.76, 16.72, 16.68, 16.52, 16.6, 16.8, 16.72, 16.48, 16.04, 16.28, 16.32, 16.32, 16.24, 16.52, 16.52]
+297.66
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+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 287374, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.518575429916382, 'TIME_S_1KI': 0.0366023907170321, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 276.1757572364807, 'W': 18.95351584212515, 'J_1KI': 0.9610325124627862, 'W_1KI': 0.06595417762958775, 'W_D': 4.070515842125149, 'J_D': 59.31236211824414, 'W_D_1KI': 0.014164523729095702, 'J_D_1KI': 4.928951028658021e-05}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.json
index 1bdafea..86e8888 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 426, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.062527418136597, "TIME_S_1KI": 23.62095638060234, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 310.93658995628357, "W": 22.910177826692795, "J_1KI": 729.8980984889287, "W_1KI": 53.77976015655585, "W_D": 4.4831778266927955, "J_D": 60.845622244596484, "W_D_1KI": 10.52389161195492, "J_D_1KI": 24.70397092008197}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 451, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.429136514663696, "TIME_S_1KI": 23.12447120768004, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 326.846988697052, "W": 22.30127348306853, "J_1KI": 724.7161611908026, "W_1KI": 49.44849996245794, "W_D": 3.7152734830685326, "J_D": 54.45096895694735, "W_D_1KI": 8.23785694693688, "J_D_1KI": 18.265758197199293}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.output
index 6496241..f8bc4a8 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.4617955684661865}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.325932502746582}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      14,      23,  ...,  999981,
-                             999993, 1000000]),
-       col_indices=tensor([ 4955,  8657, 25975,  ..., 77712, 83219, 89598]),
-       values=tensor([0.6839, 0.0631, 0.2295,  ..., 0.4308, 0.9509, 0.3745]),
+tensor(crow_indices=tensor([      0,       6,      14,  ...,  999981,
+                             999990, 1000000]),
+       col_indices=tensor([22036, 42901, 43352,  ..., 54182, 64519, 79020]),
+       values=tensor([0.6047, 0.8603, 0.3318,  ..., 0.9013, 0.6992, 0.1471]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8318, 0.0587, 0.7825,  ..., 0.4905, 0.7506, 0.0148])
+tensor([0.6244, 0.4084, 0.4276,  ..., 0.6633, 0.7989, 0.2197])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 2.4617955684661865 seconds
+Time: 2.325932502746582 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 426 -ss 100000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.062527418136597}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 451 -ss 100000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.429136514663696}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       9,      16,  ...,  999978,
-                             999987, 1000000]),
-       col_indices=tensor([ 4266, 12843, 25231,  ..., 84479, 87700, 95752]),
-       values=tensor([0.9986, 0.4680, 0.6719,  ..., 0.1198, 0.1607, 0.3222]),
+tensor(crow_indices=tensor([      0,      14,      30,  ...,  999979,
+                             999990, 1000000]),
+       col_indices=tensor([ 3062,  9122, 10011,  ..., 76699, 91842, 98883]),
+       values=tensor([0.0641, 0.1904, 0.9530,  ..., 0.2612, 0.7327, 0.9970]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1538, 0.6601, 0.2448,  ..., 0.8405, 0.0282, 0.7640])
+tensor([0.3371, 0.7219, 0.6879,  ..., 0.7440, 0.2171, 0.2377])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.062527418136597 seconds
+Time: 10.429136514663696 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       9,      16,  ...,  999978,
-                             999987, 1000000]),
-       col_indices=tensor([ 4266, 12843, 25231,  ..., 84479, 87700, 95752]),
-       values=tensor([0.9986, 0.4680, 0.6719,  ..., 0.1198, 0.1607, 0.3222]),
+tensor(crow_indices=tensor([      0,      14,      30,  ...,  999979,
+                             999990, 1000000]),
+       col_indices=tensor([ 3062,  9122, 10011,  ..., 76699, 91842, 98883]),
+       values=tensor([0.0641, 0.1904, 0.9530,  ..., 0.2612, 0.7327, 0.9970]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1538, 0.6601, 0.2448,  ..., 0.8405, 0.0282, 0.7640])
+tensor([0.3371, 0.7219, 0.6879,  ..., 0.7440, 0.2171, 0.2377])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.062527418136597 seconds
+Time: 10.429136514663696 seconds
 
-[20.6, 20.44, 20.32, 20.4, 20.44, 20.84, 21.0, 20.88, 20.52, 20.4]
-[20.4, 20.24, 20.28, 24.24, 25.56, 27.32, 28.24, 28.56, 25.84, 24.76, 24.56, 24.68, 24.68]
-13.571985006332397
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 426, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.062527418136597, 'TIME_S_1KI': 23.62095638060234, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.93658995628357, 'W': 22.910177826692795}
-[20.6, 20.44, 20.32, 20.4, 20.44, 20.84, 21.0, 20.88, 20.52, 20.4, 20.64, 20.56, 20.4, 20.2, 20.36, 20.36, 20.12, 20.2, 20.4, 20.56]
-368.53999999999996
-18.427
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 426, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.062527418136597, 'TIME_S_1KI': 23.62095638060234, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.93658995628357, 'W': 22.910177826692795, 'J_1KI': 729.8980984889287, 'W_1KI': 53.77976015655585, 'W_D': 4.4831778266927955, 'J_D': 60.845622244596484, 'W_D_1KI': 10.52389161195492, 'J_D_1KI': 24.70397092008197}
+[20.6, 20.72, 21.0, 21.12, 21.12, 21.0, 20.88, 20.48, 20.52, 20.48]
+[20.56, 20.56, 21.24, 22.16, 24.12, 25.08, 25.96, 25.56, 25.32, 24.28, 24.44, 24.44, 24.6, 24.92]
+14.655978679656982
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 451, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.429136514663696, 'TIME_S_1KI': 23.12447120768004, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.846988697052, 'W': 22.30127348306853}
+[20.6, 20.72, 21.0, 21.12, 21.12, 21.0, 20.88, 20.48, 20.52, 20.48, 20.28, 20.24, 20.28, 20.36, 20.52, 20.64, 20.76, 20.64, 20.52, 20.48]
+371.71999999999997
+18.586
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 451, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.429136514663696, 'TIME_S_1KI': 23.12447120768004, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.846988697052, 'W': 22.30127348306853, 'J_1KI': 724.7161611908026, 'W_1KI': 49.44849996245794, 'W_D': 3.7152734830685326, 'J_D': 54.45096895694735, 'W_D_1KI': 8.23785694693688, 'J_D_1KI': 18.265758197199293}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.json
index 9db277a..ccc36b0 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 22.583206176757812, "TIME_S_1KI": 225.83206176757812, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 712.6889356327057, "W": 23.468990304831248, "J_1KI": 7126.889356327057, "W_1KI": 234.68990304831246, "W_D": 4.866990304831251, "J_D": 147.7971610636712, "W_D_1KI": 48.66990304831251, "J_D_1KI": 486.6990304831251}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 23.465809106826782, "TIME_S_1KI": 234.65809106826782, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 674.7585023498535, "W": 22.99999113724053, "J_1KI": 6747.5850234985355, "W_1KI": 229.9999113724053, "W_D": 4.333991137240535, "J_D": 127.1477606887818, "W_D_1KI": 43.339911372405346, "J_D_1KI": 433.3991137240535}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.output
index 1c6b2fe..7117084 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 22.583206176757812}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 23.465809106826782}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      115,      205,  ...,  9999778,
-                             9999875, 10000000]),
-       col_indices=tensor([ 1402,  2097,  3965,  ..., 98532, 99293, 99429]),
-       values=tensor([0.3375, 0.2900, 0.6603,  ..., 0.1611, 0.9536, 0.6072]),
+tensor(crow_indices=tensor([       0,      104,      211,  ...,  9999807,
+                             9999895, 10000000]),
+       col_indices=tensor([   67,  3512,  6711,  ..., 97065, 98136, 98202]),
+       values=tensor([0.4389, 0.5287, 0.7636,  ..., 0.3983, 0.3336, 0.7095]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8425, 0.9618, 0.5102,  ..., 0.7524, 0.4133, 0.9192])
+tensor([0.8197, 0.9888, 0.1798,  ..., 0.6227, 0.7072, 0.7961])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,16 +16,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 22.583206176757812 seconds
+Time: 23.465809106826782 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      115,      205,  ...,  9999778,
-                             9999875, 10000000]),
-       col_indices=tensor([ 1402,  2097,  3965,  ..., 98532, 99293, 99429]),
-       values=tensor([0.3375, 0.2900, 0.6603,  ..., 0.1611, 0.9536, 0.6072]),
+tensor(crow_indices=tensor([       0,      104,      211,  ...,  9999807,
+                             9999895, 10000000]),
+       col_indices=tensor([   67,  3512,  6711,  ..., 97065, 98136, 98202]),
+       values=tensor([0.4389, 0.5287, 0.7636,  ..., 0.3983, 0.3336, 0.7095]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8425, 0.9618, 0.5102,  ..., 0.7524, 0.4133, 0.9192])
+tensor([0.8197, 0.9888, 0.1798,  ..., 0.6227, 0.7072, 0.7961])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -33,13 +33,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 22.583206176757812 seconds
+Time: 23.465809106826782 seconds
 
-[20.72, 21.0, 21.0, 21.04, 20.76, 20.44, 20.16, 20.32, 20.24, 20.52]
-[20.4, 20.36, 21.96, 22.92, 24.92, 26.56, 28.48, 28.48, 28.88, 28.96, 27.92, 26.48, 25.56, 24.72, 24.64, 24.76, 24.96, 24.76, 24.6, 24.56, 24.6, 24.36, 24.76, 24.88, 24.92, 24.68, 24.6, 24.36, 24.04]
-30.367260217666626
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 22.583206176757812, 'TIME_S_1KI': 225.83206176757812, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 712.6889356327057, 'W': 23.468990304831248}
-[20.72, 21.0, 21.0, 21.04, 20.76, 20.44, 20.16, 20.32, 20.24, 20.52, 20.24, 20.24, 20.2, 20.4, 20.64, 21.08, 21.24, 21.12, 21.0, 20.84]
-372.03999999999996
-18.601999999999997
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 22.583206176757812, 'TIME_S_1KI': 225.83206176757812, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 712.6889356327057, 'W': 23.468990304831248, 'J_1KI': 7126.889356327057, 'W_1KI': 234.68990304831246, 'W_D': 4.866990304831251, 'J_D': 147.7971610636712, 'W_D_1KI': 48.66990304831251, 'J_D_1KI': 486.6990304831251}
+[20.6, 20.76, 20.68, 20.68, 20.8, 20.8, 20.8, 20.92, 20.88, 20.52]
+[20.24, 20.24, 20.72, 21.88, 23.68, 25.04, 27.2, 28.28, 28.24, 27.24, 26.04, 25.4, 24.8, 24.8, 24.92, 24.84, 24.52, 24.48, 24.36, 24.28, 24.6, 24.84, 24.96, 24.72, 24.64, 24.52, 24.8, 24.6]
+29.337337493896484
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 23.465809106826782, 'TIME_S_1KI': 234.65809106826782, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 674.7585023498535, 'W': 22.99999113724053}
+[20.6, 20.76, 20.68, 20.68, 20.8, 20.8, 20.8, 20.92, 20.88, 20.52, 20.48, 20.52, 20.76, 21.0, 20.76, 20.8, 20.8, 20.76, 20.64, 20.32]
+373.31999999999994
+18.665999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 23.465809106826782, 'TIME_S_1KI': 234.65809106826782, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 674.7585023498535, 'W': 22.99999113724053, 'J_1KI': 6747.5850234985355, 'W_1KI': 229.9999113724053, 'W_D': 4.333991137240535, 'J_D': 127.1477606887818, 'W_D_1KI': 43.339911372405346, 'J_D_1KI': 433.3991137240535}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..c2d358d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 225.18899512290955, "TIME_S_1KI": 2251.8899512290955, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 6281.944063549044, "W": 24.226548404040123, "J_1KI": 62819.440635490435, "W_1KI": 242.26548404040125, "W_D": 5.685548404040119, "J_D": 1474.262715807439, "W_D_1KI": 56.855484040401194, "J_D_1KI": 568.5548404040119}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..f52b83d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 225.18899512290955}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       978,      1954,  ...,
+                             99998005,  99998965, 100000000]),
+       col_indices=tensor([  209,   344,   348,  ..., 99501, 99539, 99765]),
+       values=tensor([0.9950, 0.2460, 0.5493,  ..., 0.2748, 0.4865, 0.5723]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.9371, 0.2528, 0.1068,  ..., 0.3070, 0.8056, 0.1542])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 225.18899512290955 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       978,      1954,  ...,
+                             99998005,  99998965, 100000000]),
+       col_indices=tensor([  209,   344,   348,  ..., 99501, 99539, 99765]),
+       values=tensor([0.9950, 0.2460, 0.5493,  ..., 0.2748, 0.4865, 0.5723]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.9371, 0.2528, 0.1068,  ..., 0.3070, 0.8056, 0.1542])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 225.18899512290955 seconds
+
+[22.56, 22.04, 21.08, 20.72, 20.6, 20.8, 20.88, 20.84, 20.6, 20.52]
+[20.32, 20.16, 23.24, 23.24, 24.44, 27.2, 28.68, 29.56, 27.16, 26.44, 27.56, 29.08, 31.16, 30.96, 30.08, 28.12, 26.16, 25.6, 26.4, 28.84, 31.16, 32.92, 33.4, 32.0, 30.72, 30.72, 29.72, 28.08, 27.88, 27.2, 26.56, 26.96, 27.92, 28.84, 29.48, 30.08, 29.8, 29.44, 28.88, 28.36, 27.68, 26.88, 26.2, 25.88, 24.92, 24.84, 25.04, 25.04, 25.04, 24.88, 24.84, 24.88, 24.6, 24.68, 24.84, 24.76, 24.92, 25.04, 24.88, 24.64, 24.64, 24.64, 24.88, 24.76, 24.88, 24.92, 24.96, 25.0, 24.96, 24.96, 25.08, 25.2, 24.92, 25.52, 25.56, 25.6, 25.48, 25.64, 25.2, 25.08, 24.96, 25.04, 25.0, 25.2, 25.28, 25.08, 24.96, 24.84, 25.08, 25.12, 25.36, 25.36, 25.56, 25.52, 25.52, 25.6, 25.56, 25.28, 25.2, 25.08, 24.84, 24.88, 24.92, 25.12, 24.96, 25.12, 25.0, 25.08, 25.04, 25.08, 25.12, 25.08, 24.96, 24.96, 24.8, 24.72, 24.72, 24.68, 24.64, 24.68, 25.0, 24.84, 25.0, 24.96, 24.84, 24.76, 25.04, 24.88, 25.2, 25.08, 24.96, 24.76, 24.76, 24.92, 24.8, 24.8, 24.96, 25.0, 25.12, 25.04, 25.16, 24.96, 24.96, 24.64, 24.72, 24.92, 25.04, 25.04, 24.92, 24.64, 24.72, 24.56, 24.64, 24.64, 24.84, 24.8, 24.72, 24.72, 24.88, 25.12, 25.08, 25.2, 25.32, 25.12, 25.12, 25.24, 25.08, 24.88, 25.16, 25.04, 24.92, 25.12, 25.08, 24.76, 25.16, 25.08, 25.24, 25.32, 25.48, 25.48, 25.32, 25.24, 25.2, 24.92, 24.8, 24.72, 24.76, 24.68, 24.56, 24.88, 24.8, 24.68, 24.8, 24.6, 24.44, 24.64, 25.0, 24.96, 25.08, 25.0, 24.8, 24.8, 24.56, 24.44, 24.68, 24.64, 24.68, 24.92, 25.0, 25.12, 25.2, 25.0, 24.92, 24.72, 24.6, 25.04, 25.2, 25.4, 25.32, 25.2, 25.04, 24.8, 24.72, 24.72, 24.68, 24.68, 24.76, 24.8, 24.92, 24.92, 24.72, 24.76, 24.84, 24.76, 25.0, 25.12, 25.0, 24.92, 24.92, 24.68, 24.72, 24.52, 24.68, 24.72, 24.76, 24.76, 24.68]
+259.30000257492065
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 225.18899512290955, 'TIME_S_1KI': 2251.8899512290955, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 6281.944063549044, 'W': 24.226548404040123}
+[22.56, 22.04, 21.08, 20.72, 20.6, 20.8, 20.88, 20.84, 20.6, 20.52, 20.16, 20.36, 20.12, 19.92, 19.96, 20.16, 20.2, 20.24, 20.48, 20.4]
+370.82000000000005
+18.541000000000004
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 225.18899512290955, 'TIME_S_1KI': 2251.8899512290955, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 6281.944063549044, 'W': 24.226548404040123, 'J_1KI': 62819.440635490435, 'W_1KI': 242.26548404040125, 'W_D': 5.685548404040119, 'J_D': 1474.262715807439, 'W_D_1KI': 56.855484040401194, 'J_D_1KI': 568.5548404040119}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.json
index 21e4694..d3bede1 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3104, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.44502592086792, "TIME_S_1KI": 3.3650212373930155, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 348.514190454483, "W": 23.934119588627873, "J_1KI": 112.27905620311952, "W_1KI": 7.710734403552794, "W_D": 5.569119588627871, "J_D": 81.09415505290025, "W_D_1KI": 1.794175125202278, "J_D_1KI": 0.578020336727538}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3013, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.526468276977539, "TIME_S_1KI": 3.4936834639819243, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 342.6458623886109, "W": 23.39607518698974, "J_1KI": 113.72249000617687, "W_1KI": 7.7650432084267305, "W_D": 5.13707518698974, "J_D": 75.2347367467881, "W_D_1KI": 1.7049701915000797, "J_D_1KI": 0.5658712882509392}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.output
index 9aacb6b..8bf6a02 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.3382716178894043}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.3484072685241699}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      4,  ...,  99999, 100000,
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99998, 100000,
                             100000]),
-       col_indices=tensor([91034, 37166, 45389,  ..., 40200, 40353,   102]),
-       values=tensor([0.2917, 0.4189, 0.5553,  ..., 0.7170, 0.1120, 0.1885]),
+       col_indices=tensor([31509, 39574,  2838,  ..., 40555, 30553, 63171]),
+       values=tensor([0.0296, 0.4364, 0.3072,  ..., 0.7619, 0.5758, 0.8083]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2165, 0.9661, 0.1946,  ..., 0.3640, 0.8184, 0.1773])
+tensor([0.7050, 0.3658, 0.2226,  ..., 0.8215, 0.3821, 0.1635])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 0.3382716178894043 seconds
+Time: 0.3484072685241699 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3104 -ss 100000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.44502592086792}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3013 -ss 100000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.526468276977539}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      2,      2,  ...,  99997,  99998,
                             100000]),
-       col_indices=tensor([  252, 18132, 64781,  ..., 90653, 85542, 48452]),
-       values=tensor([0.2676, 0.4026, 0.9927,  ..., 0.1189, 0.3190, 0.1177]),
+       col_indices=tensor([49537, 94006, 52089,  ..., 12901, 18523, 59775]),
+       values=tensor([0.4676, 0.9884, 0.1511,  ..., 0.3386, 0.6520, 0.3542]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6783, 0.3478, 0.4100,  ..., 0.2741, 0.0736, 0.8098])
+tensor([0.1323, 0.2430, 0.2483,  ..., 0.0221, 0.3749, 0.4623])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.44502592086792 seconds
+Time: 10.526468276977539 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      2,      2,  ...,  99997,  99998,
                             100000]),
-       col_indices=tensor([  252, 18132, 64781,  ..., 90653, 85542, 48452]),
-       values=tensor([0.2676, 0.4026, 0.9927,  ..., 0.1189, 0.3190, 0.1177]),
+       col_indices=tensor([49537, 94006, 52089,  ..., 12901, 18523, 59775]),
+       values=tensor([0.4676, 0.9884, 0.1511,  ..., 0.3386, 0.6520, 0.3542]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6783, 0.3478, 0.4100,  ..., 0.2741, 0.0736, 0.8098])
+tensor([0.1323, 0.2430, 0.2483,  ..., 0.0221, 0.3749, 0.4623])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.44502592086792 seconds
+Time: 10.526468276977539 seconds
 
-[20.36, 20.36, 20.36, 20.36, 20.36, 20.52, 20.6, 20.76, 20.96, 20.92]
-[20.92, 20.6, 23.92, 25.4, 27.24, 28.24, 28.24, 29.08, 26.32, 25.76, 25.4, 25.2, 25.44, 25.64]
-14.56139588356018
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3104, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.44502592086792, 'TIME_S_1KI': 3.3650212373930155, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.514190454483, 'W': 23.934119588627873}
-[20.36, 20.36, 20.36, 20.36, 20.36, 20.52, 20.6, 20.76, 20.96, 20.92, 20.24, 20.2, 20.48, 20.2, 20.2, 20.2, 20.12, 20.28, 20.28, 20.6]
-367.30000000000007
-18.365000000000002
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3104, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.44502592086792, 'TIME_S_1KI': 3.3650212373930155, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.514190454483, 'W': 23.934119588627873, 'J_1KI': 112.27905620311952, 'W_1KI': 7.710734403552794, 'W_D': 5.569119588627871, 'J_D': 81.09415505290025, 'W_D_1KI': 1.794175125202278, 'J_D_1KI': 0.578020336727538}
+[20.36, 20.28, 20.28, 20.36, 20.48, 20.64, 20.6, 20.6, 20.32, 20.12]
+[20.04, 20.24, 23.28, 24.48, 26.12, 27.16, 28.36, 25.92, 25.92, 25.8, 25.2, 25.28, 25.68, 25.48]
+14.645442008972168
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3013, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.526468276977539, 'TIME_S_1KI': 3.4936834639819243, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.6458623886109, 'W': 23.39607518698974}
+[20.36, 20.28, 20.28, 20.36, 20.48, 20.64, 20.6, 20.6, 20.32, 20.12, 20.04, 19.96, 19.8, 20.16, 20.2, 20.04, 20.2, 20.44, 20.32, 20.48]
+365.18
+18.259
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3013, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.526468276977539, 'TIME_S_1KI': 3.4936834639819243, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.6458623886109, 'W': 23.39607518698974, 'J_1KI': 113.72249000617687, 'W_1KI': 7.7650432084267305, 'W_D': 5.13707518698974, 'J_D': 75.2347367467881, 'W_D_1KI': 1.7049701915000797, 'J_D_1KI': 0.5658712882509392}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_5e-05.json
index 9e7c05a..234b80f 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_5e-05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 868, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.049697399139404, "TIME_S_1KI": 12.730066128040788, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 347.2841278934479, "W": 23.818433138573972, "J_1KI": 400.0969215362303, "W_1KI": 27.44059117347232, "W_D": 5.198433138573975, "J_D": 75.79563728809362, "W_D_1KI": 5.988978270246515, "J_D_1KI": 6.899744550975249}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 825, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.260325908660889, "TIME_S_1KI": 12.436758677164715, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 303.46346472740174, "W": 22.318076871815833, "J_1KI": 367.8345026998809, "W_1KI": 27.052214390079797, "W_D": 3.8030768718158328, "J_D": 51.71121556639671, "W_D_1KI": 4.609790147655556, "J_D_1KI": 5.587624421400673}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_5e-05.output
index 5c6adba..89a3369 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_5e-05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_100000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 5e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.209580421447754}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.2715063095092773}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,     11,  ..., 499991, 499997,
+tensor(crow_indices=tensor([     0,      4,      6,  ..., 499995, 499997,
                             500000]),
-       col_indices=tensor([ 8709, 33303, 39829,  ..., 65447, 85964, 93697]),
-       values=tensor([0.2765, 0.3303, 0.4846,  ..., 0.1571, 0.7749, 0.0327]),
+       col_indices=tensor([19868, 76207, 77915,  ..., 16706, 63731, 82229]),
+       values=tensor([0.0924, 0.2177, 0.7599,  ..., 0.8921, 0.4124, 0.6805]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.2847, 0.7446, 0.1507,  ..., 0.7274, 0.5755, 0.0187])
+tensor([0.4234, 0.3489, 0.0579,  ..., 0.4277, 0.7771, 0.0656])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 1.209580421447754 seconds
+Time: 1.2715063095092773 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 868 -ss 100000 -sd 5e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.049697399139404}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 825 -ss 100000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.260325908660889}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     19,  ..., 499985, 499993,
+tensor(crow_indices=tensor([     0,      5,     13,  ..., 499993, 499996,
                             500000]),
-       col_indices=tensor([  930,  5720, 18229,  ..., 18263, 29630, 53753]),
-       values=tensor([0.0983, 0.1468, 0.4729,  ..., 0.5988, 0.3077, 0.5585]),
+       col_indices=tensor([ 8610, 58283, 67606,  ..., 37260, 87311, 96234]),
+       values=tensor([0.5221, 0.7908, 0.9284,  ..., 0.4846, 0.8145, 0.6299]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.4408, 0.1732, 0.5273,  ..., 0.8772, 0.6136, 0.9894])
+tensor([0.0481, 0.3492, 0.7798,  ..., 0.2984, 0.7068, 0.8860])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 11.049697399139404 seconds
+Time: 10.260325908660889 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     19,  ..., 499985, 499993,
+tensor(crow_indices=tensor([     0,      5,     13,  ..., 499993, 499996,
                             500000]),
-       col_indices=tensor([  930,  5720, 18229,  ..., 18263, 29630, 53753]),
-       values=tensor([0.0983, 0.1468, 0.4729,  ..., 0.5988, 0.3077, 0.5585]),
+       col_indices=tensor([ 8610, 58283, 67606,  ..., 37260, 87311, 96234]),
+       values=tensor([0.5221, 0.7908, 0.9284,  ..., 0.4846, 0.8145, 0.6299]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.4408, 0.1732, 0.5273,  ..., 0.8772, 0.6136, 0.9894])
+tensor([0.0481, 0.3492, 0.7798,  ..., 0.2984, 0.7068, 0.8860])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 11.049697399139404 seconds
+Time: 10.260325908660889 seconds
 
-[20.76, 20.76, 20.64, 20.6, 20.48, 20.24, 20.32, 20.36, 20.4, 20.64]
-[20.8, 20.68, 23.8, 24.88, 26.88, 27.84, 27.84, 28.76, 26.08, 26.28, 25.56, 25.36, 25.48, 25.4]
-14.580477476119995
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 868, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.049697399139404, 'TIME_S_1KI': 12.730066128040788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 347.2841278934479, 'W': 23.818433138573972}
-[20.76, 20.76, 20.64, 20.6, 20.48, 20.24, 20.32, 20.36, 20.4, 20.64, 20.68, 20.8, 20.6, 20.6, 20.92, 21.0, 21.0, 21.08, 21.04, 21.04]
-372.4
-18.619999999999997
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 868, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.049697399139404, 'TIME_S_1KI': 12.730066128040788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 347.2841278934479, 'W': 23.818433138573972, 'J_1KI': 400.0969215362303, 'W_1KI': 27.44059117347232, 'W_D': 5.198433138573975, 'J_D': 75.79563728809362, 'W_D_1KI': 5.988978270246515, 'J_D_1KI': 6.899744550975249}
+[20.16, 20.24, 20.36, 20.52, 20.56, 20.56, 20.6, 20.6, 20.68, 20.64]
+[20.8, 20.88, 21.32, 22.88, 24.76, 25.28, 26.12, 26.08, 25.2, 24.52, 24.56, 24.56, 24.52]
+13.59720492362976
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 825, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.260325908660889, 'TIME_S_1KI': 12.436758677164715, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 303.46346472740174, 'W': 22.318076871815833}
+[20.16, 20.24, 20.36, 20.52, 20.56, 20.56, 20.6, 20.6, 20.68, 20.64, 21.08, 20.6, 20.44, 20.44, 20.6, 20.76, 20.88, 20.8, 20.44, 20.56]
+370.3
+18.515
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 825, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.260325908660889, 'TIME_S_1KI': 12.436758677164715, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 303.46346472740174, 'W': 22.318076871815833, 'J_1KI': 367.8345026998809, 'W_1KI': 27.052214390079797, 'W_D': 3.8030768718158328, 'J_D': 51.71121556639671, 'W_D_1KI': 4.609790147655556, 'J_D_1KI': 5.587624421400673}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.json
index 669cf9a..51ec237 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 32669, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.073116302490234, "TIME_S_1KI": 0.33894873741131454, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 342.2707403564453, "W": 23.410284009847928, "J_1KI": 10.476927373242074, "W_1KI": 0.7165901622286549, "W_D": 4.943284009847925, "J_D": 72.2734280853271, "W_D_1KI": 0.15131421255159097, "J_D_1KI": 0.004631736892821665}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 32768, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.543907403945923, "TIME_S_1KI": 0.32177451794268563, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 331.4041768455506, "W": 22.589687525895688, "J_1KI": 10.113652857835406, "W_1KI": 0.6893825538908596, "W_D": 4.118687525895684, "J_D": 60.423600266218195, "W_D_1KI": 0.1256923683439845, "J_D_1KI": 0.0038358266706538242}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.output
index 4a81d8b..67ba37e 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.03962588310241699}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.040886878967285156}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ...,  9995,  9999, 10000]),
-       col_indices=tensor([5736, 4740, 5169,  ..., 5050, 7314, 6933]),
-       values=tensor([0.2904, 0.9920, 0.0901,  ..., 0.6475, 0.2992, 0.6153]),
+tensor(crow_indices=tensor([    0,     2,     3,  ...,  9998, 10000, 10000]),
+       col_indices=tensor([4512, 6064, 8080,  ..., 2543, 8282, 8614]),
+       values=tensor([0.3924, 0.2795, 0.3542,  ..., 0.2395, 0.4436, 0.6954]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.6798, 0.4263, 0.2506,  ..., 0.2181, 0.0906, 0.7562])
+tensor([0.0035, 0.7615, 0.3613,  ..., 0.8492, 0.8058, 0.7078])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 0.03962588310241699 seconds
+Time: 0.040886878967285156 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 26497 -ss 10000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.51607346534729}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 25680 -ss 10000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.228740930557251}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9998, 10000]),
-       col_indices=tensor([5374, 4189, 5897,  ..., 9913, 4567, 8496]),
-       values=tensor([0.8167, 0.6460, 0.7856,  ..., 0.9381, 0.0308, 0.1187]),
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9994,  9997, 10000]),
+       col_indices=tensor([6444, 5673, 2875,  ..., 1711, 2717, 9846]),
+       values=tensor([0.5286, 0.5997, 0.8297,  ..., 0.2375, 0.6173, 0.2781]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.9083, 0.0911, 0.6427,  ..., 0.4641, 0.3576, 0.6926])
+tensor([0.4725, 0.3238, 0.8742,  ..., 0.2370, 0.4782, 0.2133])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 8.51607346534729 seconds
+Time: 8.228740930557251 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32669 -ss 10000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.073116302490234}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32768 -ss 10000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.543907403945923}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     5,  ...,  9999, 10000, 10000]),
-       col_indices=tensor([2638,  262,  675,  ..., 9893, 8606, 4272]),
-       values=tensor([0.0918, 0.5777, 0.8540,  ..., 0.4523, 0.7955, 0.7135]),
+tensor(crow_indices=tensor([    0,     2,     3,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([2167, 2598, 1557,  ..., 6726, 6700, 6866]),
+       values=tensor([0.0219, 0.6259, 0.0041,  ..., 0.9746, 0.7379, 0.9575]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1165, 0.4058, 0.2834,  ..., 0.7342, 0.6568, 0.2677])
+tensor([0.1977, 0.2810, 0.1152,  ..., 0.4726, 0.1521, 0.8391])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 11.073116302490234 seconds
+Time: 10.543907403945923 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     5,  ...,  9999, 10000, 10000]),
-       col_indices=tensor([2638,  262,  675,  ..., 9893, 8606, 4272]),
-       values=tensor([0.0918, 0.5777, 0.8540,  ..., 0.4523, 0.7955, 0.7135]),
+tensor(crow_indices=tensor([    0,     2,     3,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([2167, 2598, 1557,  ..., 6726, 6700, 6866]),
+       values=tensor([0.0219, 0.6259, 0.0041,  ..., 0.9746, 0.7379, 0.9575]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1165, 0.4058, 0.2834,  ..., 0.7342, 0.6568, 0.2677])
+tensor([0.1977, 0.2810, 0.1152,  ..., 0.4726, 0.1521, 0.8391])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 11.073116302490234 seconds
+Time: 10.543907403945923 seconds
 
-[20.76, 20.6, 20.6, 20.6, 20.44, 20.48, 20.48, 20.48, 20.48, 20.64]
-[20.44, 20.4, 23.56, 25.72, 27.84, 28.36, 29.08, 29.08, 26.36, 24.68, 23.72, 23.68, 23.44, 23.32]
-14.620529174804688
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32669, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.073116302490234, 'TIME_S_1KI': 0.33894873741131454, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.2707403564453, 'W': 23.410284009847928}
-[20.76, 20.6, 20.6, 20.6, 20.44, 20.48, 20.48, 20.48, 20.48, 20.64, 20.6, 20.88, 20.92, 20.76, 20.64, 20.48, 20.16, 20.16, 20.08, 20.2]
-369.34000000000003
-18.467000000000002
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32669, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.073116302490234, 'TIME_S_1KI': 0.33894873741131454, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.2707403564453, 'W': 23.410284009847928, 'J_1KI': 10.476927373242074, 'W_1KI': 0.7165901622286549, 'W_D': 4.943284009847925, 'J_D': 72.2734280853271, 'W_D_1KI': 0.15131421255159097, 'J_D_1KI': 0.004631736892821665}
+[20.56, 20.44, 20.44, 20.4, 20.4, 20.4, 20.44, 20.4, 20.44, 20.48]
+[20.44, 20.6, 22.44, 24.08, 26.08, 26.52, 27.2, 27.2, 25.04, 24.76, 23.52, 23.44, 23.24, 23.12]
+14.67059588432312
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32768, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.543907403945923, 'TIME_S_1KI': 0.32177451794268563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 331.4041768455506, 'W': 22.589687525895688}
+[20.56, 20.44, 20.44, 20.4, 20.4, 20.4, 20.44, 20.4, 20.44, 20.48, 20.76, 20.8, 20.72, 20.52, 20.4, 20.48, 20.6, 20.64, 20.6, 20.8]
+369.4200000000001
+18.471000000000004
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32768, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.543907403945923, 'TIME_S_1KI': 0.32177451794268563, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 331.4041768455506, 'W': 22.589687525895688, 'J_1KI': 10.113652857835406, 'W_1KI': 0.6893825538908596, 'W_D': 4.118687525895684, 'J_D': 60.423600266218195, 'W_D_1KI': 0.1256923683439845, 'J_D_1KI': 0.0038358266706538242}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.json
index 3604623..11b4007 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4348, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.135539293289185, "TIME_S_1KI": 2.3310807942247433, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 299.55518106460573, "W": 22.140804095253714, "J_1KI": 68.89493584742543, "W_1KI": 5.092181254658168, "W_D": 3.514804095253716, "J_D": 47.55372806835178, "W_D_1KI": 0.8083726070040745, "J_D_1KI": 0.1859182628804219}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4555, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.090666770935059, "TIME_S_1KI": 2.2152945710065994, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 295.87606741905216, "W": 21.89535079560157, "J_1KI": 64.95632654644395, "W_1KI": 4.806882721317578, "W_D": 3.6443507956015715, "J_D": 49.246810054063886, "W_D_1KI": 0.8000770133044065, "J_D_1KI": 0.1756480819548642}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.output
index 4918d44..593256a 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2414851188659668}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2305009365081787}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      9,     19,  ...,  99982,  99991,
+tensor(crow_indices=tensor([     0,      9,     18,  ...,  99987,  99992,
                             100000]),
-       col_indices=tensor([ 302, 1349, 1385,  ..., 9083, 9115, 9373]),
-       values=tensor([0.3908, 0.9700, 0.7778,  ..., 0.9299, 0.7856, 0.5693]),
+       col_indices=tensor([ 358, 1147, 1177,  ..., 5323, 6358, 7564]),
+       values=tensor([0.8466, 0.4120, 0.3659,  ..., 0.5272, 0.1337, 0.6938]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.9198, 0.1049, 0.3911,  ..., 0.9152, 0.2471, 0.8814])
+tensor([0.9531, 0.6758, 0.0878,  ..., 0.9084, 0.1040, 0.7470])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 0.2414851188659668 seconds
+Time: 0.2305009365081787 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4348 -ss 10000 -sd 0.001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.135539293289185}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4555 -ss 10000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.090666770935059}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     11,     23,  ...,  99987,  99990,
+tensor(crow_indices=tensor([     0,     10,     19,  ...,  99964,  99978,
                             100000]),
-       col_indices=tensor([  62,  627, 2703,  ..., 9273, 9381, 9947]),
-       values=tensor([0.4329, 0.2872, 0.8964,  ..., 0.9783, 0.1219, 0.9101]),
+       col_indices=tensor([1012, 1716, 2947,  ..., 9010, 9137, 9342]),
+       values=tensor([0.7676, 0.8318, 0.5067,  ..., 0.7706, 0.2340, 0.7681]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8278, 0.7584, 0.9132,  ..., 0.6086, 0.4680, 0.0616])
+tensor([0.1100, 0.7323, 0.2241,  ..., 0.7674, 0.2013, 0.4710])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.135539293289185 seconds
+Time: 10.090666770935059 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     11,     23,  ...,  99987,  99990,
+tensor(crow_indices=tensor([     0,     10,     19,  ...,  99964,  99978,
                             100000]),
-       col_indices=tensor([  62,  627, 2703,  ..., 9273, 9381, 9947]),
-       values=tensor([0.4329, 0.2872, 0.8964,  ..., 0.9783, 0.1219, 0.9101]),
+       col_indices=tensor([1012, 1716, 2947,  ..., 9010, 9137, 9342]),
+       values=tensor([0.7676, 0.8318, 0.5067,  ..., 0.7706, 0.2340, 0.7681]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8278, 0.7584, 0.9132,  ..., 0.6086, 0.4680, 0.0616])
+tensor([0.1100, 0.7323, 0.2241,  ..., 0.7674, 0.2013, 0.4710])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.135539293289185 seconds
+Time: 10.090666770935059 seconds
 
-[20.76, 20.76, 21.0, 21.0, 20.84, 20.76, 20.68, 20.44, 20.6, 20.72]
-[20.88, 20.96, 21.56, 22.88, 24.32, 25.12, 25.84, 25.2, 25.2, 24.92, 24.16, 24.04, 24.2]
-13.529552936553955
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4348, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.135539293289185, 'TIME_S_1KI': 2.3310807942247433, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 299.55518106460573, 'W': 22.140804095253714}
-[20.76, 20.76, 21.0, 21.0, 20.84, 20.76, 20.68, 20.44, 20.6, 20.72, 20.52, 20.44, 20.36, 20.68, 20.84, 20.96, 20.92, 20.68, 20.36, 20.4]
-372.52
-18.625999999999998
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4348, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.135539293289185, 'TIME_S_1KI': 2.3310807942247433, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 299.55518106460573, 'W': 22.140804095253714, 'J_1KI': 68.89493584742543, 'W_1KI': 5.092181254658168, 'W_D': 3.514804095253716, 'J_D': 47.55372806835178, 'W_D_1KI': 0.8083726070040745, 'J_D_1KI': 0.1859182628804219}
+[20.36, 20.24, 20.48, 20.48, 20.44, 20.36, 20.48, 20.24, 20.36, 20.52]
+[20.48, 20.6, 20.96, 22.12, 23.84, 24.64, 25.4, 25.16, 25.16, 25.04, 24.2, 24.24, 23.96]
+13.51319146156311
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4555, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.090666770935059, 'TIME_S_1KI': 2.2152945710065994, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 295.87606741905216, 'W': 21.89535079560157}
+[20.36, 20.24, 20.48, 20.48, 20.44, 20.36, 20.48, 20.24, 20.36, 20.52, 20.32, 20.36, 20.2, 20.04, 20.0, 20.04, 20.16, 20.24, 20.24, 20.12]
+365.02
+18.250999999999998
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4555, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.090666770935059, 'TIME_S_1KI': 2.2152945710065994, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 295.87606741905216, 'W': 21.89535079560157, 'J_1KI': 64.95632654644395, 'W_1KI': 4.806882721317578, 'W_D': 3.6443507956015715, 'J_D': 49.246810054063886, 'W_D_1KI': 0.8000770133044065, 'J_D_1KI': 0.1756480819548642}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.json
index 1a09527..8b4cbb0 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 493, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.442772150039673, "TIME_S_1KI": 21.18209361062814, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 317.52540328025816, "W": 21.825092476629965, "J_1KI": 644.0677551323695, "W_1KI": 44.26996445563888, "W_D": 3.2900924766299617, "J_D": 47.86636948227874, "W_D_1KI": 6.673615571257529, "J_D_1KI": 13.536745580644075}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 482, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.540670394897461, "TIME_S_1KI": 23.94329957447606, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 320.7209017944336, "W": 22.04720368625311, "J_1KI": 665.3960618141775, "W_1KI": 45.7410864860023, "W_D": 3.5162036862531103, "J_D": 51.15025166893007, "W_D_1KI": 7.295028394715996, "J_D_1KI": 15.134913681983395}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.output
index 449d7aa..0792bc2 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.01.output
@@ -1,34 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.01 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.1293396949768066}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.1746718883514404}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      94,     210,  ...,  999806,
-                             999898, 1000000]),
-       col_indices=tensor([ 197,  225,  349,  ..., 9664, 9718, 9909]),
-       values=tensor([0.2825, 0.4405, 0.0615,  ..., 0.4764, 0.3721, 0.7741]),
-       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2003, 0.0291, 0.9415,  ..., 0.2751, 0.8368, 0.8186])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000000
-Density: 0.01
-Time: 2.1293396949768066 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 493 -ss 10000 -sd 0.01 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.442772150039673}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     103,     211,  ...,  999799,
+tensor(crow_indices=tensor([      0,     117,     206,  ...,  999808,
                              999895, 1000000]),
-       col_indices=tensor([  29,  259,  296,  ..., 9649, 9833, 9895]),
-       values=tensor([0.6562, 0.6337, 0.8410,  ..., 0.1779, 0.9179, 0.3279]),
+       col_indices=tensor([  38,   49,  125,  ..., 9767, 9798, 9825]),
+       values=tensor([0.7626, 0.4173, 0.7077,  ..., 0.4492, 0.4327, 0.3876]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2837, 0.1453, 0.4499,  ..., 0.4322, 0.7993, 0.4344])
+tensor([0.5141, 0.8808, 0.9438,  ..., 0.4524, 0.8668, 0.5263])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.442772150039673 seconds
+Time: 2.1746718883514404 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 482 -ss 10000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 11.540670394897461}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     103,     211,  ...,  999799,
-                             999895, 1000000]),
-       col_indices=tensor([  29,  259,  296,  ..., 9649, 9833, 9895]),
-       values=tensor([0.6562, 0.6337, 0.8410,  ..., 0.1779, 0.9179, 0.3279]),
+tensor(crow_indices=tensor([      0,      95,     189,  ...,  999807,
+                             999906, 1000000]),
+       col_indices=tensor([ 184,  234,  387,  ..., 9882, 9937, 9961]),
+       values=tensor([0.1456, 0.1844, 0.1237,  ..., 0.5239, 0.9721, 0.9496]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2837, 0.1453, 0.4499,  ..., 0.4322, 0.7993, 0.4344])
+tensor([0.3253, 0.6092, 0.1701,  ..., 0.4726, 0.0642, 0.0427])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +36,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.442772150039673 seconds
+Time: 11.540670394897461 seconds
 
-[20.48, 20.72, 20.88, 20.72, 20.92, 20.68, 20.44, 20.48, 20.4, 20.4]
-[20.48, 20.48, 20.76, 21.64, 23.08, 24.24, 25.0, 25.48, 24.92, 24.24, 24.2, 24.12, 24.0, 23.96]
-14.54863953590393
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 493, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.442772150039673, 'TIME_S_1KI': 21.18209361062814, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 317.52540328025816, 'W': 21.825092476629965}
-[20.48, 20.72, 20.88, 20.72, 20.92, 20.68, 20.44, 20.48, 20.4, 20.4, 20.6, 20.6, 20.36, 20.32, 20.48, 20.48, 20.64, 20.64, 20.8, 20.8]
-370.70000000000005
-18.535000000000004
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 493, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.442772150039673, 'TIME_S_1KI': 21.18209361062814, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 317.52540328025816, 'W': 21.825092476629965, 'J_1KI': 644.0677551323695, 'W_1KI': 44.26996445563888, 'W_D': 3.2900924766299617, 'J_D': 47.86636948227874, 'W_D_1KI': 6.673615571257529, 'J_D_1KI': 13.536745580644075}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      95,     189,  ...,  999807,
+                             999906, 1000000]),
+       col_indices=tensor([ 184,  234,  387,  ..., 9882, 9937, 9961]),
+       values=tensor([0.1456, 0.1844, 0.1237,  ..., 0.5239, 0.9721, 0.9496]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.3253, 0.6092, 0.1701,  ..., 0.4726, 0.0642, 0.0427])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 11.540670394897461 seconds
+
+[20.32, 20.24, 20.16, 20.2, 20.52, 20.4, 20.48, 20.56, 20.56, 20.56]
+[20.52, 20.48, 20.48, 21.8, 22.52, 24.2, 24.92, 25.2, 25.04, 24.52, 24.84, 25.08, 25.08, 25.12]
+14.54701042175293
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 482, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.540670394897461, 'TIME_S_1KI': 23.94329957447606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 320.7209017944336, 'W': 22.04720368625311}
+[20.32, 20.24, 20.16, 20.2, 20.52, 20.4, 20.48, 20.56, 20.56, 20.56, 20.76, 20.88, 20.84, 20.84, 20.56, 20.6, 20.68, 20.92, 20.96, 20.8]
+370.62
+18.531
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 482, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 11.540670394897461, 'TIME_S_1KI': 23.94329957447606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 320.7209017944336, 'W': 22.04720368625311, 'J_1KI': 665.3960618141775, 'W_1KI': 45.7410864860023, 'W_D': 3.5162036862531103, 'J_D': 51.15025166893007, 'W_D_1KI': 7.295028394715996, 'J_D_1KI': 15.134913681983395}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.json
index ad31b62..4a87c51 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.699479103088379, "TIME_S_1KI": 106.99479103088379, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 372.40043621063234, "W": 23.7347384855202, "J_1KI": 3724.0043621063232, "W_1KI": 237.34738485520202, "W_D": 5.215738485520202, "J_D": 81.83546190547948, "W_D_1KI": 52.15738485520202, "J_D_1KI": 521.5738485520202}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.83819031715393, "TIME_S_1KI": 108.3819031715393, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 370.6096257972718, "W": 23.619261647740284, "J_1KI": 3706.096257972718, "W_1KI": 236.19261647740282, "W_D": 5.2132616477402856, "J_D": 81.80124244642268, "W_D_1KI": 52.132616477402856, "J_D_1KI": 521.3261647740286}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.output
index f0aea1a..ad8d7d2 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.699479103088379}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.83819031715393}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     534,    1091,  ..., 4998975,
-                            4999490, 5000000]),
-       col_indices=tensor([   4,   42,   44,  ..., 9941, 9942, 9945]),
-       values=tensor([0.3387, 0.3479, 0.7697,  ..., 0.0992, 0.1573, 0.7910]),
+tensor(crow_indices=tensor([      0,     517,    1012,  ..., 4998964,
+                            4999467, 5000000]),
+       col_indices=tensor([   4,   10,   19,  ..., 9973, 9989, 9990]),
+       values=tensor([0.2609, 0.1530, 0.6472,  ..., 0.9186, 0.8556, 0.5075]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.0536, 0.4974, 0.9494,  ..., 0.5617, 0.8582, 0.7161])
+tensor([0.4885, 0.4408, 0.6533,  ..., 0.1062, 0.1430, 0.1529])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.699479103088379 seconds
+Time: 10.83819031715393 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     534,    1091,  ..., 4998975,
-                            4999490, 5000000]),
-       col_indices=tensor([   4,   42,   44,  ..., 9941, 9942, 9945]),
-       values=tensor([0.3387, 0.3479, 0.7697,  ..., 0.0992, 0.1573, 0.7910]),
+tensor(crow_indices=tensor([      0,     517,    1012,  ..., 4998964,
+                            4999467, 5000000]),
+       col_indices=tensor([   4,   10,   19,  ..., 9973, 9989, 9990]),
+       values=tensor([0.2609, 0.1530, 0.6472,  ..., 0.9186, 0.8556, 0.5075]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.0536, 0.4974, 0.9494,  ..., 0.5617, 0.8582, 0.7161])
+tensor([0.4885, 0.4408, 0.6533,  ..., 0.1062, 0.1430, 0.1529])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.699479103088379 seconds
+Time: 10.83819031715393 seconds
 
-[20.52, 20.4, 20.48, 20.48, 20.48, 20.64, 20.76, 20.8, 20.8, 20.56]
-[20.56, 20.2, 20.28, 23.96, 25.84, 29.28, 30.68, 31.08, 27.28, 26.24, 25.12, 24.52, 24.52, 24.32, 24.2]
-15.69010066986084
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.699479103088379, 'TIME_S_1KI': 106.99479103088379, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 372.40043621063234, 'W': 23.7347384855202}
-[20.52, 20.4, 20.48, 20.48, 20.48, 20.64, 20.76, 20.8, 20.8, 20.56, 20.6, 20.6, 20.6, 20.68, 20.68, 20.68, 20.56, 20.4, 20.28, 20.44]
-370.38
-18.519
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.699479103088379, 'TIME_S_1KI': 106.99479103088379, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 372.40043621063234, 'W': 23.7347384855202, 'J_1KI': 3724.0043621063232, 'W_1KI': 237.34738485520202, 'W_D': 5.215738485520202, 'J_D': 81.83546190547948, 'W_D_1KI': 52.15738485520202, 'J_D_1KI': 521.5738485520202}
+[20.12, 20.2, 20.32, 20.32, 20.36, 20.4, 20.32, 20.32, 20.28, 20.52]
+[20.68, 20.72, 20.76, 25.48, 27.28, 29.12, 30.52, 28.36, 26.92, 25.16, 24.48, 24.28, 24.2, 24.12, 24.24]
+15.690991163253784
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.83819031715393, 'TIME_S_1KI': 108.3819031715393, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 370.6096257972718, 'W': 23.619261647740284}
+[20.12, 20.2, 20.32, 20.32, 20.36, 20.4, 20.32, 20.32, 20.28, 20.52, 20.48, 20.48, 20.6, 20.6, 20.68, 20.76, 20.6, 20.48, 20.52, 20.64]
+368.12
+18.406
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.83819031715393, 'TIME_S_1KI': 108.3819031715393, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 370.6096257972718, 'W': 23.619261647740284, 'J_1KI': 3706.096257972718, 'W_1KI': 236.19261647740282, 'W_D': 5.2132616477402856, 'J_D': 81.80124244642268, 'W_D_1KI': 52.132616477402856, 'J_D_1KI': 521.3261647740286}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.json
index f0b391a..cf15ded 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.31538224220276, "TIME_S_1KI": 213.1538224220276, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 656.8145585250853, "W": 23.224732449579832, "J_1KI": 6568.145585250853, "W_1KI": 232.24732449579832, "W_D": 4.7157324495798285, "J_D": 133.36479693436596, "W_D_1KI": 47.157324495798285, "J_D_1KI": 471.57324495798287}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.307238340377808, "TIME_S_1KI": 213.07238340377808, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 644.1193591403961, "W": 22.767072029791674, "J_1KI": 6441.193591403961, "W_1KI": 227.67072029791674, "W_D": 4.383072029791673, "J_D": 124.00459502220151, "W_D_1KI": 43.830720297916734, "J_D_1KI": 438.30720297916736}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.output
index f5c642e..aedd9eb 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.1 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.31538224220276}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.307238340377808}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      941,     1920,  ...,  9998069,
-                             9999051, 10000000]),
-       col_indices=tensor([   4,   12,   19,  ..., 9982, 9986, 9989]),
-       values=tensor([0.3288, 0.1903, 0.7853,  ..., 0.1848, 0.4723, 0.3439]),
+tensor(crow_indices=tensor([       0,      978,     2000,  ...,  9997908,
+                             9998960, 10000000]),
+       col_indices=tensor([  10,   23,   33,  ..., 9971, 9974, 9991]),
+       values=tensor([0.8946, 0.8479, 0.7256,  ..., 0.4012, 0.5254, 0.1171]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8250, 0.2999, 0.1337,  ..., 0.5908, 0.0422, 0.7676])
+tensor([0.4414, 0.3269, 0.8562,  ..., 0.9318, 0.1636, 0.1553])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 21.31538224220276 seconds
+Time: 21.307238340377808 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      941,     1920,  ...,  9998069,
-                             9999051, 10000000]),
-       col_indices=tensor([   4,   12,   19,  ..., 9982, 9986, 9989]),
-       values=tensor([0.3288, 0.1903, 0.7853,  ..., 0.1848, 0.4723, 0.3439]),
+tensor(crow_indices=tensor([       0,      978,     2000,  ...,  9997908,
+                             9998960, 10000000]),
+       col_indices=tensor([  10,   23,   33,  ..., 9971, 9974, 9991]),
+       values=tensor([0.8946, 0.8479, 0.7256,  ..., 0.4012, 0.5254, 0.1171]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8250, 0.2999, 0.1337,  ..., 0.5908, 0.0422, 0.7676])
+tensor([0.4414, 0.3269, 0.8562,  ..., 0.9318, 0.1636, 0.1553])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 21.31538224220276 seconds
+Time: 21.307238340377808 seconds
 
-[20.48, 20.28, 20.4, 20.48, 20.4, 20.6, 20.96, 20.96, 21.04, 20.88]
-[20.92, 20.84, 20.88, 24.28, 26.48, 27.96, 29.4, 28.08, 28.12, 26.52, 25.88, 24.92, 24.44, 24.32, 24.56, 24.84, 24.84, 24.96, 24.88, 24.72, 24.72, 24.48, 24.56, 24.28, 24.16, 24.12, 24.48]
-28.280823469161987
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.31538224220276, 'TIME_S_1KI': 213.1538224220276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 656.8145585250853, 'W': 23.224732449579832}
-[20.48, 20.28, 20.4, 20.48, 20.4, 20.6, 20.96, 20.96, 21.04, 20.88, 20.44, 20.52, 20.68, 20.36, 20.52, 20.4, 20.4, 20.4, 20.52, 20.72]
-370.18000000000006
-18.509000000000004
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.31538224220276, 'TIME_S_1KI': 213.1538224220276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 656.8145585250853, 'W': 23.224732449579832, 'J_1KI': 6568.145585250853, 'W_1KI': 232.24732449579832, 'W_D': 4.7157324495798285, 'J_D': 133.36479693436596, 'W_D_1KI': 47.157324495798285, 'J_D_1KI': 471.57324495798287}
+[20.48, 20.56, 20.6, 20.4, 20.4, 20.44, 20.52, 20.44, 20.48, 20.64]
+[20.6, 20.52, 21.4, 22.48, 24.2, 25.6, 26.84, 26.96, 26.44, 25.72, 25.28, 25.28, 24.6, 24.52, 24.52, 24.52, 24.6, 24.68, 24.64, 24.68, 24.6, 24.48, 24.4, 24.44, 24.4, 24.48, 24.68]
+28.29170823097229
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.307238340377808, 'TIME_S_1KI': 213.07238340377808, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 644.1193591403961, 'W': 22.767072029791674}
+[20.48, 20.56, 20.6, 20.4, 20.4, 20.44, 20.52, 20.44, 20.48, 20.64, 19.76, 20.0, 20.16, 20.4, 20.64, 20.64, 20.6, 20.52, 20.36, 20.16]
+367.68
+18.384
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.307238340377808, 'TIME_S_1KI': 213.07238340377808, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 644.1193591403961, 'W': 22.767072029791674, 'J_1KI': 6441.193591403961, 'W_1KI': 227.67072029791674, 'W_D': 4.383072029791673, 'J_D': 124.00459502220151, 'W_D_1KI': 43.830720297916734, 'J_D_1KI': 438.30720297916736}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.2.json
index 6cc4360..d5be847 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.2.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.2.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.396695137023926, "TIME_S_1KI": 423.96695137023926, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1244.9685798645019, "W": 23.740989470007165, "J_1KI": 12449.68579864502, "W_1KI": 237.40989470007165, "W_D": 5.275989470007168, "J_D": 276.6709081840517, "W_D_1KI": 52.75989470007168, "J_D_1KI": 527.5989470007169}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.98237729072571, "TIME_S_1KI": 429.8237729072571, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1240.7171248722075, "W": 23.6549559152447, "J_1KI": 12407.171248722074, "W_1KI": 236.549559152447, "W_D": 5.164955915244697, "J_D": 270.90514462232557, "W_D_1KI": 51.64955915244697, "J_D_1KI": 516.4955915244697}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.2.output
index 8c655a7..734d8cb 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.2.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.2 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.396695137023926}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.98237729072571}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1981,     3970,  ..., 19995969,
-                            19997989, 20000000]),
-       col_indices=tensor([   3,    4,    9,  ..., 9978, 9982, 9987]),
-       values=tensor([0.8747, 0.4611, 0.0013,  ..., 0.7048, 0.8145, 0.2728]),
+tensor(crow_indices=tensor([       0,     1987,     4017,  ..., 19995959,
+                            19997966, 20000000]),
+       col_indices=tensor([  11,   19,   20,  ..., 9988, 9989, 9996]),
+       values=tensor([0.2351, 0.1753, 0.4676,  ..., 0.9953, 0.9645, 0.6300]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.1891, 0.5511, 0.0831,  ..., 0.7428, 0.4718, 0.5050])
+tensor([0.3446, 0.4327, 0.7046,  ..., 0.4760, 0.7086, 0.8763])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 42.396695137023926 seconds
+Time: 42.98237729072571 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1981,     3970,  ..., 19995969,
-                            19997989, 20000000]),
-       col_indices=tensor([   3,    4,    9,  ..., 9978, 9982, 9987]),
-       values=tensor([0.8747, 0.4611, 0.0013,  ..., 0.7048, 0.8145, 0.2728]),
+tensor(crow_indices=tensor([       0,     1987,     4017,  ..., 19995959,
+                            19997966, 20000000]),
+       col_indices=tensor([  11,   19,   20,  ..., 9988, 9989, 9996]),
+       values=tensor([0.2351, 0.1753, 0.4676,  ..., 0.9953, 0.9645, 0.6300]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.1891, 0.5511, 0.0831,  ..., 0.7428, 0.4718, 0.5050])
+tensor([0.3446, 0.4327, 0.7046,  ..., 0.4760, 0.7086, 0.8763])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 42.396695137023926 seconds
+Time: 42.98237729072571 seconds
 
-[20.28, 20.48, 20.28, 20.48, 20.68, 20.72, 20.92, 20.84, 20.6, 20.6]
-[20.68, 20.48, 20.48, 24.96, 25.96, 28.64, 31.0, 30.08, 29.88, 29.2, 29.04, 27.32, 26.6, 25.64, 24.48, 24.28, 24.48, 24.6, 24.64, 24.64, 24.8, 24.8, 24.96, 24.76, 25.0, 24.92, 24.88, 24.72, 24.76, 24.72, 24.68, 24.72, 24.56, 24.6, 24.6, 24.56, 24.72, 24.8, 24.76, 24.64, 24.64, 24.64, 24.68, 24.56, 24.52, 24.68, 24.44, 24.28, 24.24, 24.04]
-52.43962478637695
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.396695137023926, 'TIME_S_1KI': 423.96695137023926, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1244.9685798645019, 'W': 23.740989470007165}
-[20.28, 20.48, 20.28, 20.48, 20.68, 20.72, 20.92, 20.84, 20.6, 20.6, 20.92, 20.6, 20.68, 20.68, 20.32, 20.36, 20.28, 20.24, 20.16, 20.16]
-369.29999999999995
-18.464999999999996
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.396695137023926, 'TIME_S_1KI': 423.96695137023926, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1244.9685798645019, 'W': 23.740989470007165, 'J_1KI': 12449.68579864502, 'W_1KI': 237.40989470007165, 'W_D': 5.275989470007168, 'J_D': 276.6709081840517, 'W_D_1KI': 52.75989470007168, 'J_D_1KI': 527.5989470007169}
+[20.6, 20.88, 20.8, 20.76, 20.76, 20.36, 20.44, 20.68, 20.8, 21.08]
+[21.12, 21.04, 21.04, 24.2, 26.04, 27.64, 29.88, 31.6, 28.48, 28.48, 28.68, 26.56, 26.44, 25.72, 24.92, 24.84, 24.72, 24.36, 24.24, 24.24, 24.32, 24.56, 24.52, 24.76, 24.76, 24.84, 24.96, 24.96, 24.8, 24.96, 24.6, 24.48, 24.48, 24.2, 24.16, 24.36, 24.56, 24.68, 24.76, 24.64, 24.52, 24.32, 24.36, 24.52, 24.64, 24.8, 24.8, 24.84, 24.72, 24.36]
+52.450620889663696
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.98237729072571, 'TIME_S_1KI': 429.8237729072571, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1240.7171248722075, 'W': 23.6549559152447}
+[20.6, 20.88, 20.8, 20.76, 20.76, 20.36, 20.44, 20.68, 20.8, 21.08, 20.56, 20.44, 20.04, 20.2, 20.28, 20.32, 20.48, 20.64, 20.52, 20.56]
+369.8
+18.490000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.98237729072571, 'TIME_S_1KI': 429.8237729072571, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1240.7171248722075, 'W': 23.6549559152447, 'J_1KI': 12407.171248722074, 'W_1KI': 236.549559152447, 'W_D': 5.164955915244697, 'J_D': 270.90514462232557, 'W_D_1KI': 51.64955915244697, 'J_D_1KI': 516.4955915244697}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.3.json
index 33ea6fd..6ccdb07 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.3.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.3.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 62.99070644378662, "TIME_S_1KI": 629.9070644378662, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1817.8575790786747, "W": 23.739082320260188, "J_1KI": 18178.57579078675, "W_1KI": 237.3908232026019, "W_D": 5.089082320260189, "J_D": 389.7044856929784, "W_D_1KI": 50.890823202601894, "J_D_1KI": 508.908232026019}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 62.992671489715576, "TIME_S_1KI": 629.9267148971558, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1924.1088186645509, "W": 23.521015273480383, "J_1KI": 19241.08818664551, "W_1KI": 235.21015273480384, "W_D": 5.0750152734803855, "J_D": 415.15561845541015, "W_D_1KI": 50.750152734803855, "J_D_1KI": 507.50152734803856}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.3.output
index 58e2c22..45c0b9c 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.3.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.3 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 62.99070644378662}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 62.992671489715576}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2976,     6021,  ..., 29993904,
-                            29996986, 30000000]),
-       col_indices=tensor([   0,    1,    2,  ..., 9993, 9995, 9997]),
-       values=tensor([0.2230, 0.6279, 0.9702,  ..., 0.2815, 0.5420, 0.7025]),
+tensor(crow_indices=tensor([       0,     2964,     5936,  ..., 29994031,
+                            29997007, 30000000]),
+       col_indices=tensor([   3,   13,   15,  ..., 9991, 9994, 9999]),
+       values=tensor([0.4668, 0.1670, 0.4964,  ..., 0.2068, 0.5203, 0.8363]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.3830, 0.2972, 0.7622,  ..., 0.1887, 0.7379, 0.3841])
+tensor([0.2452, 0.0965, 0.0920,  ..., 0.6406, 0.2865, 0.2449])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 62.99070644378662 seconds
+Time: 62.992671489715576 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2976,     6021,  ..., 29993904,
-                            29996986, 30000000]),
-       col_indices=tensor([   0,    1,    2,  ..., 9993, 9995, 9997]),
-       values=tensor([0.2230, 0.6279, 0.9702,  ..., 0.2815, 0.5420, 0.7025]),
+tensor(crow_indices=tensor([       0,     2964,     5936,  ..., 29994031,
+                            29997007, 30000000]),
+       col_indices=tensor([   3,   13,   15,  ..., 9991, 9994, 9999]),
+       values=tensor([0.4668, 0.1670, 0.4964,  ..., 0.2068, 0.5203, 0.8363]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.3830, 0.2972, 0.7622,  ..., 0.1887, 0.7379, 0.3841])
+tensor([0.2452, 0.0965, 0.0920,  ..., 0.6406, 0.2865, 0.2449])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 62.99070644378662 seconds
+Time: 62.992671489715576 seconds
 
-[20.4, 20.36, 20.44, 20.44, 20.72, 20.96, 20.88, 21.12, 20.72, 20.36]
-[20.44, 20.2, 23.44, 25.08, 27.68, 28.88, 31.04, 29.08, 29.08, 29.48, 28.4, 29.16, 28.52, 28.08, 27.8, 26.96, 25.8, 24.96, 24.8, 24.68, 24.76, 24.68, 24.72, 24.68, 24.52, 24.44, 24.4, 24.76, 24.6, 24.88, 24.88, 24.72, 24.44, 24.48, 24.24, 24.32, 24.24, 24.16, 24.36, 24.56, 24.28, 24.24, 24.28, 24.16, 23.76, 23.92, 24.36, 24.4, 24.52, 24.84, 24.76, 24.44, 24.44, 24.24, 24.32, 24.04, 24.08, 24.28, 24.08, 24.08, 24.0, 23.88, 24.04, 24.28, 24.16, 24.4, 24.48, 24.4, 24.48, 24.6, 24.52, 24.52, 24.52]
-76.5765733718872
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 62.99070644378662, 'TIME_S_1KI': 629.9070644378662, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1817.8575790786747, 'W': 23.739082320260188}
-[20.4, 20.36, 20.44, 20.44, 20.72, 20.96, 20.88, 21.12, 20.72, 20.36, 20.28, 20.84, 20.84, 20.8, 21.0, 21.12, 21.0, 20.52, 20.56, 20.32]
-373.0
-18.65
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 62.99070644378662, 'TIME_S_1KI': 629.9070644378662, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1817.8575790786747, 'W': 23.739082320260188, 'J_1KI': 18178.57579078675, 'W_1KI': 237.3908232026019, 'W_D': 5.089082320260189, 'J_D': 389.7044856929784, 'W_D_1KI': 50.890823202601894, 'J_D_1KI': 508.908232026019}
+[20.2, 20.2, 20.16, 20.32, 20.48, 20.52, 20.72, 20.6, 20.6, 20.44]
+[20.4, 20.36, 21.32, 22.36, 24.48, 25.44, 25.44, 27.88, 28.32, 28.08, 28.28, 28.84, 28.12, 28.28, 27.48, 26.56, 25.64, 24.44, 24.32, 24.36, 24.4, 24.24, 24.12, 24.04, 24.2, 24.32, 24.44, 24.6, 24.6, 24.68, 24.48, 24.2, 24.32, 24.16, 24.12, 24.28, 24.4, 24.56, 24.64, 24.32, 24.24, 24.16, 24.48, 24.64, 24.8, 24.72, 24.68, 24.56, 24.6, 24.6, 24.48, 24.72, 24.64, 24.64, 24.68, 24.92, 24.84, 24.8, 24.6, 24.56, 24.64, 24.56, 24.4, 24.4, 24.36, 24.24, 24.16, 24.16, 24.16, 23.96, 24.08, 24.08, 24.24, 24.24, 24.28, 24.44, 24.32, 24.32]
+81.80381655693054
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 62.992671489715576, 'TIME_S_1KI': 629.9267148971558, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1924.1088186645509, 'W': 23.521015273480383}
+[20.2, 20.2, 20.16, 20.32, 20.48, 20.52, 20.72, 20.6, 20.6, 20.44, 20.8, 20.8, 20.56, 20.52, 20.32, 20.44, 20.6, 20.6, 20.44, 20.64]
+368.91999999999996
+18.445999999999998
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 62.992671489715576, 'TIME_S_1KI': 629.9267148971558, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1924.1088186645509, 'W': 23.521015273480383, 'J_1KI': 19241.08818664551, 'W_1KI': 235.21015273480384, 'W_D': 5.0750152734803855, 'J_D': 415.15561845541015, 'W_D_1KI': 50.750152734803855, 'J_D_1KI': 507.50152734803856}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..56a0b93
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 85.97017788887024, "TIME_S_1KI": 859.7017788887024, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2387.2728435325635, "W": 23.694035817285897, "J_1KI": 23872.728435325633, "W_1KI": 236.94035817285896, "W_D": 5.440035817285899, "J_D": 548.1062776556028, "W_D_1KI": 54.400358172858994, "J_D_1KI": 544.00358172859}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..ecd3fea
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.4.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.4 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 85.97017788887024}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4089,     8015,  ..., 39991984,
+                            39995972, 40000000]),
+       col_indices=tensor([   3,    7,    8,  ..., 9991, 9993, 9999]),
+       values=tensor([0.1769, 0.1553, 0.1261,  ..., 0.3324, 0.8180, 0.7589]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.3780, 0.5744, 0.3320,  ..., 0.1044, 0.3638, 0.0832])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 85.97017788887024 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4089,     8015,  ..., 39991984,
+                            39995972, 40000000]),
+       col_indices=tensor([   3,    7,    8,  ..., 9991, 9993, 9999]),
+       values=tensor([0.1769, 0.1553, 0.1261,  ..., 0.3324, 0.8180, 0.7589]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.3780, 0.5744, 0.3320,  ..., 0.1044, 0.3638, 0.0832])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 85.97017788887024 seconds
+
+[20.48, 20.4, 20.28, 20.52, 20.64, 20.48, 20.52, 20.44, 20.16, 20.08]
+[20.08, 20.24, 20.32, 21.52, 23.0, 25.08, 25.88, 28.48, 30.52, 29.92, 30.08, 30.4, 29.48, 28.32, 29.08, 28.76, 27.96, 26.72, 25.64, 24.92, 24.48, 24.48, 24.48, 24.32, 24.28, 24.24, 24.28, 24.24, 24.44, 24.48, 24.84, 24.92, 24.8, 24.8, 24.84, 24.48, 24.32, 24.52, 24.36, 24.24, 24.36, 24.4, 24.4, 24.4, 24.48, 24.28, 24.28, 24.48, 24.36, 24.4, 24.52, 24.44, 24.52, 24.64, 24.68, 24.88, 24.92, 24.72, 24.96, 24.72, 24.44, 24.64, 24.64, 24.64, 24.68, 24.68, 24.8, 24.52, 24.28, 24.28, 24.24, 24.16, 24.24, 24.36, 24.48, 24.68, 24.72, 24.6, 24.48, 24.48, 24.24, 24.36, 24.4, 24.44, 24.84, 25.0, 25.04, 25.04, 25.04, 24.68, 24.4, 24.36, 24.48, 24.44, 24.48, 24.68]
+100.75416707992554
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 85.97017788887024, 'TIME_S_1KI': 859.7017788887024, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2387.2728435325635, 'W': 23.694035817285897}
+[20.48, 20.4, 20.28, 20.52, 20.64, 20.48, 20.52, 20.44, 20.16, 20.08, 20.56, 20.4, 20.44, 20.16, 20.08, 19.92, 20.0, 19.84, 20.16, 20.16]
+365.08
+18.253999999999998
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 85.97017788887024, 'TIME_S_1KI': 859.7017788887024, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2387.2728435325635, 'W': 23.694035817285897, 'J_1KI': 23872.728435325633, 'W_1KI': 236.94035817285896, 'W_D': 5.440035817285899, 'J_D': 548.1062776556028, 'W_D_1KI': 54.400358172858994, 'J_D_1KI': 544.00358172859}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..3f6b51b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 104.97069644927979, "TIME_S_1KI": 1049.7069644927979, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3012.450877876282, "W": 23.728634661712928, "J_1KI": 30124.50877876282, "W_1KI": 237.28634661712928, "W_D": 4.652634661712927, "J_D": 590.6717167224888, "W_D_1KI": 46.52634661712927, "J_D_1KI": 465.2634661712927}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..8b19e00
--- /dev/null
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_0.5.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.5 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 104.97069644927979}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4981,     9935,  ..., 49989912,
+                            49994956, 50000000]),
+       col_indices=tensor([   1,    5,    6,  ..., 9994, 9995, 9998]),
+       values=tensor([0.9764, 0.1800, 0.4422,  ..., 0.5448, 0.5139, 0.1546]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.1638, 0.1363, 0.5993,  ..., 0.4801, 0.4016, 0.8447])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 104.97069644927979 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4981,     9935,  ..., 49989912,
+                            49994956, 50000000]),
+       col_indices=tensor([   1,    5,    6,  ..., 9994, 9995, 9998]),
+       values=tensor([0.9764, 0.1800, 0.4422,  ..., 0.5448, 0.5139, 0.1546]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.1638, 0.1363, 0.5993,  ..., 0.4801, 0.4016, 0.8447])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 104.97069644927979 seconds
+
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diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.json
index 256b7e3..39e7e8d 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 147223, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.998100280761719, "TIME_S_1KI": 0.07470368271779354, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.2257844543456, "W": 22.66095026687019, "J_1KI": 2.256616048133414, "W_1KI": 0.15392262259884795, "W_D": 4.3619502668701955, "J_D": 63.94931951642035, "W_D_1KI": 0.029628184909084827, "J_D_1KI": 0.00020124698524744657}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 141479, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.518462419509888, "TIME_S_1KI": 0.07434645720926701, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 335.0979061508179, "W": 22.891804729847298, "J_1KI": 2.368534596306292, "W_1KI": 0.1618035519748323, "W_D": 4.302804729847299, "J_D": 62.98589703011518, "W_D_1KI": 0.030413027586053753, "J_D_1KI": 0.0002149649600721927}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.output
index a769041..063f646 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_1e-05.output
@@ -1,266 +1,373 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.015372514724731445}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.015038013458251953}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -268,650 +375,271 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.015372514724731445 seconds
+Time: 0.015038013458251953 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 68303 -ss 10000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.871366500854492}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 1000, 1000, 1000]),
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-                      3.1108e-01, 6.0390e-01, 1.0443e-01, 2.6637e-01]),
-       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.5445, 0.4387, 0.2146,  ..., 0.9330, 0.4366, 0.2965])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 4.871366500854492 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 147223 -ss 10000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.998100280761719}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 69823 -ss 10000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.181962966918945}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.2906, 0.3588, 0.4315, 0.5729, 0.6483, 0.8535, 0.1307,
+                      0.8408, 0.8607, 0.9545, 0.5493, 0.8516, 0.8836, 0.2334,
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+                      0.8516, 0.3598, 0.9753, 0.0488, 0.2694, 0.1902]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.8452, 0.1047, 0.0563,  ..., 0.6079, 0.4820, 0.4351])
+tensor([0.0156, 0.6147, 0.1096,  ..., 0.9950, 0.7654, 0.9443])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -919,268 +647,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.998100280761719 seconds
+Time: 5.181962966918945 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 141479 -ss 10000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.518462419509888}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
-       col_indices=tensor([ 606, 4248, 4323,  142, 3267, 1209, 7616, 5137, 8211,
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+                      8.5059e-01, 2.1034e-02, 7.3786e-01, 5.2646e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.8452, 0.1047, 0.0563,  ..., 0.6079, 0.4820, 0.4351])
+tensor([0.4567, 0.4180, 0.1623,  ..., 0.7337, 0.1746, 0.4398])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1188,13 +1026,389 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.998100280761719 seconds
+Time: 10.518462419509888 seconds
 
-[20.36, 20.16, 20.2, 20.32, 20.32, 20.36, 20.56, 20.6, 20.52, 20.56]
-[20.68, 20.8, 21.2, 24.88, 26.6, 27.2, 27.72, 25.88, 24.68, 23.88, 23.88, 23.8, 23.88, 23.68]
-14.660717248916626
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 147223, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.998100280761719, 'TIME_S_1KI': 0.07470368271779354, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.2257844543456, 'W': 22.66095026687019}
-[20.36, 20.16, 20.2, 20.32, 20.32, 20.36, 20.56, 20.6, 20.52, 20.56, 20.32, 20.36, 20.24, 20.2, 20.24, 20.36, 20.24, 20.2, 20.36, 20.24]
-365.9799999999999
-18.298999999999996
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 147223, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.998100280761719, 'TIME_S_1KI': 0.07470368271779354, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.2257844543456, 'W': 22.66095026687019, 'J_1KI': 2.256616048133414, 'W_1KI': 0.15392262259884795, 'W_D': 4.3619502668701955, 'J_D': 63.94931951642035, 'W_D_1KI': 0.029628184909084827, 'J_D_1KI': 0.00020124698524744657}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([5164, 5014, 8214, 7408, 7189, 1684, 5605, 5380, 8291,
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+                      4.1656e-01, 1.4362e-01, 9.6354e-01, 5.7426e-01,
+                      7.8689e-01, 2.4730e-01, 7.7422e-01, 3.5805e-02,
+                      1.9351e-01, 5.0990e-01, 6.2640e-01, 3.7221e-01,
+                      3.1572e-01, 8.4142e-01, 5.0295e-01, 4.9000e-01,
+                      9.5176e-01, 9.2623e-02, 3.9572e-01, 3.3737e-01,
+                      4.9911e-01, 7.9045e-01, 5.2579e-01, 1.4566e-01,
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+                      8.2843e-01, 8.2229e-01, 8.6943e-01, 4.9570e-01,
+                      5.8722e-01, 9.6892e-01, 8.6864e-01, 4.1771e-01,
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+                      1.8733e-01, 2.0567e-01, 6.0647e-01, 4.6222e-01,
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+                      1.0113e-01, 6.0608e-01, 2.5950e-01, 7.7470e-01,
+                      4.5427e-01, 7.0661e-01, 4.1646e-01, 9.6120e-01,
+                      2.1486e-01, 7.9020e-01, 6.9684e-01, 8.5318e-01,
+                      2.4751e-02, 1.9432e-01, 3.3890e-03, 3.5931e-01,
+                      6.0078e-01, 3.1420e-01, 7.9915e-01, 7.2995e-01,
+                      4.5837e-01, 4.8434e-01, 2.4191e-01, 8.7264e-01,
+                      4.4628e-01, 1.7433e-01, 3.9048e-01, 5.8578e-01,
+                      5.1387e-01, 2.3966e-01, 5.9851e-01, 6.1038e-01,
+                      9.8604e-01, 8.5463e-01, 7.8940e-01, 2.9878e-01,
+                      8.8823e-01, 2.9276e-01, 8.4526e-01, 7.0736e-01,
+                      1.9470e-01, 6.4877e-01, 9.0222e-01, 1.7804e-01,
+                      5.1116e-01, 5.3144e-01, 1.6398e-02, 1.8603e-01,
+                      8.4622e-01, 9.7673e-01, 8.3551e-01, 7.9152e-01,
+                      2.9879e-02, 3.2414e-02, 3.3248e-01, 9.0278e-01,
+                      7.2618e-02, 3.8623e-02, 4.5740e-01, 1.3855e-01,
+                      4.6681e-01, 5.3146e-01, 3.4373e-01, 3.8471e-01,
+                      7.6964e-01, 3.2053e-01, 4.4641e-01, 9.3560e-01,
+                      3.6526e-01, 1.7340e-01, 4.1921e-01, 3.2031e-01,
+                      1.1241e-01, 7.3094e-01, 6.8199e-01, 4.7013e-01,
+                      5.7060e-01, 8.6818e-01, 9.5280e-01, 2.7502e-01,
+                      2.5660e-01, 2.5142e-01, 8.7490e-01, 9.3681e-01,
+                      7.6161e-01, 3.5498e-01, 3.9613e-02, 7.6181e-01,
+                      9.7582e-01, 1.9366e-01, 9.0553e-02, 1.5015e-01,
+                      3.4037e-01, 2.7234e-01, 2.6012e-01, 5.4730e-01,
+                      7.5914e-01, 7.2804e-01, 8.8377e-01, 2.9417e-01,
+                      8.2797e-01, 7.8180e-01, 6.9616e-01, 5.9021e-01,
+                      3.7858e-01, 4.2900e-01, 2.6402e-01, 7.0528e-01,
+                      1.6843e-01, 1.3766e-01, 2.6640e-01, 1.8234e-01,
+                      8.9174e-01, 2.9224e-01, 9.0712e-01, 6.5247e-01,
+                      5.3578e-01, 4.8627e-01, 5.4907e-01, 8.4350e-01,
+                      6.6691e-01, 8.1816e-01, 1.7307e-01, 7.7056e-01,
+                      8.6383e-02, 4.5982e-01, 4.4756e-01, 4.6444e-01,
+                      4.6836e-01, 9.6666e-01, 8.8660e-01, 8.5342e-01,
+                      5.4247e-01, 8.6053e-01, 8.6540e-01, 9.2791e-01,
+                      2.1159e-01, 2.4351e-01, 4.9738e-01, 4.1807e-01,
+                      2.7549e-01, 8.2197e-01, 7.6196e-01, 8.8791e-01,
+                      9.1088e-01, 8.8134e-01, 6.6822e-01, 5.0556e-01,
+                      5.4217e-01, 3.6135e-01, 5.4197e-01, 8.7029e-01,
+                      1.5876e-01, 2.6070e-01, 1.4902e-01, 4.9540e-01,
+                      3.2658e-01, 9.2315e-01, 9.8529e-02, 8.3386e-01,
+                      6.6861e-01, 6.8086e-01, 4.1234e-01, 9.1040e-01,
+                      8.0733e-02, 8.3027e-01, 2.7401e-02, 7.0357e-01,
+                      7.4001e-01, 1.0754e-01, 4.0982e-01, 7.3224e-01,
+                      3.0803e-01, 2.1047e-01, 8.9684e-01, 2.7451e-01,
+                      1.7786e-01, 8.7756e-01, 9.1917e-01, 5.9533e-01,
+                      2.2290e-01, 8.5840e-01, 8.1366e-01, 4.6831e-01,
+                      3.2199e-01, 1.0599e-01, 9.0382e-01, 5.5555e-01,
+                      1.1648e-01, 2.6181e-01, 9.0756e-02, 7.6516e-01,
+                      1.3491e-01, 1.5806e-01, 6.1588e-01, 3.5698e-01,
+                      9.6605e-01, 7.0001e-01, 3.4645e-01, 3.6756e-01,
+                      8.9547e-01, 5.0293e-01, 7.8512e-02, 9.5652e-01,
+                      4.6658e-01, 5.7874e-01, 8.5198e-01, 1.4339e-01,
+                      3.6238e-01, 4.9661e-01, 7.7543e-01, 1.9030e-01,
+                      1.1043e-01, 5.6247e-01, 8.0300e-01, 2.2866e-01,
+                      1.3044e-01, 4.3849e-01, 6.0789e-02, 6.0168e-01,
+                      8.5059e-01, 2.1034e-02, 7.3786e-01, 5.2646e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4567, 0.4180, 0.1623,  ..., 0.7337, 0.1746, 0.4398])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.518462419509888 seconds
+
+[20.44, 20.6, 20.6, 20.72, 20.68, 20.8, 20.68, 20.68, 20.6, 20.6]
+[21.04, 21.0, 21.12, 25.8, 27.6, 28.0, 28.72, 26.32, 24.76, 23.36, 23.68, 23.56, 23.52, 23.84]
+14.63833498954773
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 141479, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.518462419509888, 'TIME_S_1KI': 0.07434645720926701, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.0979061508179, 'W': 22.891804729847298}
+[20.44, 20.6, 20.6, 20.72, 20.68, 20.8, 20.68, 20.68, 20.6, 20.6, 20.68, 20.56, 20.56, 20.56, 20.6, 20.64, 20.56, 20.76, 20.92, 20.8]
+371.78
+18.589
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 141479, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.518462419509888, 'TIME_S_1KI': 0.07434645720926701, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.0979061508179, 'W': 22.891804729847298, 'J_1KI': 2.368534596306292, 'W_1KI': 0.1618035519748323, 'W_D': 4.302804729847299, 'J_D': 62.98589703011518, 'W_D_1KI': 0.030413027586053753, 'J_D_1KI': 0.0002149649600721927}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_5e-05.json
index f94bbe2..7c93d40 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_5e-05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 52408, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.675631761550903, "TIME_S_1KI": 0.2037023309714338, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 322.76721565246584, "W": 22.07477701860741, "J_1KI": 6.1587394224634755, "W_1KI": 0.42121006370415603, "W_D": 3.4607770186074056, "J_D": 50.6018865489959, "W_D_1KI": 0.06603528122819809, "J_D_1KI": 0.0012600229207029095}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 50379, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.08161449432373, "TIME_S_1KI": 0.20011541504046787, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 293.8021245384217, "W": 21.614421497236524, "J_1KI": 5.8318371650572995, "W_1KI": 0.4290363345290007, "W_D": 3.025421497236522, "J_D": 41.12417552447326, "W_D_1KI": 0.060053226487951764, "J_D_1KI": 0.0011920289503156427}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_5e-05.output
index a9bfe94..c2ed777 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_5e-05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_10000_5e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 5e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.02814483642578125}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.027973651885986328}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    3,  ..., 4997, 4998, 5000]),
-       col_indices=tensor([7223,  597, 5381,  ..., 4437, 2871, 7175]),
-       values=tensor([0.8424, 0.9605, 0.7186,  ..., 0.3316, 0.2968, 0.8125]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 4998, 4998, 5000]),
+       col_indices=tensor([5173, 3476, 8311,  ..., 3507, 1550, 2416]),
+       values=tensor([0.1624, 0.2116, 0.7810,  ..., 0.1936, 0.2168, 0.5075]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.9947, 0.8149, 0.3597,  ..., 0.7445, 0.4060, 0.0098])
+tensor([0.9559, 0.2089, 0.7482,  ..., 0.9674, 0.6990, 0.3683])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 0.02814483642578125 seconds
+Time: 0.027973651885986328 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 37307 -ss 10000 -sd 5e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.474437952041626}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 37535 -ss 10000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.822920083999634}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    2,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([7873, 9438, 5376,  ..., 1254, 8934, 6510]),
-       values=tensor([0.8139, 0.0055, 0.6843,  ..., 0.4362, 0.9226, 0.6386]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([1207, 4588, 6635,  ..., 4462, 7085, 9737]),
+       values=tensor([0.8511, 0.6719, 0.6671,  ..., 0.0958, 0.9883, 0.1849]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.9683, 0.4961, 0.7880,  ..., 0.7466, 0.9086, 0.6990])
+tensor([0.7262, 0.6607, 0.5856,  ..., 0.5982, 0.8892, 0.2173])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 7.474437952041626 seconds
+Time: 7.822920083999634 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 52408 -ss 10000 -sd 5e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.675631761550903}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 50379 -ss 10000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.08161449432373}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([6316, 4387, 8598,  ...,  977, 3012, 3071]),
-       values=tensor([0.0249, 0.1066, 0.4899,  ..., 0.3057, 0.2915, 0.5832]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([5494, 8331, 1267,  ..., 9174,  296, 8633]),
+       values=tensor([0.8003, 0.4485, 0.3154,  ..., 0.8226, 0.3395, 0.4010]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.6250, 0.8754, 0.6636,  ..., 0.3831, 0.1537, 0.5147])
+tensor([0.1323, 0.6232, 0.2469,  ..., 0.6634, 0.4951, 0.0985])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.675631761550903 seconds
+Time: 10.08161449432373 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([6316, 4387, 8598,  ...,  977, 3012, 3071]),
-       values=tensor([0.0249, 0.1066, 0.4899,  ..., 0.3057, 0.2915, 0.5832]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([5494, 8331, 1267,  ..., 9174,  296, 8633]),
+       values=tensor([0.8003, 0.4485, 0.3154,  ..., 0.8226, 0.3395, 0.4010]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.6250, 0.8754, 0.6636,  ..., 0.3831, 0.1537, 0.5147])
+tensor([0.1323, 0.6232, 0.2469,  ..., 0.6634, 0.4951, 0.0985])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.675631761550903 seconds
+Time: 10.08161449432373 seconds
 
-[20.6, 20.52, 20.48, 20.48, 20.44, 20.28, 20.56, 20.6, 20.76, 21.08]
-[21.08, 20.76, 21.6, 22.6, 23.96, 24.64, 25.4, 24.84, 24.84, 24.72, 23.92, 24.04, 24.08, 23.92]
-14.621539115905762
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 52408, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.675631761550903, 'TIME_S_1KI': 0.2037023309714338, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.76721565246584, 'W': 22.07477701860741}
-[20.6, 20.52, 20.48, 20.48, 20.44, 20.28, 20.56, 20.6, 20.76, 21.08, 20.48, 20.64, 20.8, 20.76, 20.92, 21.0, 20.88, 20.76, 20.84, 20.96]
-372.2800000000001
-18.614000000000004
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 52408, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.675631761550903, 'TIME_S_1KI': 0.2037023309714338, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.76721565246584, 'W': 22.07477701860741, 'J_1KI': 6.1587394224634755, 'W_1KI': 0.42121006370415603, 'W_D': 3.4607770186074056, 'J_D': 50.6018865489959, 'W_D_1KI': 0.06603528122819809, 'J_D_1KI': 0.0012600229207029095}
+[20.4, 20.4, 20.52, 20.68, 20.68, 20.64, 20.48, 20.52, 20.4, 20.32]
+[20.32, 20.16, 20.96, 22.16, 24.08, 25.0, 25.56, 25.08, 24.76, 23.56, 23.56, 23.32, 23.28]
+13.592874765396118
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 50379, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.08161449432373, 'TIME_S_1KI': 0.20011541504046787, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 293.8021245384217, 'W': 21.614421497236524}
+[20.4, 20.4, 20.52, 20.68, 20.68, 20.64, 20.48, 20.52, 20.4, 20.32, 20.72, 20.36, 20.24, 20.36, 20.56, 21.12, 21.28, 21.16, 21.2, 20.92]
+371.78000000000003
+18.589000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 50379, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.08161449432373, 'TIME_S_1KI': 0.20011541504046787, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 293.8021245384217, 'W': 21.614421497236524, 'J_1KI': 5.8318371650572995, 'W_1KI': 0.4290363345290007, 'W_D': 3.025421497236522, 'J_D': 41.12417552447326, 'W_D_1KI': 0.060053226487951764, 'J_D_1KI': 0.0011920289503156427}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_0.0001.json
index 118d60f..6638e3c 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_0.0001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 98.35910129547119, "TIME_S_1KI": 983.5910129547119, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2706.022798595428, "W": 24.09983015121324, "J_1KI": 27060.22798595428, "W_1KI": 240.9983015121324, "W_D": 5.635830151213241, "J_D": 632.8129610252375, "W_D_1KI": 56.358301512132414, "J_D_1KI": 563.5830151213241}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 98.34671473503113, "TIME_S_1KI": 983.4671473503113, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2702.3393918704983, "W": 24.052580950114546, "J_1KI": 27023.393918704984, "W_1KI": 240.52580950114546, "W_D": 5.428580950114547, "J_D": 609.9082744541165, "W_D_1KI": 54.28580950114547, "J_D_1KI": 542.8580950114548}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_0.0001.output
index 22bc540..bbf7a9b 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_0.0001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_0.0001.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 98.35910129547119}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 98.34671473503113}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       62,      113,  ..., 24999916,
-                            24999964, 25000000]),
-       col_indices=tensor([ 13628,  17541,  24252,  ..., 467551, 469636,
-                           477818]),
-       values=tensor([0.8374, 0.1433, 0.7046,  ..., 0.7606, 0.4438, 0.1648]),
+tensor(crow_indices=tensor([       0,       60,      122,  ..., 24999905,
+                            24999951, 25000000]),
+       col_indices=tensor([ 13817,  14058,  49011,  ..., 453122, 457251,
+                           499785]),
+       values=tensor([0.7246, 0.5124, 0.1037,  ..., 0.4878, 0.2821, 0.0236]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.5427, 0.9990, 0.7165,  ..., 0.2818, 0.2990, 0.5329])
+tensor([0.3477, 0.6286, 0.4734,  ..., 0.5349, 0.4636, 0.2407])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,17 +17,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 98.35910129547119 seconds
+Time: 98.34671473503113 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       62,      113,  ..., 24999916,
-                            24999964, 25000000]),
-       col_indices=tensor([ 13628,  17541,  24252,  ..., 467551, 469636,
-                           477818]),
-       values=tensor([0.8374, 0.1433, 0.7046,  ..., 0.7606, 0.4438, 0.1648]),
+tensor(crow_indices=tensor([       0,       60,      122,  ..., 24999905,
+                            24999951, 25000000]),
+       col_indices=tensor([ 13817,  14058,  49011,  ..., 453122, 457251,
+                           499785]),
+       values=tensor([0.7246, 0.5124, 0.1037,  ..., 0.4878, 0.2821, 0.0236]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.5427, 0.9990, 0.7165,  ..., 0.2818, 0.2990, 0.5329])
+tensor([0.3477, 0.6286, 0.4734,  ..., 0.5349, 0.4636, 0.2407])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -35,13 +35,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 98.35910129547119 seconds
+Time: 98.34671473503113 seconds
 
-[20.28, 20.32, 20.44, 20.32, 20.6, 20.64, 20.64, 20.52, 20.76, 20.8]
-[20.68, 20.52, 20.6, 22.08, 24.8, 25.76, 28.52, 28.48, 30.56, 30.0, 29.28, 29.8, 28.92, 28.32, 28.32, 27.32, 26.84, 25.96, 25.16, 25.08, 24.72, 24.92, 24.96, 25.12, 25.32, 25.44, 25.32, 25.24, 25.2, 25.08, 25.12, 25.28, 25.16, 25.32, 25.2, 25.04, 25.04, 25.04, 25.28, 25.16, 25.28, 25.36, 25.36, 25.36, 25.44, 25.48, 25.28, 25.24, 25.12, 25.04, 25.04, 25.2, 25.16, 25.36, 25.24, 25.36, 25.12, 25.12, 25.12, 25.2, 25.32, 25.36, 25.16, 25.16, 25.2, 25.28, 25.4, 25.44, 25.48, 25.28, 25.24, 25.36, 25.36, 25.24, 25.36, 25.2, 25.24, 25.52, 25.52, 25.52, 25.52, 25.4, 25.08, 25.2, 25.24, 25.44, 25.12, 25.12, 24.92, 24.8, 24.8, 25.08, 25.12, 25.32, 25.28, 25.2, 25.04, 25.08, 25.16, 25.4, 25.44, 25.36, 25.36, 25.36, 25.28, 25.04, 25.16]
-112.28389501571655
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 98.35910129547119, 'TIME_S_1KI': 983.5910129547119, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2706.022798595428, 'W': 24.09983015121324}
-[20.28, 20.32, 20.44, 20.32, 20.6, 20.64, 20.64, 20.52, 20.76, 20.8, 20.48, 20.64, 20.56, 20.44, 20.36, 20.4, 20.36, 20.52, 20.64, 20.68]
-369.28
-18.464
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 98.35910129547119, 'TIME_S_1KI': 983.5910129547119, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2706.022798595428, 'W': 24.09983015121324, 'J_1KI': 27060.22798595428, 'W_1KI': 240.9983015121324, 'W_D': 5.635830151213241, 'J_D': 632.8129610252375, 'W_D_1KI': 56.358301512132414, 'J_D_1KI': 563.5830151213241}
+[20.48, 20.48, 20.32, 20.24, 20.52, 20.64, 20.92, 21.0, 20.84, 20.56]
+[20.32, 20.16, 22.68, 23.96, 26.8, 28.44, 28.44, 30.0, 29.32, 29.6, 29.0, 28.16, 28.68, 27.4, 27.08, 27.0, 26.2, 25.52, 25.28, 25.24, 25.08, 25.0, 24.92, 25.08, 25.0, 25.04, 25.0, 25.12, 25.08, 25.08, 25.08, 25.0, 25.0, 25.2, 25.0, 25.16, 25.2, 25.08, 24.96, 24.76, 24.84, 24.92, 25.04, 25.2, 25.4, 25.32, 25.32, 25.24, 25.0, 25.16, 25.08, 25.08, 25.04, 25.44, 25.24, 25.48, 25.52, 25.44, 25.36, 25.28, 25.08, 25.12, 25.12, 25.04, 24.96, 24.96, 24.96, 24.96, 25.12, 25.2, 25.2, 25.2, 25.08, 25.08, 24.96, 24.88, 25.04, 25.12, 25.4, 25.32, 25.44, 25.56, 25.36, 25.24, 25.32, 25.36, 25.44, 25.6, 25.48, 25.4, 25.32, 25.08, 25.16, 25.04, 25.0, 25.0, 24.88, 24.96, 25.04, 25.16, 25.16, 24.88, 25.12, 24.84, 25.0, 24.92, 25.16]
+112.35132718086243
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 98.34671473503113, 'TIME_S_1KI': 983.4671473503113, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2702.3393918704983, 'W': 24.052580950114546}
+[20.48, 20.48, 20.32, 20.24, 20.52, 20.64, 20.92, 21.0, 20.84, 20.56, 20.52, 20.48, 20.64, 20.76, 20.76, 20.92, 20.92, 20.92, 20.84, 21.0]
+372.47999999999996
+18.624
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 98.34671473503113, 'TIME_S_1KI': 983.4671473503113, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2702.3393918704983, 'W': 24.052580950114546, 'J_1KI': 27023.393918704984, 'W_1KI': 240.52580950114546, 'W_D': 5.428580950114547, 'J_D': 609.9082744541165, 'W_D_1KI': 54.28580950114547, 'J_D_1KI': 542.8580950114548}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.json
index 36dce3a..838aa74 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.078357696533203, "TIME_S_1KI": 100.78357696533203, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 355.02523105621333, "W": 24.261238712111865, "J_1KI": 3550.2523105621335, "W_1KI": 242.61238712111864, "W_D": 5.657238712111866, "J_D": 82.78482829093929, "W_D_1KI": 56.57238712111866, "J_D_1KI": 565.7238712111866}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.067489624023438, "TIME_S_1KI": 100.67489624023438, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 335.37869496345525, "W": 22.8901921314268, "J_1KI": 3353.7869496345525, "W_1KI": 228.901921314268, "W_D": 4.521192131426801, "J_D": 66.24284794163708, "W_D_1KI": 45.21192131426801, "J_D_1KI": 452.1192131426801}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.output
index 8996689..6328d8b 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.078357696533203}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.067489624023438}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       0,       6,  ..., 2499989,
-                            2499997, 2500000]),
-       col_indices=tensor([ 10944, 177257, 201447,  ..., 125511, 168548,
-                           443200]),
-       values=tensor([0.0549, 0.4670, 0.3111,  ..., 0.0129, 0.0661, 0.9327]),
+tensor(crow_indices=tensor([      0,       4,      11,  ..., 2499996,
+                            2499999, 2500000]),
+       col_indices=tensor([261896, 273598, 341514,  ..., 229093, 382580,
+                           115157]),
+       values=tensor([0.6612, 0.8768, 0.0724,  ..., 0.5774, 0.6445, 0.0934]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3951, 0.3409, 0.2222,  ..., 0.4533, 0.5999, 0.5088])
+tensor([0.8715, 0.7923, 0.2777,  ..., 0.2922, 0.6586, 0.1397])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,17 +17,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.078357696533203 seconds
+Time: 10.067489624023438 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       0,       6,  ..., 2499989,
-                            2499997, 2500000]),
-       col_indices=tensor([ 10944, 177257, 201447,  ..., 125511, 168548,
-                           443200]),
-       values=tensor([0.0549, 0.4670, 0.3111,  ..., 0.0129, 0.0661, 0.9327]),
+tensor(crow_indices=tensor([      0,       4,      11,  ..., 2499996,
+                            2499999, 2500000]),
+       col_indices=tensor([261896, 273598, 341514,  ..., 229093, 382580,
+                           115157]),
+       values=tensor([0.6612, 0.8768, 0.0724,  ..., 0.5774, 0.6445, 0.0934]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3951, 0.3409, 0.2222,  ..., 0.4533, 0.5999, 0.5088])
+tensor([0.8715, 0.7923, 0.2777,  ..., 0.2922, 0.6586, 0.1397])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -35,13 +35,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.078357696533203 seconds
+Time: 10.067489624023438 seconds
 
-[20.6, 20.6, 20.76, 20.88, 20.68, 20.64, 20.36, 20.76, 20.88, 20.96]
-[21.0, 20.96, 24.12, 26.84, 26.84, 28.92, 30.04, 30.92, 26.56, 25.12, 24.8, 25.2, 25.28, 25.52]
-14.633433818817139
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.078357696533203, 'TIME_S_1KI': 100.78357696533203, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 355.02523105621333, 'W': 24.261238712111865}
-[20.6, 20.6, 20.76, 20.88, 20.68, 20.64, 20.36, 20.76, 20.88, 20.96, 20.52, 20.48, 20.56, 20.6, 20.6, 20.64, 20.64, 20.76, 20.8, 20.8]
-372.08
-18.604
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.078357696533203, 'TIME_S_1KI': 100.78357696533203, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 355.02523105621333, 'W': 24.261238712111865, 'J_1KI': 3550.2523105621335, 'W_1KI': 242.61238712111864, 'W_D': 5.657238712111866, 'J_D': 82.78482829093929, 'W_D_1KI': 56.57238712111866, 'J_D_1KI': 565.7238712111866}
+[20.24, 20.0, 20.0, 20.2, 20.28, 20.52, 20.4, 20.2, 20.52, 20.6]
+[20.64, 20.76, 21.52, 22.24, 24.4, 25.84, 26.96, 26.96, 26.68, 26.68, 25.0, 24.64, 24.68, 24.92]
+14.651633024215698
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.067489624023438, 'TIME_S_1KI': 100.67489624023438, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.37869496345525, 'W': 22.8901921314268}
+[20.24, 20.0, 20.0, 20.2, 20.28, 20.52, 20.4, 20.2, 20.52, 20.6, 20.4, 20.32, 20.44, 20.52, 20.32, 20.6, 20.72, 20.68, 20.76, 20.56]
+367.38
+18.369
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.067489624023438, 'TIME_S_1KI': 100.67489624023438, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.37869496345525, 'W': 22.8901921314268, 'J_1KI': 3353.7869496345525, 'W_1KI': 228.901921314268, 'W_D': 4.521192131426801, 'J_D': 66.24284794163708, 'W_D_1KI': 45.21192131426801, 'J_D_1KI': 452.1192131426801}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_5e-05.json
index 741a72d..b28b780 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_5e-05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 50.092703104019165, "TIME_S_1KI": 500.92703104019165, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1421.3682743358613, "W": 24.212233919725165, "J_1KI": 14213.682743358613, "W_1KI": 242.12233919725165, "W_D": 5.561233919725165, "J_D": 326.46972955346126, "W_D_1KI": 55.61233919725165, "J_D_1KI": 556.1233919725165}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 50.31077265739441, "TIME_S_1KI": 503.1077265739441, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1394.836717920303, "W": 24.161271875633943, "J_1KI": 13948.36717920303, "W_1KI": 241.61271875633943, "W_D": 5.625271875633942, "J_D": 324.7484569854733, "W_D_1KI": 56.25271875633942, "J_D_1KI": 562.5271875633941}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_5e-05.output
index 2787c93..a18507d 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_5e-05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_500000_5e-05.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 5e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 50.092703104019165}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 50.31077265739441}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       19,       45,  ..., 12499947,
-                            12499973, 12500000]),
-       col_indices=tensor([ 17397,  55872, 132943,  ..., 437400, 464141,
-                           486359]),
-       values=tensor([0.6537, 0.5151, 0.3039,  ..., 0.7629, 0.2656, 0.5446]),
+tensor(crow_indices=tensor([       0,       24,       53,  ..., 12499945,
+                            12499970, 12500000]),
+       col_indices=tensor([ 18146,  22620,  44577,  ..., 431175, 474683,
+                           476577]),
+       values=tensor([0.0909, 0.4067, 0.5768,  ..., 0.8704, 0.8226, 0.2311]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.4135, 0.5444, 0.9798,  ..., 0.8106, 0.6562, 0.9974])
+tensor([0.7846, 0.2097, 0.9073,  ..., 0.9672, 0.4159, 0.3283])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,17 +17,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 50.092703104019165 seconds
+Time: 50.31077265739441 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       19,       45,  ..., 12499947,
-                            12499973, 12500000]),
-       col_indices=tensor([ 17397,  55872, 132943,  ..., 437400, 464141,
-                           486359]),
-       values=tensor([0.6537, 0.5151, 0.3039,  ..., 0.7629, 0.2656, 0.5446]),
+tensor(crow_indices=tensor([       0,       24,       53,  ..., 12499945,
+                            12499970, 12500000]),
+       col_indices=tensor([ 18146,  22620,  44577,  ..., 431175, 474683,
+                           476577]),
+       values=tensor([0.0909, 0.4067, 0.5768,  ..., 0.8704, 0.8226, 0.2311]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.4135, 0.5444, 0.9798,  ..., 0.8106, 0.6562, 0.9974])
+tensor([0.7846, 0.2097, 0.9073,  ..., 0.9672, 0.4159, 0.3283])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -35,13 +35,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 50.092703104019165 seconds
+Time: 50.31077265739441 seconds
 
-[20.76, 20.64, 20.64, 20.64, 20.48, 20.64, 20.6, 20.8, 21.08, 20.96]
-[20.92, 20.92, 23.96, 24.8, 26.64, 28.44, 28.44, 30.32, 28.64, 28.92, 27.6, 26.6, 25.88, 25.0, 25.04, 25.08, 25.08, 25.16, 25.16, 25.12, 25.08, 24.88, 25.08, 25.12, 25.2, 25.28, 25.36, 25.32, 25.32, 25.04, 25.04, 25.28, 25.16, 25.12, 25.24, 25.16, 25.16, 25.36, 25.24, 25.2, 25.28, 25.12, 24.96, 25.12, 25.2, 25.32, 25.36, 25.48, 25.56, 25.52, 25.52, 25.36, 25.4, 25.2, 25.28, 25.56]
-58.704549074172974
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 50.092703104019165, 'TIME_S_1KI': 500.92703104019165, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1421.3682743358613, 'W': 24.212233919725165}
-[20.76, 20.64, 20.64, 20.64, 20.48, 20.64, 20.6, 20.8, 21.08, 20.96, 20.76, 20.4, 20.48, 20.92, 20.8, 20.92, 20.88, 20.72, 20.64, 21.0]
-373.02
-18.651
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 50.092703104019165, 'TIME_S_1KI': 500.92703104019165, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1421.3682743358613, 'W': 24.212233919725165, 'J_1KI': 14213.682743358613, 'W_1KI': 242.12233919725165, 'W_D': 5.561233919725165, 'J_D': 326.46972955346126, 'W_D_1KI': 55.61233919725165, 'J_D_1KI': 556.1233919725165}
+[20.8, 20.44, 20.36, 20.32, 20.28, 20.24, 20.36, 20.28, 20.28, 20.28]
+[20.4, 20.8, 20.64, 25.2, 26.28, 28.52, 30.88, 29.6, 29.28, 28.92, 28.28, 26.44, 26.08, 25.2, 25.04, 25.28, 25.24, 25.2, 25.2, 25.32, 25.36, 25.32, 25.32, 25.28, 25.24, 25.0, 25.0, 24.84, 24.88, 24.76, 24.88, 25.12, 25.24, 25.4, 25.68, 25.56, 25.6, 25.36, 25.08, 24.96, 24.96, 24.84, 24.88, 25.36, 25.32, 25.64, 25.6, 25.32, 25.2, 25.28, 25.04, 25.08, 25.0, 25.08, 25.0]
+57.730268716812134
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 50.31077265739441, 'TIME_S_1KI': 503.1077265739441, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1394.836717920303, 'W': 24.161271875633943}
+[20.8, 20.44, 20.36, 20.32, 20.28, 20.24, 20.36, 20.28, 20.28, 20.28, 20.88, 21.2, 21.0, 20.8, 20.92, 20.88, 20.88, 20.48, 20.72, 20.6]
+370.72
+18.536
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 50.31077265739441, 'TIME_S_1KI': 503.1077265739441, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1394.836717920303, 'W': 24.161271875633943, 'J_1KI': 13948.36717920303, 'W_1KI': 241.61271875633943, 'W_D': 5.625271875633942, 'J_D': 324.7484569854733, 'W_D_1KI': 56.25271875633942, 'J_D_1KI': 562.5271875633941}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.json
index 8bb383c..2112a13 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1701, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.195863723754883, "TIME_S_1KI": 5.994040989861777, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 298.5689013671875, "W": 22.02886990511616, "J_1KI": 175.5255152070473, "W_1KI": 12.950540802537425, "W_D": 3.5278699051161624, "J_D": 47.81508294677735, "W_D_1KI": 2.0739975926608833, "J_D_1KI": 1.2192813595889966}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1805, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.802817583084106, "TIME_S_1KI": 5.984940489243272, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 337.43533813476563, "W": 23.11078306223205, "J_1KI": 186.94478567023026, "W_1KI": 12.803757929214433, "W_D": 4.706783062232045, "J_D": 68.72267935943601, "W_D_1KI": 2.60763604555792, "J_D_1KI": 1.4446737094503712}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.output
index 7e66808..ecd1a48 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6172366142272949}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6196467876434326}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,     11,  ..., 249984, 249992,
+tensor(crow_indices=tensor([     0,      7,      9,  ..., 249995, 249997,
                             250000]),
-       col_indices=tensor([   55, 33912,  7825,  ..., 25553, 31300, 45367]),
-       values=tensor([0.2156, 0.3825, 0.1471,  ..., 0.6075, 0.9514, 0.6641]),
+       col_indices=tensor([ 8366,  9967, 13922,  ...,  7259, 19728, 20274]),
+       values=tensor([0.2696, 0.3809, 0.3608,  ..., 0.4336, 0.8213, 0.0030]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8972, 0.9948, 0.0628,  ..., 0.4950, 0.5589, 0.8119])
+tensor([0.8091, 0.0187, 0.2706,  ..., 0.3325, 0.9347, 0.7061])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 0.6172366142272949 seconds
+Time: 0.6196467876434326 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1701 -ss 50000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.195863723754883}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1694 -ss 50000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.853110074996948}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,     10,  ..., 249986, 249995,
+tensor(crow_indices=tensor([     0,      9,     15,  ..., 249992, 249997,
                             250000]),
-       col_indices=tensor([ 4095,  7631, 26458,  ..., 36946, 37655, 49733]),
-       values=tensor([0.3588, 0.4994, 0.4557,  ..., 0.6547, 0.8163, 0.6645]),
+       col_indices=tensor([ 2870,  8286, 14444,  ..., 21713, 35775, 49251]),
+       values=tensor([0.0516, 0.1595, 0.3064,  ..., 0.9376, 0.2976, 0.2768]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0295, 0.0838, 0.1870,  ..., 0.2542, 0.3969, 0.7673])
+tensor([0.4626, 0.6501, 0.4407,  ..., 0.3508, 0.8520, 0.2800])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.195863723754883 seconds
+Time: 9.853110074996948 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1805 -ss 50000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.802817583084106}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,     10,  ..., 249986, 249995,
+tensor(crow_indices=tensor([     0,      5,     12,  ..., 249993, 249997,
                             250000]),
-       col_indices=tensor([ 4095,  7631, 26458,  ..., 36946, 37655, 49733]),
-       values=tensor([0.3588, 0.4994, 0.4557,  ..., 0.6547, 0.8163, 0.6645]),
+       col_indices=tensor([ 8417, 19550, 34715,  ..., 18246, 29072, 37036]),
+       values=tensor([0.0315, 0.0556, 0.7166,  ..., 0.2758, 0.8792, 0.3364]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0295, 0.0838, 0.1870,  ..., 0.2542, 0.3969, 0.7673])
+tensor([0.0639, 0.6431, 0.4064,  ..., 0.4418, 0.0626, 0.0226])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +56,30 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.195863723754883 seconds
+Time: 10.802817583084106 seconds
 
-[20.6, 20.6, 20.68, 20.56, 20.68, 20.6, 20.6, 20.48, 20.36, 20.44]
-[20.44, 20.16, 20.24, 22.04, 22.92, 24.8, 25.72, 25.84, 25.56, 24.68, 24.84, 25.0, 25.2]
-13.55352783203125
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1701, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.195863723754883, 'TIME_S_1KI': 5.994040989861777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 298.5689013671875, 'W': 22.02886990511616}
-[20.6, 20.6, 20.68, 20.56, 20.68, 20.6, 20.6, 20.48, 20.36, 20.44, 20.56, 20.52, 20.52, 20.44, 20.44, 20.44, 20.44, 20.56, 20.88, 20.84]
-370.02
-18.500999999999998
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1701, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.195863723754883, 'TIME_S_1KI': 5.994040989861777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 298.5689013671875, 'W': 22.02886990511616, 'J_1KI': 175.5255152070473, 'W_1KI': 12.950540802537425, 'W_D': 3.5278699051161624, 'J_D': 47.81508294677735, 'W_D_1KI': 2.0739975926608833, 'J_D_1KI': 1.2192813595889966}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,     12,  ..., 249993, 249997,
+                            250000]),
+       col_indices=tensor([ 8417, 19550, 34715,  ..., 18246, 29072, 37036]),
+       values=tensor([0.0315, 0.0556, 0.7166,  ..., 0.2758, 0.8792, 0.3364]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0639, 0.6431, 0.4064,  ..., 0.4418, 0.0626, 0.0226])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.802817583084106 seconds
+
+[20.28, 20.24, 20.56, 20.4, 20.36, 20.56, 20.68, 20.88, 20.84, 20.84]
+[21.04, 20.84, 20.76, 24.6, 25.84, 27.68, 28.56, 26.12, 25.8, 24.72, 24.8, 24.76, 24.96, 24.96]
+14.600774765014648
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1805, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.802817583084106, 'TIME_S_1KI': 5.984940489243272, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 337.43533813476563, 'W': 23.11078306223205}
+[20.28, 20.24, 20.56, 20.4, 20.36, 20.56, 20.68, 20.88, 20.84, 20.84, 20.16, 20.04, 20.08, 20.2, 20.2, 20.36, 20.44, 20.52, 20.72, 20.72]
+368.08000000000004
+18.404000000000003
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1805, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.802817583084106, 'TIME_S_1KI': 5.984940489243272, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 337.43533813476563, 'W': 23.11078306223205, 'J_1KI': 186.94478567023026, 'W_1KI': 12.803757929214433, 'W_D': 4.706783062232045, 'J_D': 68.72267935943601, 'W_D_1KI': 2.60763604555792, 'J_D_1KI': 1.4446737094503712}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.json
index 911c49b..e1102fc 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 193, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.676417112350464, "TIME_S_1KI": 55.318223380054214, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 327.75970675468443, "W": 22.393652951640597, "J_1KI": 1698.2368225631317, "W_1KI": 116.02928990487356, "W_D": 4.002652951640599, "J_D": 58.58393717646598, "W_D_1KI": 20.73913446445906, "J_D_1KI": 107.45665525626457}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 182, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.083391189575195, "TIME_S_1KI": 55.403248294369206, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 329.27284624099735, "W": 22.48290781479867, "J_1KI": 1809.191462862623, "W_1KI": 123.5324605208718, "W_D": 3.9759078147986706, "J_D": 58.22905530524258, "W_D_1KI": 21.84564733405863, "J_D_1KI": 120.03102930801444}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.output
index 7534e40..7b030b2 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,34 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.431778192520142}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.760874509811401}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      57,      95,  ..., 2499895,
+tensor(crow_indices=tensor([      0,      40,      90,  ..., 2499907,
+                            2499950, 2500000]),
+       col_indices=tensor([ 5137,  7037,  7061,  ..., 47893, 48222, 48297]),
+       values=tensor([0.2856, 0.6560, 0.8379,  ..., 0.9963, 0.9596, 0.9129]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8414, 0.0757, 0.2660,  ..., 0.4452, 0.9388, 0.6329])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 5.760874509811401 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 182 -ss 50000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.083391189575195}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      51,     106,  ..., 2499891,
                             2499944, 2500000]),
-       col_indices=tensor([   14,  1180,  1352,  ..., 49220, 49912, 49936]),
-       values=tensor([0.8618, 0.4205, 0.6419,  ..., 0.4989, 0.5508, 0.1652]),
+       col_indices=tensor([  982,  1922,  2289,  ..., 46268, 46530, 48275]),
+       values=tensor([0.0968, 0.2247, 0.7152,  ..., 0.3494, 0.1935, 0.0398]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3965, 0.7585, 0.8670,  ..., 0.1152, 0.9413, 0.0865])
+tensor([0.0363, 0.2596, 0.3372,  ..., 0.9210, 0.3896, 0.9305])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 5.431778192520142 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 193 -ss 50000 -sd 0.001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.676417112350464}
+Time: 10.083391189575195 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      57,     106,  ..., 2499905,
-                            2499942, 2500000]),
-       col_indices=tensor([  275,  2452,  2625,  ..., 47289, 48937, 49987]),
-       values=tensor([0.8108, 0.0031, 0.6812,  ..., 0.9899, 0.5982, 0.1156]),
+tensor(crow_indices=tensor([      0,      51,     106,  ..., 2499891,
+                            2499944, 2500000]),
+       col_indices=tensor([  982,  1922,  2289,  ..., 46268, 46530, 48275]),
+       values=tensor([0.0968, 0.2247, 0.7152,  ..., 0.3494, 0.1935, 0.0398]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1893, 0.7932, 0.1409,  ..., 0.0408, 0.4757, 0.3205])
+tensor([0.0363, 0.2596, 0.3372,  ..., 0.9210, 0.3896, 0.9305])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,30 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.676417112350464 seconds
+Time: 10.083391189575195 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      57,     106,  ..., 2499905,
-                            2499942, 2500000]),
-       col_indices=tensor([  275,  2452,  2625,  ..., 47289, 48937, 49987]),
-       values=tensor([0.8108, 0.0031, 0.6812,  ..., 0.9899, 0.5982, 0.1156]),
-       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1893, 0.7932, 0.1409,  ..., 0.0408, 0.4757, 0.3205])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 2500000
-Density: 0.001
-Time: 10.676417112350464 seconds
-
-[20.32, 20.4, 20.88, 20.88, 20.88, 20.88, 20.64, 20.24, 20.48, 20.52]
-[20.48, 20.64, 20.88, 22.0, 23.72, 25.32, 26.2, 26.2, 26.28, 25.08, 24.6, 24.56, 24.36, 24.36]
-14.636276960372925
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.676417112350464, 'TIME_S_1KI': 55.318223380054214, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.75970675468443, 'W': 22.393652951640597}
-[20.32, 20.4, 20.88, 20.88, 20.88, 20.88, 20.64, 20.24, 20.48, 20.52, 20.24, 20.24, 20.24, 20.4, 20.4, 20.2, 20.16, 20.08, 20.16, 20.24]
-367.81999999999994
-18.391
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.676417112350464, 'TIME_S_1KI': 55.318223380054214, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.75970675468443, 'W': 22.393652951640597, 'J_1KI': 1698.2368225631317, 'W_1KI': 116.02928990487356, 'W_D': 4.002652951640599, 'J_D': 58.58393717646598, 'W_D_1KI': 20.73913446445906, 'J_D_1KI': 107.45665525626457}
+[20.36, 20.48, 20.36, 20.36, 20.44, 20.36, 20.36, 20.2, 20.2, 20.36]
+[20.6, 20.48, 21.6, 22.36, 24.2, 25.16, 26.2, 25.8, 25.48, 25.48, 24.84, 24.64, 24.6, 24.56]
+14.645474195480347
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 182, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.083391189575195, 'TIME_S_1KI': 55.403248294369206, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.27284624099735, 'W': 22.48290781479867}
+[20.36, 20.48, 20.36, 20.36, 20.44, 20.36, 20.36, 20.2, 20.2, 20.36, 20.12, 20.44, 20.28, 20.4, 20.88, 21.0, 21.04, 21.24, 21.04, 21.28]
+370.14
+18.506999999999998
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 182, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.083391189575195, 'TIME_S_1KI': 55.403248294369206, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.27284624099735, 'W': 22.48290781479867, 'J_1KI': 1809.191462862623, 'W_1KI': 123.5324605208718, 'W_D': 3.9759078147986706, 'J_D': 58.22905530524258, 'W_D_1KI': 21.84564733405863, 'J_D_1KI': 120.03102930801444}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.01.json
index 0d711cb..238daf2 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.01.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.01.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 53.1586058139801, "TIME_S_1KI": 531.586058139801, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1662.8207085037227, "W": 23.65320095688087, "J_1KI": 16628.20708503723, "W_1KI": 236.53200956880872, "W_D": 4.901200956880867, "J_D": 344.55456842803903, "W_D_1KI": 49.01200956880867, "J_D_1KI": 490.12009568808674}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 53.2018678188324, "TIME_S_1KI": 532.018678188324, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1611.726173362732, "W": 23.645525596360955, "J_1KI": 16117.26173362732, "W_1KI": 236.45525596360955, "W_D": 5.302525596360958, "J_D": 361.43071778011347, "W_D_1KI": 53.02525596360958, "J_D_1KI": 530.2525596360958}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.01.output
index 142816b..5cc7744 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.01.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.01 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 53.1586058139801}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 53.2018678188324}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      489,      973,  ..., 24998974,
-                            24999478, 25000000]),
-       col_indices=tensor([  275,   454,   699,  ..., 49715, 49729, 49796]),
-       values=tensor([0.3350, 0.9556, 0.9308,  ..., 0.7756, 0.4208, 0.8843]),
+tensor(crow_indices=tensor([       0,      513,      981,  ..., 24998959,
+                            24999509, 25000000]),
+       col_indices=tensor([ 1090,  1172,  1302,  ..., 49749, 49921, 49980]),
+       values=tensor([0.8467, 0.6526, 0.0884,  ..., 0.9518, 0.6309, 0.1810]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.3743, 0.4258, 0.0327,  ..., 0.7931, 0.5462, 0.4257])
+tensor([0.7712, 0.6597, 0.5718,  ..., 0.6878, 0.4395, 0.4563])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,16 +16,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 53.1586058139801 seconds
+Time: 53.2018678188324 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      489,      973,  ..., 24998974,
-                            24999478, 25000000]),
-       col_indices=tensor([  275,   454,   699,  ..., 49715, 49729, 49796]),
-       values=tensor([0.3350, 0.9556, 0.9308,  ..., 0.7756, 0.4208, 0.8843]),
+tensor(crow_indices=tensor([       0,      513,      981,  ..., 24998959,
+                            24999509, 25000000]),
+       col_indices=tensor([ 1090,  1172,  1302,  ..., 49749, 49921, 49980]),
+       values=tensor([0.8467, 0.6526, 0.0884,  ..., 0.9518, 0.6309, 0.1810]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.3743, 0.4258, 0.0327,  ..., 0.7931, 0.5462, 0.4257])
+tensor([0.7712, 0.6597, 0.5718,  ..., 0.6878, 0.4395, 0.4563])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -33,13 +33,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 53.1586058139801 seconds
+Time: 53.2018678188324 seconds
 
-[20.44, 20.76, 21.08, 21.12, 21.36, 21.16, 21.04, 21.0, 20.96, 20.84]
-[21.0, 20.84, 20.84, 23.72, 24.72, 26.8, 28.0, 29.48, 28.44, 28.04, 28.08, 27.92, 28.6, 27.68, 27.36, 26.52, 25.72, 24.68, 24.64, 24.72, 24.64, 24.64, 24.68, 24.68, 24.56, 24.76, 24.76, 24.64, 24.68, 24.64, 24.8, 24.64, 25.04, 24.96, 24.84, 24.88, 24.68, 24.68, 24.64, 24.8, 24.72, 24.72, 24.68, 24.56, 24.36, 24.36, 24.48, 24.4, 24.44, 24.4, 24.6, 24.56, 24.72, 24.96, 24.92, 24.84, 24.76, 24.72, 24.76, 24.8, 24.88, 24.92, 24.76, 24.68, 24.56, 24.64, 24.6]
-70.30002880096436
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 53.1586058139801, 'TIME_S_1KI': 531.586058139801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1662.8207085037227, 'W': 23.65320095688087}
-[20.44, 20.76, 21.08, 21.12, 21.36, 21.16, 21.04, 21.0, 20.96, 20.84, 20.6, 20.6, 20.72, 20.72, 20.68, 20.56, 20.76, 20.64, 20.6, 20.68]
-375.04
-18.752000000000002
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 53.1586058139801, 'TIME_S_1KI': 531.586058139801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1662.8207085037227, 'W': 23.65320095688087, 'J_1KI': 16628.20708503723, 'W_1KI': 236.53200956880872, 'W_D': 4.901200956880867, 'J_D': 344.55456842803903, 'W_D_1KI': 49.01200956880867, 'J_D_1KI': 490.12009568808674}
+[20.48, 20.44, 20.68, 20.68, 20.44, 20.48, 20.44, 20.32, 20.44, 20.56]
+[20.56, 20.48, 20.56, 24.2, 24.84, 26.92, 29.04, 30.2, 29.4, 28.96, 28.96, 28.32, 28.76, 26.88, 26.6, 26.24, 24.96, 24.56, 24.4, 24.44, 24.44, 24.76, 24.72, 24.64, 24.6, 24.52, 24.4, 24.32, 24.52, 24.52, 24.68, 24.76, 24.92, 24.96, 24.8, 25.0, 24.84, 24.84, 24.72, 24.72, 24.72, 24.96, 24.96, 24.96, 24.84, 24.96, 24.68, 24.52, 24.6, 24.68, 24.72, 24.8, 24.84, 24.8, 24.64, 24.6, 24.52, 24.48, 24.64, 24.6, 24.76, 24.68, 24.48, 24.48, 24.36]
+68.16199398040771
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 53.2018678188324, 'TIME_S_1KI': 532.018678188324, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1611.726173362732, 'W': 23.645525596360955}
+[20.48, 20.44, 20.68, 20.68, 20.44, 20.48, 20.44, 20.32, 20.44, 20.56, 20.24, 20.24, 20.2, 20.24, 20.36, 20.2, 20.12, 20.4, 20.36, 20.36]
+366.85999999999996
+18.342999999999996
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 53.2018678188324, 'TIME_S_1KI': 532.018678188324, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1611.726173362732, 'W': 23.645525596360955, 'J_1KI': 16117.26173362732, 'W_1KI': 236.45525596360955, 'W_D': 5.302525596360958, 'J_D': 361.43071778011347, 'W_D_1KI': 53.02525596360958, 'J_D_1KI': 530.2525596360958}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.json
index 458d682..d8d6b08 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 10429, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.529776096343994, "TIME_S_1KI": 1.0096630641810331, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 331.7018713665009, "W": 22.733124806095596, "J_1KI": 31.80572167671885, "W_1KI": 2.1797990992516634, "W_D": 4.373124806095593, "J_D": 63.8088117790222, "W_D_1KI": 0.41932350235838456, "J_D_1KI": 0.040207450604888735}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 10737, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.584118604660034, "TIME_S_1KI": 0.9857612559057496, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 341.27173243522645, "W": 23.373566767227835, "J_1KI": 31.78464491340472, "W_1KI": 2.176917832469762, "W_D": 4.731566767227836, "J_D": 69.08444927835465, "W_D_1KI": 0.4406786595164232, "J_D_1KI": 0.04104299706774921}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.output
index f7f583d..f384557 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.1077582836151123}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.10845208168029785}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 24997, 24998, 25000]),
-       col_indices=tensor([30956, 25020,  4290,  ...,  1571,  5930, 34059]),
-       values=tensor([0.1925, 0.5429, 0.7430,  ..., 0.0669, 0.5504, 0.8934]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([10441, 12058,  1165,  ..., 36295, 43132,  4272]),
+       values=tensor([0.3253, 0.3276, 0.1912,  ..., 0.9523, 0.2694, 0.6554]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9564, 0.4579, 0.9465,  ..., 0.7236, 0.9546, 0.7676])
+tensor([0.6034, 0.6498, 0.5195,  ..., 0.6962, 0.2706, 0.7770])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,18 +15,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.1077582836151123 seconds
+Time: 0.10845208168029785 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 9744 -ss 50000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.809481859207153}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 9681 -ss 50000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.466950178146362}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 24999, 25000]),
-       col_indices=tensor([47588, 45161, 40455,  ..., 30522, 42036,  2005]),
-       values=tensor([0.2055, 0.2802, 0.2448,  ..., 0.0926, 0.8451, 0.9361]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([16649, 30767, 40043,  ..., 14732, 49411, 48251]),
+       values=tensor([0.2763, 0.6267, 0.8853,  ..., 0.1180, 0.1805, 0.1584]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5605, 0.1853, 0.0043,  ..., 0.6007, 0.1968, 0.9775])
+tensor([0.0516, 0.0339, 0.9930,  ..., 0.5767, 0.5756, 0.9045])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,18 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 9.809481859207153 seconds
+Time: 9.466950178146362 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10429 -ss 50000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.529776096343994}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10737 -ss 50000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.584118604660034}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 24998, 24999, 25000]),
-       col_indices=tensor([20158, 23859, 20874,  ..., 41939, 15422, 41283]),
-       values=tensor([0.7225, 0.1851, 0.6655,  ..., 0.6086, 0.8791, 0.8414]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 25000, 25000]),
+       col_indices=tensor([ 5127, 27266, 30482,  ...,  7060, 29930, 33047]),
+       values=tensor([0.1376, 0.9046, 0.0909,  ..., 0.3129, 0.7703, 0.5618]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1130, 0.7539, 0.9598,  ..., 0.4914, 0.7455, 0.6539])
+tensor([0.5979, 0.8287, 0.9902,  ..., 0.5455, 0.7521, 0.9043])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,15 +53,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.529776096343994 seconds
+Time: 10.584118604660034 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 24998, 24999, 25000]),
-       col_indices=tensor([20158, 23859, 20874,  ..., 41939, 15422, 41283]),
-       values=tensor([0.7225, 0.1851, 0.6655,  ..., 0.6086, 0.8791, 0.8414]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 25000, 25000]),
+       col_indices=tensor([ 5127, 27266, 30482,  ...,  7060, 29930, 33047]),
+       values=tensor([0.1376, 0.9046, 0.0909,  ..., 0.3129, 0.7703, 0.5618]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1130, 0.7539, 0.9598,  ..., 0.4914, 0.7455, 0.6539])
+tensor([0.5979, 0.8287, 0.9902,  ..., 0.5455, 0.7521, 0.9043])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -69,13 +69,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.529776096343994 seconds
+Time: 10.584118604660034 seconds
 
-[20.44, 20.44, 20.32, 20.64, 20.6, 20.48, 20.6, 20.36, 20.32, 20.16]
-[20.36, 20.16, 20.76, 22.32, 23.88, 23.88, 25.12, 26.36, 26.64, 26.44, 26.08, 26.2, 25.88, 25.48]
-14.591125249862671
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10429, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.529776096343994, 'TIME_S_1KI': 1.0096630641810331, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 331.7018713665009, 'W': 22.733124806095596}
-[20.44, 20.44, 20.32, 20.64, 20.6, 20.48, 20.6, 20.36, 20.32, 20.16, 20.56, 20.32, 20.36, 20.56, 20.36, 20.28, 20.44, 20.16, 20.16, 20.44]
-367.20000000000005
-18.360000000000003
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10429, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.529776096343994, 'TIME_S_1KI': 1.0096630641810331, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 331.7018713665009, 'W': 22.733124806095596, 'J_1KI': 31.80572167671885, 'W_1KI': 2.1797990992516634, 'W_D': 4.373124806095593, 'J_D': 63.8088117790222, 'W_D_1KI': 0.41932350235838456, 'J_D_1KI': 0.040207450604888735}
+[20.16, 20.36, 20.52, 20.48, 20.72, 20.72, 20.8, 20.52, 20.48, 20.52]
+[20.48, 20.56, 21.24, 23.48, 25.24, 26.28, 27.48, 27.48, 26.56, 25.88, 26.0, 26.0, 26.08, 26.12]
+14.600755453109741
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10737, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.584118604660034, 'TIME_S_1KI': 0.9857612559057496, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 341.27173243522645, 'W': 23.373566767227835}
+[20.16, 20.36, 20.52, 20.48, 20.72, 20.72, 20.8, 20.52, 20.48, 20.52, 20.68, 21.12, 21.04, 21.08, 21.0, 20.92, 20.68, 20.64, 20.76, 20.64]
+372.84
+18.642
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10737, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.584118604660034, 'TIME_S_1KI': 0.9857612559057496, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 341.27173243522645, 'W': 23.373566767227835, 'J_1KI': 31.78464491340472, 'W_1KI': 2.176917832469762, 'W_D': 4.731566767227836, 'J_D': 69.08444927835465, 'W_D_1KI': 0.4406786595164232, 'J_D_1KI': 0.04104299706774921}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_5e-05.json
index fea28ac..8608fc6 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_5e-05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3217, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.183324813842773, "TIME_S_1KI": 3.1654724320307035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 313.5762239074707, "W": 23.142141761096784, "J_1KI": 97.47473543906457, "W_1KI": 7.193702754459679, "W_D": 4.7631417610967866, "J_D": 64.54061265373231, "W_D_1KI": 1.4806160276956128, "J_D_1KI": 0.46024744410805496}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3224, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.37877106666565, "TIME_S_1KI": 3.2192217948714794, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 314.116130990982, "W": 23.234138612361182, "J_1KI": 97.43056172176861, "W_1KI": 7.206618676290689, "W_D": 4.5161386123611855, "J_D": 61.056362432956696, "W_D_1KI": 1.4007874107820055, "J_D_1KI": 0.4344874102921853}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_5e-05.output
index dbda68a..e89d63c 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_5e-05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_50000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 5e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.3263556957244873}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.325664758682251}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      2,  ..., 124996, 124997,
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 124996, 124999,
                             125000]),
-       col_indices=tensor([ 8999, 37078,  2648,  ..., 24880, 43913, 47673]),
-       values=tensor([0.7939, 0.1706, 0.9831,  ..., 0.2838, 0.4924, 0.0921]),
+       col_indices=tensor([ 6097, 42457,  3456,  ..., 36348, 42448, 28927]),
+       values=tensor([0.4987, 0.3368, 0.7540,  ..., 0.7918, 0.8816, 0.1270]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.2834, 0.1318, 0.3567,  ..., 0.3503, 0.0519, 0.6169])
+tensor([0.7083, 0.2316, 0.4262,  ..., 0.1086, 0.8747, 0.3498])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 0.3263556957244873 seconds
+Time: 0.325664758682251 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3217 -ss 50000 -sd 5e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.183324813842773}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3224 -ss 50000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.37877106666565}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      7,  ..., 125000, 125000,
+tensor(crow_indices=tensor([     0,      4,      9,  ..., 124994, 124996,
                             125000]),
-       col_indices=tensor([ 9508, 26799,  1812,  ..., 32912, 38580, 39384]),
-       values=tensor([0.1038, 0.2683, 0.7729,  ..., 0.6337, 0.2232, 0.8870]),
+       col_indices=tensor([15659, 16774, 27785,  ..., 24457, 28743, 36442]),
+       values=tensor([0.7259, 0.8899, 0.5066,  ..., 0.6604, 0.1626, 0.6211]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.7924, 0.9644, 0.0933,  ..., 0.2945, 0.3904, 0.9557])
+tensor([0.0844, 0.2883, 0.1673,  ..., 0.8919, 0.1822, 0.4280])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.183324813842773 seconds
+Time: 10.37877106666565 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      7,  ..., 125000, 125000,
+tensor(crow_indices=tensor([     0,      4,      9,  ..., 124994, 124996,
                             125000]),
-       col_indices=tensor([ 9508, 26799,  1812,  ..., 32912, 38580, 39384]),
-       values=tensor([0.1038, 0.2683, 0.7729,  ..., 0.6337, 0.2232, 0.8870]),
+       col_indices=tensor([15659, 16774, 27785,  ..., 24457, 28743, 36442]),
+       values=tensor([0.7259, 0.8899, 0.5066,  ..., 0.6604, 0.1626, 0.6211]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.7924, 0.9644, 0.0933,  ..., 0.2945, 0.3904, 0.9557])
+tensor([0.0844, 0.2883, 0.1673,  ..., 0.8919, 0.1822, 0.4280])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.183324813842773 seconds
+Time: 10.37877106666565 seconds
 
-[20.36, 20.32, 20.32, 20.32, 20.36, 20.28, 20.48, 20.52, 20.64, 20.52]
-[20.52, 20.48, 21.56, 23.32, 25.2, 26.32, 27.16, 27.16, 26.96, 26.76, 25.76, 25.84, 25.52]
-13.550008773803711
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3217, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.183324813842773, 'TIME_S_1KI': 3.1654724320307035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 313.5762239074707, 'W': 23.142141761096784}
-[20.36, 20.32, 20.32, 20.32, 20.36, 20.28, 20.48, 20.52, 20.64, 20.52, 20.52, 20.6, 20.44, 20.6, 20.48, 20.32, 20.2, 20.36, 20.4, 20.48]
-367.5799999999999
-18.378999999999998
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3217, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.183324813842773, 'TIME_S_1KI': 3.1654724320307035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 313.5762239074707, 'W': 23.142141761096784, 'J_1KI': 97.47473543906457, 'W_1KI': 7.193702754459679, 'W_D': 4.7631417610967866, 'J_D': 64.54061265373231, 'W_D_1KI': 1.4806160276956128, 'J_D_1KI': 0.46024744410805496}
+[20.16, 20.52, 20.52, 20.68, 21.04, 21.04, 20.8, 20.72, 20.52, 20.2]
+[20.2, 20.2, 20.28, 24.4, 25.8, 27.76, 28.48, 29.28, 25.96, 25.12, 25.44, 25.48, 25.32]
+13.519594430923462
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3224, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.37877106666565, 'TIME_S_1KI': 3.2192217948714794, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.116130990982, 'W': 23.234138612361182}
+[20.16, 20.52, 20.52, 20.68, 21.04, 21.04, 20.8, 20.72, 20.52, 20.2, 20.56, 20.84, 21.04, 21.12, 21.16, 21.0, 21.0, 20.96, 20.68, 20.52]
+374.35999999999996
+18.717999999999996
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3224, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.37877106666565, 'TIME_S_1KI': 3.2192217948714794, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 314.116130990982, 'W': 23.234138612361182, 'J_1KI': 97.43056172176861, 'W_1KI': 7.206618676290689, 'W_D': 4.5161386123611855, 'J_D': 61.056362432956696, 'W_D_1KI': 1.4007874107820055, 'J_D_1KI': 0.4344874102921853}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.json
index 769abe1..9d5e4cc 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 97993, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.781827449798584, "TIME_S_1KI": 0.1100265064831017, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 315.41705104827884, "W": 21.541217597275665, "J_1KI": 3.218771249459439, "W_1KI": 0.21982404454681115, "W_D": 2.808217597275668, "J_D": 41.119296494484, "W_D_1KI": 0.02865732855689353, "J_D_1KI": 0.00029244260872606745}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 95747, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.263434171676636, "TIME_S_1KI": 0.10719327155604494, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 333.5903104114533, "W": 22.704418765935785, "J_1KI": 3.484081072111432, "W_1KI": 0.23712929664569946, "W_D": 4.341418765935785, "J_D": 63.78737322831156, "W_D_1KI": 0.04534260881213808, "J_D_1KI": 0.0004735668878621584}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.output
index bb7f54b..55ca761 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.0191800594329834}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.018416166305541992}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([3313, 1621, 3812,  ..., 4525, 1664, 4698]),
-       values=tensor([0.0941, 0.2796, 0.9707,  ..., 0.4661, 0.7642, 0.2416]),
+tensor(crow_indices=tensor([   0,    2,    2,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([ 735, 4326,  100,  ..., 1042, 2421, 4766]),
+       values=tensor([0.6086, 0.7437, 0.5596,  ..., 0.8625, 0.2710, 0.0986]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.5336, 0.9402, 0.6361,  ..., 0.0126, 0.4753, 0.7232])
+tensor([0.9833, 0.8269, 0.5336,  ..., 0.7281, 0.6185, 0.5983])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.0191800594329834 seconds
+Time: 0.018416166305541992 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 54744 -ss 5000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.86583399772644}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 57015 -ss 5000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.25248908996582}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([ 440, 3019, 2397,  ..., 2648, 4224, 1471]),
-       values=tensor([0.9686, 0.9548, 0.6770,  ..., 0.0683, 0.1247, 0.7029]),
+tensor(crow_indices=tensor([   0,    0,    3,  ..., 2498, 2499, 2500]),
+       col_indices=tensor([ 369, 1877, 3345,  ...,   21, 2128, 3541]),
+       values=tensor([0.3671, 0.0402, 0.3630,  ..., 0.4019, 0.4016, 0.1662]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.3648, 0.8360, 0.9424,  ..., 0.5773, 0.5768, 0.8650])
+tensor([0.2965, 0.8012, 0.0739,  ..., 0.9768, 0.4941, 0.4983])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 5.86583399772644 seconds
+Time: 6.25248908996582 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 97993 -ss 5000 -sd 0.0001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.781827449798584}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 95747 -ss 5000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.263434171676636}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    2,  ..., 2498, 2500, 2500]),
-       col_indices=tensor([3713, 3378, 4473,  ..., 4286, 2104, 3764]),
-       values=tensor([0.2566, 0.6316, 0.0221,  ..., 0.9864, 0.6559, 0.8912]),
+tensor(crow_indices=tensor([   0,    2,    3,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([1213, 4481,  360,  ..., 4227, 4904, 3632]),
+       values=tensor([0.3676, 0.2026, 0.1814,  ..., 0.2745, 0.8391, 0.9884]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.3213, 0.3541, 0.7168,  ..., 0.5598, 0.7087, 0.6560])
+tensor([0.3591, 0.1818, 0.1202,  ..., 0.7531, 0.0604, 0.6207])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.781827449798584 seconds
+Time: 10.263434171676636 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    2,  ..., 2498, 2500, 2500]),
-       col_indices=tensor([3713, 3378, 4473,  ..., 4286, 2104, 3764]),
-       values=tensor([0.2566, 0.6316, 0.0221,  ..., 0.9864, 0.6559, 0.8912]),
+tensor(crow_indices=tensor([   0,    2,    3,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([1213, 4481,  360,  ..., 4227, 4904, 3632]),
+       values=tensor([0.3676, 0.2026, 0.1814,  ..., 0.2745, 0.8391, 0.9884]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.3213, 0.3541, 0.7168,  ..., 0.5598, 0.7087, 0.6560])
+tensor([0.3591, 0.1818, 0.1202,  ..., 0.7531, 0.0604, 0.6207])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.781827449798584 seconds
+Time: 10.263434171676636 seconds
 
-[20.44, 20.48, 20.88, 20.96, 20.96, 20.92, 20.72, 20.72, 20.56, 20.48]
-[20.44, 20.32, 20.6, 21.56, 23.32, 24.04, 24.72, 24.96, 24.56, 23.56, 23.68, 23.44, 23.52, 23.56]
-14.642489433288574
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 97993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.781827449798584, 'TIME_S_1KI': 0.1100265064831017, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.41705104827884, 'W': 21.541217597275665}
-[20.44, 20.48, 20.88, 20.96, 20.96, 20.92, 20.72, 20.72, 20.56, 20.48, 20.36, 20.44, 20.44, 20.32, 20.4, 20.68, 21.08, 21.76, 21.76, 21.88]
-374.65999999999997
-18.732999999999997
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 97993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.781827449798584, 'TIME_S_1KI': 0.1100265064831017, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.41705104827884, 'W': 21.541217597275665, 'J_1KI': 3.218771249459439, 'W_1KI': 0.21982404454681115, 'W_D': 2.808217597275668, 'J_D': 41.119296494484, 'W_D_1KI': 0.02865732855689353, 'J_D_1KI': 0.00029244260872606745}
+[20.84, 20.68, 20.64, 20.56, 20.56, 20.56, 20.6, 20.48, 20.52, 20.56]
+[20.88, 20.76, 23.04, 24.4, 25.92, 26.52, 27.36, 27.36, 24.8, 24.32, 23.56, 23.64, 23.56, 23.4]
+14.692748308181763
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 95747, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.263434171676636, 'TIME_S_1KI': 0.10719327155604494, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 333.5903104114533, 'W': 22.704418765935785}
+[20.84, 20.68, 20.64, 20.56, 20.56, 20.56, 20.6, 20.48, 20.52, 20.56, 20.0, 20.24, 20.4, 20.24, 20.28, 20.28, 20.04, 20.28, 20.16, 20.08]
+367.26
+18.363
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 95747, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.263434171676636, 'TIME_S_1KI': 0.10719327155604494, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 333.5903104114533, 'W': 22.704418765935785, 'J_1KI': 3.484081072111432, 'W_1KI': 0.23712929664569946, 'W_D': 4.341418765935785, 'J_D': 63.78737322831156, 'W_D_1KI': 0.04534260881213808, 'J_D_1KI': 0.0004735668878621584}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.json
index 1de7362..54b5f5b 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 17801, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.451899290084839, "TIME_S_1KI": 0.5871523672875029, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 335.3643196678161, "W": 22.918247866906853, "J_1KI": 18.839633709781257, "W_1KI": 1.2874696852371694, "W_D": 4.382247866906855, "J_D": 64.12573871421813, "W_D_1KI": 0.24617987005824704, "J_D_1KI": 0.013829552837382564}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 17473, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.709591150283813, "TIME_S_1KI": 0.612922288690197, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 324.97824934959414, "W": 22.14868587809875, "J_1KI": 18.598881093664176, "W_1KI": 1.2675949108967408, "W_D": 3.5076858780987514, "J_D": 51.466783275842694, "W_D_1KI": 0.20074891993926353, "J_D_1KI": 0.011489092882691211}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.output
index 6e9f06a..66e7d6f 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06674790382385254}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.0665597915649414}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     6,    11,  ..., 24994, 24998, 25000]),
-       col_indices=tensor([ 153, 1166, 1591,  ..., 4476, 1654, 3013]),
-       values=tensor([0.9133, 0.8479, 0.0929,  ..., 0.2328, 0.6185, 0.0308]),
+tensor(crow_indices=tensor([    0,     5,    14,  ..., 24994, 24998, 25000]),
+       col_indices=tensor([ 507,  638, 1055,  ..., 4798, 3833, 3997]),
+       values=tensor([0.8063, 0.4305, 0.6118,  ..., 0.6098, 0.4042, 0.1710]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9627, 0.4329, 0.3045,  ..., 0.2813, 0.7730, 0.0924])
+tensor([0.6867, 0.0560, 0.6335,  ..., 0.3316, 0.3395, 0.5116])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.06674790382385254 seconds
+Time: 0.0665597915649414 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 15730 -ss 5000 -sd 0.001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.27815866470337}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 15775 -ss 5000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.479244947433472}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     5,  ..., 24988, 24995, 25000]),
-       col_indices=tensor([1294,  410,  634,  ..., 1096, 2182, 3875]),
-       values=tensor([0.7576, 0.8466, 0.6529,  ..., 0.8373, 0.3120, 0.9707]),
+tensor(crow_indices=tensor([    0,     4,    11,  ..., 24992, 24995, 25000]),
+       col_indices=tensor([2207, 4124, 4643,  ..., 2062, 3898, 4585]),
+       values=tensor([0.5047, 0.0690, 0.0441,  ..., 0.5027, 0.1579, 0.0562]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.6367, 0.3440, 0.8123,  ..., 0.8035, 0.7344, 0.3858])
+tensor([0.6664, 0.4626, 0.0519,  ..., 0.2284, 0.8294, 0.4871])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 9.27815866470337 seconds
+Time: 9.479244947433472 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17801 -ss 5000 -sd 0.001 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.451899290084839}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17473 -ss 5000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.709591150283813}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     5,  ..., 24991, 24996, 25000]),
-       col_indices=tensor([ 139, 2091, 2694,  ..., 3635, 3692, 4401]),
-       values=tensor([0.7198, 0.5125, 0.0166,  ..., 0.6335, 0.1279, 0.4059]),
+tensor(crow_indices=tensor([    0,     6,    16,  ..., 24987, 24994, 25000]),
+       col_indices=tensor([2119, 2193, 3608,  ..., 1895, 4273, 4313]),
+       values=tensor([0.0069, 0.3550, 0.7527,  ..., 0.0941, 0.9384, 0.2112]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0330, 0.5472, 0.9005,  ..., 0.3693, 0.0673, 0.4597])
+tensor([0.9071, 0.9833, 0.8622,  ..., 0.4214, 0.4481, 0.9386])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.451899290084839 seconds
+Time: 10.709591150283813 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     5,  ..., 24991, 24996, 25000]),
-       col_indices=tensor([ 139, 2091, 2694,  ..., 3635, 3692, 4401]),
-       values=tensor([0.7198, 0.5125, 0.0166,  ..., 0.6335, 0.1279, 0.4059]),
+tensor(crow_indices=tensor([    0,     6,    16,  ..., 24987, 24994, 25000]),
+       col_indices=tensor([2119, 2193, 3608,  ..., 1895, 4273, 4313]),
+       values=tensor([0.0069, 0.3550, 0.7527,  ..., 0.0941, 0.9384, 0.2112]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0330, 0.5472, 0.9005,  ..., 0.3693, 0.0673, 0.4597])
+tensor([0.9071, 0.9833, 0.8622,  ..., 0.4214, 0.4481, 0.9386])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.451899290084839 seconds
+Time: 10.709591150283813 seconds
 
-[20.28, 20.36, 20.4, 20.6, 20.68, 20.64, 20.52, 20.48, 20.32, 20.24]
-[20.4, 20.48, 23.8, 23.8, 25.36, 27.2, 28.0, 28.44, 25.2, 23.88, 23.72, 23.92, 24.08, 24.08]
-14.63306975364685
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 17801, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.451899290084839, 'TIME_S_1KI': 0.5871523672875029, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.3643196678161, 'W': 22.918247866906853}
-[20.28, 20.36, 20.4, 20.6, 20.68, 20.64, 20.52, 20.48, 20.32, 20.24, 20.44, 20.64, 20.96, 20.68, 20.56, 20.68, 20.72, 20.76, 20.76, 20.96]
-370.71999999999997
-18.535999999999998
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 17801, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.451899290084839, 'TIME_S_1KI': 0.5871523672875029, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.3643196678161, 'W': 22.918247866906853, 'J_1KI': 18.839633709781257, 'W_1KI': 1.2874696852371694, 'W_D': 4.382247866906855, 'J_D': 64.12573871421813, 'W_D_1KI': 0.24617987005824704, 'J_D_1KI': 0.013829552837382564}
+[20.52, 20.72, 20.68, 20.56, 20.52, 20.56, 20.48, 20.48, 20.6, 20.56]
+[20.68, 20.76, 21.92, 21.92, 23.64, 25.88, 26.32, 26.52, 25.52, 23.72, 23.6, 23.68, 23.72, 23.4]
+14.672574758529663
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 17473, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.709591150283813, 'TIME_S_1KI': 0.612922288690197, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.97824934959414, 'W': 22.14868587809875}
+[20.52, 20.72, 20.68, 20.56, 20.52, 20.56, 20.48, 20.48, 20.6, 20.56, 20.68, 20.8, 21.4, 21.44, 21.04, 20.88, 20.8, 20.8, 20.16, 20.04]
+372.82
+18.641
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 17473, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.709591150283813, 'TIME_S_1KI': 0.612922288690197, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.97824934959414, 'W': 22.14868587809875, 'J_1KI': 18.598881093664176, 'W_1KI': 1.2675949108967408, 'W_D': 3.5076858780987514, 'J_D': 51.466783275842694, 'W_D_1KI': 0.20074891993926353, 'J_D_1KI': 0.011489092882691211}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.json
index d1eb1c9..ddb34f3 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1927, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.278133630752563, "TIME_S_1KI": 5.333748640764174, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 315.9295339012146, "W": 21.68469080536072, "J_1KI": 163.94890186881918, "W_1KI": 11.253082929611168, "W_D": 3.326690805360723, "J_D": 48.46736737012868, "W_D_1KI": 1.7263574495904115, "J_D_1KI": 0.8958782820915472}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1833, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.383540153503418, "TIME_S_1KI": 5.6647791344808605, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 292.9585527515411, "W": 21.694089269658985, "J_1KI": 159.82463325234104, "W_1KI": 11.835291472809049, "W_D": 3.3000892696589865, "J_D": 44.564644515514345, "W_D_1KI": 1.8003760336382906, "J_D_1KI": 0.9822018732342012}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.output
index 9788e34..8dfa4d8 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.01 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.5448694229125977}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.5728230476379395}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     55,    104,  ..., 249896, 249951,
+tensor(crow_indices=tensor([     0,     43,     96,  ..., 249904, 249954,
                             250000]),
-       col_indices=tensor([ 128,  142,  245,  ..., 4657, 4734, 4838]),
-       values=tensor([0.1820, 0.1438, 0.1562,  ..., 0.6881, 0.0081, 0.4382]),
+       col_indices=tensor([  95,  346,  388,  ..., 4782, 4863, 4911]),
+       values=tensor([0.3664, 0.9331, 0.5456,  ..., 0.6113, 0.2274, 0.2730]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7604, 0.0772, 0.6951,  ..., 0.4926, 0.6864, 0.3702])
+tensor([0.4281, 0.8072, 0.4313,  ..., 0.4409, 0.3944, 0.1429])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 0.5448694229125977 seconds
+Time: 0.5728230476379395 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1927 -ss 5000 -sd 0.01 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.278133630752563}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1833 -ss 5000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.383540153503418}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     52,    109,  ..., 249898, 249957,
+tensor(crow_indices=tensor([     0,     46,     90,  ..., 249904, 249947,
                             250000]),
-       col_indices=tensor([  25,  140,  158,  ..., 4486, 4823, 4835]),
-       values=tensor([0.8176, 0.1521, 0.6094,  ..., 0.2740, 0.3181, 0.5161]),
+       col_indices=tensor([  12,   53,   87,  ..., 4543, 4695, 4843]),
+       values=tensor([0.5985, 0.4900, 0.8442,  ..., 0.5790, 0.3470, 0.4042]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4533, 0.4508, 0.3256,  ..., 0.6556, 0.1742, 0.9221])
+tensor([0.7634, 0.7341, 0.0971,  ..., 0.1366, 0.5741, 0.8769])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.278133630752563 seconds
+Time: 10.383540153503418 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     52,    109,  ..., 249898, 249957,
+tensor(crow_indices=tensor([     0,     46,     90,  ..., 249904, 249947,
                             250000]),
-       col_indices=tensor([  25,  140,  158,  ..., 4486, 4823, 4835]),
-       values=tensor([0.8176, 0.1521, 0.6094,  ..., 0.2740, 0.3181, 0.5161]),
+       col_indices=tensor([  12,   53,   87,  ..., 4543, 4695, 4843]),
+       values=tensor([0.5985, 0.4900, 0.8442,  ..., 0.5790, 0.3470, 0.4042]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4533, 0.4508, 0.3256,  ..., 0.6556, 0.1742, 0.9221])
+tensor([0.7634, 0.7341, 0.0971,  ..., 0.1366, 0.5741, 0.8769])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.278133630752563 seconds
+Time: 10.383540153503418 seconds
 
-[20.28, 20.32, 20.56, 20.6, 20.48, 20.56, 20.76, 20.68, 20.88, 20.88]
-[20.72, 20.48, 20.56, 21.28, 22.24, 24.0, 24.68, 25.2, 25.04, 23.88, 23.88, 24.04, 24.2, 24.4]
-14.56924319267273
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1927, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.278133630752563, 'TIME_S_1KI': 5.333748640764174, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.9295339012146, 'W': 21.68469080536072}
-[20.28, 20.32, 20.56, 20.6, 20.48, 20.56, 20.76, 20.68, 20.88, 20.88, 20.16, 20.08, 20.16, 20.16, 20.24, 20.36, 20.08, 20.2, 20.28, 20.2]
-367.15999999999997
-18.357999999999997
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1927, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.278133630752563, 'TIME_S_1KI': 5.333748640764174, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.9295339012146, 'W': 21.68469080536072, 'J_1KI': 163.94890186881918, 'W_1KI': 11.253082929611168, 'W_D': 3.326690805360723, 'J_D': 48.46736737012868, 'W_D_1KI': 1.7263574495904115, 'J_D_1KI': 0.8958782820915472}
+[20.48, 20.48, 20.52, 20.36, 20.32, 20.2, 20.36, 20.44, 20.44, 20.44]
+[20.44, 20.56, 20.76, 21.96, 22.6, 24.4, 25.2, 25.2, 25.16, 24.2, 24.2, 24.2, 24.2]
+13.504072427749634
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1833, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.383540153503418, 'TIME_S_1KI': 5.6647791344808605, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 292.9585527515411, 'W': 21.694089269658985}
+[20.48, 20.48, 20.52, 20.36, 20.32, 20.2, 20.36, 20.44, 20.44, 20.44, 20.32, 20.12, 20.24, 20.52, 20.6, 20.6, 20.88, 20.64, 20.36, 20.36]
+367.88
+18.394
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1833, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.383540153503418, 'TIME_S_1KI': 5.6647791344808605, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 292.9585527515411, 'W': 21.694089269658985, 'J_1KI': 159.82463325234104, 'W_1KI': 11.835291472809049, 'W_D': 3.3000892696589865, 'J_D': 44.564644515514345, 'W_D_1KI': 1.8003760336382906, 'J_D_1KI': 0.9822018732342012}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.json
index 942bb7d..2707bfa 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 393, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.605815887451172, "TIME_S_1KI": 26.986808873921557, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 343.57593024253845, "W": 23.547531420592268, "J_1KI": 874.239008250734, "W_1KI": 59.917382749598644, "W_D": 5.065531420592269, "J_D": 73.90985657548903, "W_D_1KI": 12.889392927715695, "J_D_1KI": 32.79743747510355}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 396, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.44465947151184, "TIME_S_1KI": 26.37540270583798, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 357.4221782398224, "W": 22.896313160260082, "J_1KI": 902.5812581813697, "W_1KI": 57.818972626919404, "W_D": 4.42131316026008, "J_D": 69.01877037405966, "W_D_1KI": 11.16493222287899, "J_D_1KI": 28.19427329009846}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.output
index 5869b7e..4c48fa2 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.6673474311828613}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.6507058143615723}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     248,     507,  ..., 1249488,
-                            1249771, 1250000]),
-       col_indices=tensor([   0,   22,   35,  ..., 4958, 4983, 4999]),
-       values=tensor([0.4233, 0.1325, 0.2059,  ..., 0.9744, 0.8399, 0.1366]),
+tensor(crow_indices=tensor([      0,     234,     496,  ..., 1249497,
+                            1249774, 1250000]),
+       col_indices=tensor([  44,   54,   71,  ..., 4912, 4980, 4999]),
+       values=tensor([0.9715, 0.0417, 0.5412,  ..., 0.8388, 0.4566, 0.9787]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.6304, 0.5951, 0.1863,  ..., 0.0552, 0.3796, 0.7701])
+tensor([0.5381, 0.6562, 0.7136,  ..., 0.2755, 0.3235, 0.3236])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 2.6673474311828613 seconds
+Time: 2.6507058143615723 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 393 -ss 5000 -sd 0.05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.605815887451172}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 396 -ss 5000 -sd 0.05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.44465947151184}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     284,     548,  ..., 1249494,
-                            1249762, 1250000]),
-       col_indices=tensor([   9,   27,   28,  ..., 4894, 4914, 4954]),
-       values=tensor([0.8223, 0.3728, 0.3102,  ..., 0.8633, 0.4361, 0.2072]),
+tensor(crow_indices=tensor([      0,     238,     499,  ..., 1249538,
+                            1249780, 1250000]),
+       col_indices=tensor([  54,   59,  126,  ..., 4967, 4989, 4999]),
+       values=tensor([0.2943, 0.9680, 0.2448,  ..., 0.1328, 0.5177, 0.9242]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.4176, 0.5149, 0.4165,  ..., 0.2240, 0.9505, 0.5242])
+tensor([0.0840, 0.5266, 0.2925,  ..., 0.7387, 0.5450, 0.9821])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.605815887451172 seconds
+Time: 10.44465947151184 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     284,     548,  ..., 1249494,
-                            1249762, 1250000]),
-       col_indices=tensor([   9,   27,   28,  ..., 4894, 4914, 4954]),
-       values=tensor([0.8223, 0.3728, 0.3102,  ..., 0.8633, 0.4361, 0.2072]),
+tensor(crow_indices=tensor([      0,     238,     499,  ..., 1249538,
+                            1249780, 1250000]),
+       col_indices=tensor([  54,   59,  126,  ..., 4967, 4989, 4999]),
+       values=tensor([0.2943, 0.9680, 0.2448,  ..., 0.1328, 0.5177, 0.9242]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.4176, 0.5149, 0.4165,  ..., 0.2240, 0.9505, 0.5242])
+tensor([0.0840, 0.5266, 0.2925,  ..., 0.7387, 0.5450, 0.9821])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.605815887451172 seconds
+Time: 10.44465947151184 seconds
 
-[20.32, 20.32, 20.44, 20.4, 20.52, 20.52, 20.64, 20.8, 20.92, 20.88]
-[20.92, 21.04, 24.24, 26.28, 27.72, 27.72, 28.36, 29.08, 25.2, 24.24, 24.28, 24.28, 24.28, 24.2]
-14.59074091911316
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 393, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.605815887451172, 'TIME_S_1KI': 26.986808873921557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 343.57593024253845, 'W': 23.547531420592268}
-[20.32, 20.32, 20.44, 20.4, 20.52, 20.52, 20.64, 20.8, 20.92, 20.88, 20.24, 20.28, 20.52, 20.64, 20.6, 20.56, 20.52, 20.48, 20.48, 20.56]
-369.64
-18.482
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 393, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.605815887451172, 'TIME_S_1KI': 26.986808873921557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 343.57593024253845, 'W': 23.547531420592268, 'J_1KI': 874.239008250734, 'W_1KI': 59.917382749598644, 'W_D': 5.065531420592269, 'J_D': 73.90985657548903, 'W_D_1KI': 12.889392927715695, 'J_D_1KI': 32.79743747510355}
+[20.48, 20.32, 20.16, 20.24, 20.24, 20.24, 20.32, 20.24, 20.52, 20.44]
+[20.44, 20.68, 20.56, 23.84, 25.48, 27.28, 28.2, 28.52, 24.88, 24.16, 24.0, 24.24, 24.32, 24.08, 24.28]
+15.610468626022339
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 396, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.44465947151184, 'TIME_S_1KI': 26.37540270583798, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.4221782398224, 'W': 22.896313160260082}
+[20.48, 20.32, 20.16, 20.24, 20.24, 20.24, 20.32, 20.24, 20.52, 20.44, 20.72, 20.64, 20.96, 20.92, 20.92, 20.88, 20.92, 20.68, 20.32, 20.32]
+369.5
+18.475
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 396, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.44465947151184, 'TIME_S_1KI': 26.37540270583798, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 357.4221782398224, 'W': 22.896313160260082, 'J_1KI': 902.5812581813697, 'W_1KI': 57.818972626919404, 'W_D': 4.42131316026008, 'J_D': 69.01877037405966, 'W_D_1KI': 11.16493222287899, 'J_D_1KI': 28.19427329009846}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.json
index 5939696..380f9a2 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 194, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.912250995635986, "TIME_S_1KI": 56.248716472350445, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 326.6964877033234, "W": 22.305376949262342, "J_1KI": 1684.0025139346567, "W_1KI": 114.97616984155846, "W_D": 3.7133769492623436, "J_D": 54.38810604286199, "W_D_1KI": 19.14111829516672, "J_D_1KI": 98.66555822250888}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 194, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.317471265792847, "TIME_S_1KI": 53.18284157625178, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 318.17810523986816, "W": 21.768206003636212, "J_1KI": 1640.0933259787018, "W_1KI": 112.20724744142377, "W_D": 3.159206003636214, "J_D": 46.176987673282646, "W_D_1KI": 16.28456702905265, "J_D_1KI": 83.9410671600652}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.output
index 469c726..54e21a8 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.1 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.392450332641602}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.392406225204468}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     508,    1024,  ..., 2499064,
-                            2499534, 2500000]),
-       col_indices=tensor([   1,    4,   10,  ..., 4973, 4986, 4993]),
-       values=tensor([0.4448, 0.2935, 0.6096,  ..., 0.6772, 0.8304, 0.1969]),
+tensor(crow_indices=tensor([      0,     509,    1034,  ..., 2499007,
+                            2499478, 2500000]),
+       col_indices=tensor([   3,   23,   30,  ..., 4972, 4977, 4989]),
+       values=tensor([0.8700, 0.9761, 0.7748,  ..., 0.3262, 0.4651, 0.0904]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5801, 0.6662, 0.3258,  ..., 0.9572, 0.7518, 0.3845])
+tensor([0.3291, 0.7750, 0.8899,  ..., 0.8293, 0.9829, 0.7202])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 5.392450332641602 seconds
+Time: 5.392406225204468 seconds
 
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 194 -ss 5000 -sd 0.1 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.912250995635986}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.317471265792847}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     510,    1021,  ..., 2499033,
-                            2499527, 2500000]),
-       col_indices=tensor([  27,   33,   84,  ..., 4958, 4963, 4982]),
-       values=tensor([0.5404, 0.4129, 0.3312,  ..., 0.4218, 0.5770, 0.4495]),
+tensor(crow_indices=tensor([      0,     506,     991,  ..., 2499015,
+                            2499494, 2500000]),
+       col_indices=tensor([  21,   27,   28,  ..., 4979, 4982, 4992]),
+       values=tensor([0.7502, 0.0415, 0.3266,  ..., 0.5968, 0.4348, 0.9268]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8518, 0.9058, 0.3829,  ..., 0.5160, 0.0011, 0.3108])
+tensor([0.0304, 0.6835, 0.7819,  ..., 0.2366, 0.8464, 0.6218])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.912250995635986 seconds
+Time: 10.317471265792847 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     510,    1021,  ..., 2499033,
-                            2499527, 2500000]),
-       col_indices=tensor([  27,   33,   84,  ..., 4958, 4963, 4982]),
-       values=tensor([0.5404, 0.4129, 0.3312,  ..., 0.4218, 0.5770, 0.4495]),
+tensor(crow_indices=tensor([      0,     506,     991,  ..., 2499015,
+                            2499494, 2500000]),
+       col_indices=tensor([  21,   27,   28,  ..., 4979, 4982, 4992]),
+       values=tensor([0.7502, 0.0415, 0.3266,  ..., 0.5968, 0.4348, 0.9268]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8518, 0.9058, 0.3829,  ..., 0.5160, 0.0011, 0.3108])
+tensor([0.0304, 0.6835, 0.7819,  ..., 0.2366, 0.8464, 0.6218])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.912250995635986 seconds
+Time: 10.317471265792847 seconds
 
-[20.28, 20.36, 20.6, 20.76, 20.96, 20.8, 20.76, 20.64, 20.48, 20.76]
-[20.88, 20.88, 20.64, 21.88, 22.92, 24.88, 26.08, 26.2, 25.72, 24.96, 24.48, 24.56, 24.76, 24.68]
-14.646535158157349
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 194, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.912250995635986, 'TIME_S_1KI': 56.248716472350445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.6964877033234, 'W': 22.305376949262342}
-[20.28, 20.36, 20.6, 20.76, 20.96, 20.8, 20.76, 20.64, 20.48, 20.76, 20.68, 20.08, 20.2, 20.52, 20.52, 20.72, 20.72, 21.32, 21.0, 21.08]
-371.84
-18.592
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 194, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.912250995635986, 'TIME_S_1KI': 56.248716472350445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.6964877033234, 'W': 22.305376949262342, 'J_1KI': 1684.0025139346567, 'W_1KI': 114.97616984155846, 'W_D': 3.7133769492623436, 'J_D': 54.38810604286199, 'W_D_1KI': 19.14111829516672, 'J_D_1KI': 98.66555822250888}
+[20.36, 20.52, 20.68, 20.76, 20.68, 21.04, 20.72, 20.52, 20.48, 20.4]
+[20.4, 20.16, 20.4, 21.2, 22.4, 24.12, 25.16, 25.48, 25.2, 24.44, 24.16, 24.0, 24.28, 24.16]
+14.61664342880249
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 194, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.317471265792847, 'TIME_S_1KI': 53.18284157625178, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.17810523986816, 'W': 21.768206003636212}
+[20.36, 20.52, 20.68, 20.76, 20.68, 21.04, 20.72, 20.52, 20.48, 20.4, 20.72, 20.68, 20.72, 20.56, 20.88, 20.88, 20.72, 20.64, 20.68, 20.56]
+372.17999999999995
+18.608999999999998
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 194, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.317471265792847, 'TIME_S_1KI': 53.18284157625178, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 318.17810523986816, 'W': 21.768206003636212, 'J_1KI': 1640.0933259787018, 'W_1KI': 112.20724744142377, 'W_D': 3.159206003636214, 'J_D': 46.176987673282646, 'W_D_1KI': 16.28456702905265, 'J_D_1KI': 83.9410671600652}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.2.json
index 8d5c96a..57b525e 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.2.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.2.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.527036905288696, "TIME_S_1KI": 105.27036905288696, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 363.19735393524167, "W": 23.113061201495835, "J_1KI": 3631.973539352417, "W_1KI": 231.13061201495836, "W_D": 4.666061201495836, "J_D": 73.32222533869742, "W_D_1KI": 46.66061201495836, "J_D_1KI": 466.6061201495836}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.55970311164856, "TIME_S_1KI": 105.5970311164856, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 356.9031545257568, "W": 22.746465682058464, "J_1KI": 3569.031545257568, "W_1KI": 227.46465682058465, "W_D": 4.400465682058467, "J_D": 69.04545546817783, "W_D_1KI": 44.00465682058467, "J_D_1KI": 440.0465682058467}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.2.output
index 9300b4b..a8ba021 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.2.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.2 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.527036905288696}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.55970311164856}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     996,    2006,  ..., 4997968,
-                            4998974, 5000000]),
-       col_indices=tensor([   4,    8,   12,  ..., 4976, 4983, 4993]),
-       values=tensor([0.4991, 0.7024, 0.1537,  ..., 0.4726, 0.2476, 0.0939]),
+tensor(crow_indices=tensor([      0,    1027,    2051,  ..., 4998026,
+                            4998991, 5000000]),
+       col_indices=tensor([   1,    3,    7,  ..., 4966, 4991, 4998]),
+       values=tensor([0.9616, 0.5316, 0.4974,  ..., 0.9954, 0.9065, 0.6509]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1571, 0.7792, 0.7385,  ..., 0.2151, 0.4821, 0.5033])
+tensor([0.4589, 0.4640, 0.2413,  ..., 0.6458, 0.9198, 0.8507])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.527036905288696 seconds
+Time: 10.55970311164856 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     996,    2006,  ..., 4997968,
-                            4998974, 5000000]),
-       col_indices=tensor([   4,    8,   12,  ..., 4976, 4983, 4993]),
-       values=tensor([0.4991, 0.7024, 0.1537,  ..., 0.4726, 0.2476, 0.0939]),
+tensor(crow_indices=tensor([      0,    1027,    2051,  ..., 4998026,
+                            4998991, 5000000]),
+       col_indices=tensor([   1,    3,    7,  ..., 4966, 4991, 4998]),
+       values=tensor([0.9616, 0.5316, 0.4974,  ..., 0.9954, 0.9065, 0.6509]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1571, 0.7792, 0.7385,  ..., 0.2151, 0.4821, 0.5033])
+tensor([0.4589, 0.4640, 0.2413,  ..., 0.6458, 0.9198, 0.8507])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +33,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.527036905288696 seconds
+Time: 10.55970311164856 seconds
 
-[20.48, 20.44, 20.48, 20.64, 20.72, 20.72, 20.68, 20.72, 20.68, 20.6]
-[20.4, 20.48, 21.0, 23.96, 25.48, 27.4, 28.52, 27.56, 26.24, 25.04, 24.48, 24.4, 24.4, 24.32, 24.48]
-15.71394419670105
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.527036905288696, 'TIME_S_1KI': 105.27036905288696, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 363.19735393524167, 'W': 23.113061201495835}
-[20.48, 20.44, 20.48, 20.64, 20.72, 20.72, 20.68, 20.72, 20.68, 20.6, 20.4, 20.16, 20.32, 20.32, 20.16, 20.56, 20.48, 20.52, 20.48, 20.24]
-368.94
-18.447
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.527036905288696, 'TIME_S_1KI': 105.27036905288696, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 363.19735393524167, 'W': 23.113061201495835, 'J_1KI': 3631.973539352417, 'W_1KI': 231.13061201495836, 'W_D': 4.666061201495836, 'J_D': 73.32222533869742, 'W_D_1KI': 46.66061201495836, 'J_D_1KI': 466.6061201495836}
+[20.36, 20.6, 20.68, 20.68, 20.48, 20.68, 20.64, 20.64, 20.68, 20.88]
+[20.72, 20.56, 21.08, 21.8, 23.92, 25.72, 27.04, 27.04, 26.84, 25.16, 24.48, 24.6, 24.68, 24.48, 24.48]
+15.690488338470459
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.55970311164856, 'TIME_S_1KI': 105.5970311164856, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 356.9031545257568, 'W': 22.746465682058464}
+[20.36, 20.6, 20.68, 20.68, 20.48, 20.68, 20.64, 20.64, 20.68, 20.88, 19.88, 19.88, 20.04, 20.24, 20.28, 20.32, 20.24, 20.12, 20.0, 20.32]
+366.91999999999996
+18.345999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.55970311164856, 'TIME_S_1KI': 105.5970311164856, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 356.9031545257568, 'W': 22.746465682058464, 'J_1KI': 3569.031545257568, 'W_1KI': 227.46465682058465, 'W_D': 4.400465682058467, 'J_D': 69.04545546817783, 'W_D_1KI': 44.00465682058467, 'J_D_1KI': 440.0465682058467}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.3.json
index fef5f93..d155ffd 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.3.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.3.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 15.927062749862671, "TIME_S_1KI": 159.2706274986267, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 517.2976983642578, "W": 23.521442010494468, "J_1KI": 5172.9769836425785, "W_1KI": 235.21442010494468, "W_D": 5.048442010494465, "J_D": 111.02837280082699, "W_D_1KI": 50.484420104944654, "J_D_1KI": 504.8442010494465}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 15.782387495040894, "TIME_S_1KI": 157.82387495040894, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 523.4366407585144, "W": 23.79272774089768, "J_1KI": 5234.366407585144, "W_1KI": 237.92727740897678, "W_D": 5.223727740897679, "J_D": 114.92127051210406, "W_D_1KI": 52.237277408976794, "J_D_1KI": 522.3727740897679}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.3.output
index eaea98f..2dde6be 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.3.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.3 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 15.927062749862671}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 15.782387495040894}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1527,    3024,  ..., 7496971,
-                            7498460, 7500000]),
-       col_indices=tensor([   0,    3,    4,  ..., 4985, 4992, 4996]),
-       values=tensor([0.7552, 0.2419, 0.2481,  ..., 0.7383, 0.7786, 0.4470]),
+tensor(crow_indices=tensor([      0,    1434,    2918,  ..., 7496956,
+                            7498470, 7500000]),
+       col_indices=tensor([   3,    4,    5,  ..., 4995, 4996, 4997]),
+       values=tensor([0.9895, 0.4913, 0.2478,  ..., 0.3299, 0.7256, 0.9586]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.6238, 0.3406, 0.3665,  ..., 0.0040, 0.2464, 0.8126])
+tensor([0.9076, 0.3308, 0.0614,  ..., 0.4765, 0.1763, 0.9057])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 15.927062749862671 seconds
+Time: 15.782387495040894 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1527,    3024,  ..., 7496971,
-                            7498460, 7500000]),
-       col_indices=tensor([   0,    3,    4,  ..., 4985, 4992, 4996]),
-       values=tensor([0.7552, 0.2419, 0.2481,  ..., 0.7383, 0.7786, 0.4470]),
+tensor(crow_indices=tensor([      0,    1434,    2918,  ..., 7496956,
+                            7498470, 7500000]),
+       col_indices=tensor([   3,    4,    5,  ..., 4995, 4996, 4997]),
+       values=tensor([0.9895, 0.4913, 0.2478,  ..., 0.3299, 0.7256, 0.9586]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.6238, 0.3406, 0.3665,  ..., 0.0040, 0.2464, 0.8126])
+tensor([0.9076, 0.3308, 0.0614,  ..., 0.4765, 0.1763, 0.9057])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +33,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 15.927062749862671 seconds
+Time: 15.782387495040894 seconds
 
-[20.4, 20.32, 20.32, 20.4, 20.48, 20.52, 20.8, 20.76, 20.76, 20.64]
-[20.8, 20.8, 21.12, 22.88, 23.72, 26.16, 27.92, 27.96, 27.28, 26.72, 25.24, 24.32, 24.24, 24.16, 24.2, 24.68, 24.8, 24.68, 24.68, 24.56, 24.64]
-21.99260139465332
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 15.927062749862671, 'TIME_S_1KI': 159.2706274986267, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 517.2976983642578, 'W': 23.521442010494468}
-[20.4, 20.32, 20.32, 20.4, 20.48, 20.52, 20.8, 20.76, 20.76, 20.64, 20.56, 20.16, 20.24, 20.6, 20.64, 20.76, 20.8, 20.6, 20.28, 20.44]
-369.46000000000004
-18.473000000000003
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 15.927062749862671, 'TIME_S_1KI': 159.2706274986267, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 517.2976983642578, 'W': 23.521442010494468, 'J_1KI': 5172.9769836425785, 'W_1KI': 235.21442010494468, 'W_D': 5.048442010494465, 'J_D': 111.02837280082699, 'W_D_1KI': 50.484420104944654, 'J_D_1KI': 504.8442010494465}
+[20.32, 20.2, 20.28, 20.4, 20.64, 20.72, 20.76, 20.76, 20.72, 20.4]
+[20.28, 20.4, 20.6, 25.12, 25.92, 28.28, 30.16, 28.08, 26.96, 26.28, 25.28, 24.36, 24.68, 24.68, 24.72, 24.72, 24.72, 24.48, 24.2, 24.08, 23.72]
+21.999858379364014
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 15.782387495040894, 'TIME_S_1KI': 157.82387495040894, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 523.4366407585144, 'W': 23.79272774089768}
+[20.32, 20.2, 20.28, 20.4, 20.64, 20.72, 20.76, 20.76, 20.72, 20.4, 20.48, 20.8, 20.56, 20.64, 20.56, 20.48, 20.76, 20.92, 21.08, 21.0]
+371.38
+18.569
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 15.782387495040894, 'TIME_S_1KI': 157.82387495040894, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 523.4366407585144, 'W': 23.79272774089768, 'J_1KI': 5234.366407585144, 'W_1KI': 237.92727740897678, 'W_D': 5.223727740897679, 'J_D': 114.92127051210406, 'W_D_1KI': 52.237277408976794, 'J_D_1KI': 522.3727740897679}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.4.json
index d2af780..715ce05 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.4.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.4.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 22.84249472618103, "TIME_S_1KI": 228.4249472618103, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 682.8522075366976, "W": 24.128078827131084, "J_1KI": 6828.522075366976, "W_1KI": 241.28078827131083, "W_D": 3.4220788271310845, "J_D": 96.84874200773261, "W_D_1KI": 34.220788271310845, "J_D_1KI": 342.2078827131084}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.19744086265564, "TIME_S_1KI": 211.9744086265564, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 653.3600071525573, "W": 23.102443027250636, "J_1KI": 6533.600071525573, "W_1KI": 231.02443027250635, "W_D": 3.838443027250637, "J_D": 108.5549766654967, "W_D_1KI": 38.38443027250637, "J_D_1KI": 383.8443027250637}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.4.output
index c75f057..a6eda71 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.4.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.4.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.4 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 22.84249472618103}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.19744086265564}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2006,     4067,  ...,  9995955,
-                             9997971, 10000000]),
-       col_indices=tensor([   1,    3,    4,  ..., 4995, 4998, 4999]),
-       values=tensor([0.5438, 0.4529, 0.4674,  ..., 0.4313, 0.1734, 0.8643]),
+tensor(crow_indices=tensor([       0,     1987,     3990,  ...,  9995921,
+                             9997979, 10000000]),
+       col_indices=tensor([   1,    3,    7,  ..., 4994, 4996, 4998]),
+       values=tensor([0.8352, 0.5081, 0.7013,  ..., 0.4620, 0.3531, 0.9818]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4316, 0.5719, 0.8319,  ..., 0.7407, 0.2442, 0.5797])
+tensor([0.6761, 0.9286, 0.5768,  ..., 0.1789, 0.3308, 0.7051])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 22.84249472618103 seconds
+Time: 21.19744086265564 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2006,     4067,  ...,  9995955,
-                             9997971, 10000000]),
-       col_indices=tensor([   1,    3,    4,  ..., 4995, 4998, 4999]),
-       values=tensor([0.5438, 0.4529, 0.4674,  ..., 0.4313, 0.1734, 0.8643]),
+tensor(crow_indices=tensor([       0,     1987,     3990,  ...,  9995921,
+                             9997979, 10000000]),
+       col_indices=tensor([   1,    3,    7,  ..., 4994, 4996, 4998]),
+       values=tensor([0.8352, 0.5081, 0.7013,  ..., 0.4620, 0.3531, 0.9818]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4316, 0.5719, 0.8319,  ..., 0.7407, 0.2442, 0.5797])
+tensor([0.6761, 0.9286, 0.5768,  ..., 0.1789, 0.3308, 0.7051])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +33,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 22.84249472618103 seconds
+Time: 21.19744086265564 seconds
 
-[26.88, 26.16, 25.48, 25.52, 24.68, 24.6, 24.36, 24.16, 24.16, 24.28]
-[24.4, 24.52, 24.52, 28.04, 29.2, 30.8, 31.68, 30.2, 29.88, 28.12, 26.84, 25.72, 24.28, 24.16, 24.04, 24.12, 24.24, 24.24, 24.4, 24.44, 24.48, 24.52, 24.68, 24.48, 24.68, 24.6, 24.68]
-28.301142930984497
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 22.84249472618103, 'TIME_S_1KI': 228.4249472618103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 682.8522075366976, 'W': 24.128078827131084}
-[26.88, 26.16, 25.48, 25.52, 24.68, 24.6, 24.36, 24.16, 24.16, 24.28, 20.44, 20.64, 20.68, 20.8, 20.92, 21.32, 21.36, 21.44, 21.44, 21.2]
-414.12
-20.706
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 22.84249472618103, 'TIME_S_1KI': 228.4249472618103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 682.8522075366976, 'W': 24.128078827131084, 'J_1KI': 6828.522075366976, 'W_1KI': 241.28078827131083, 'W_D': 3.4220788271310845, 'J_D': 96.84874200773261, 'W_D_1KI': 34.220788271310845, 'J_D_1KI': 342.2078827131084}
+[20.56, 20.56, 20.56, 20.76, 20.88, 20.68, 20.72, 20.44, 20.48, 20.36]
+[20.36, 20.44, 21.72, 23.12, 23.12, 25.08, 27.4, 28.88, 28.4, 27.2, 26.4, 25.64, 25.4, 25.28, 25.32, 25.2, 24.88, 24.64, 24.64, 24.72, 24.68, 24.56, 24.36, 24.48, 24.52, 24.2, 24.2]
+28.280992031097412
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.19744086265564, 'TIME_S_1KI': 211.9744086265564, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 653.3600071525573, 'W': 23.102443027250636}
+[20.56, 20.56, 20.56, 20.76, 20.88, 20.68, 20.72, 20.44, 20.48, 20.36, 21.88, 21.88, 21.84, 22.16, 22.48, 22.56, 22.44, 22.44, 22.08, 21.84]
+385.28
+19.264
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.19744086265564, 'TIME_S_1KI': 211.9744086265564, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 653.3600071525573, 'W': 23.102443027250636, 'J_1KI': 6533.600071525573, 'W_1KI': 231.02443027250635, 'W_D': 3.838443027250637, 'J_D': 108.5549766654967, 'W_D_1KI': 38.38443027250637, 'J_D_1KI': 383.8443027250637}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.5.json
index 1c5a213..425830a 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.5.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.5.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.49390745162964, "TIME_S_1KI": 264.9390745162964, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 833.4256306743622, "W": 24.07917164557845, "J_1KI": 8334.256306743622, "W_1KI": 240.79171645578452, "W_D": 5.603171645578453, "J_D": 193.9363584108353, "W_D_1KI": 56.031716455784526, "J_D_1KI": 560.3171645578453}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.608420610427856, "TIME_S_1KI": 266.08420610427856, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 808.2595381355286, "W": 23.359669488514182, "J_1KI": 8082.595381355286, "W_1KI": 233.5966948851418, "W_D": 4.68066948851418, "J_D": 161.95416466879843, "W_D_1KI": 46.8066948851418, "J_D_1KI": 468.06694885141803}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.5.output
index 99ea343..ac88e94 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.5.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_0.5.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.5 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.49390745162964}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.608420610427856}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2475,     4991,  ..., 12495070,
-                            12497536, 12500000]),
-       col_indices=tensor([   3,    6,    7,  ..., 4992, 4996, 4999]),
-       values=tensor([0.7861, 0.1444, 0.2009,  ..., 0.5207, 0.8919, 0.5019]),
+tensor(crow_indices=tensor([       0,     2453,     4908,  ..., 12494971,
+                            12497427, 12500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4996, 4997, 4999]),
+       values=tensor([0.5398, 0.0111, 0.2025,  ..., 0.8909, 0.4439, 0.2288]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.4801, 0.0235, 0.0420,  ..., 0.5930, 0.2408, 0.0610])
+tensor([0.7554, 0.3335, 0.4109,  ..., 0.1756, 0.4233, 0.6660])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 26.49390745162964 seconds
+Time: 26.608420610427856 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2475,     4991,  ..., 12495070,
-                            12497536, 12500000]),
-       col_indices=tensor([   3,    6,    7,  ..., 4992, 4996, 4999]),
-       values=tensor([0.7861, 0.1444, 0.2009,  ..., 0.5207, 0.8919, 0.5019]),
+tensor(crow_indices=tensor([       0,     2453,     4908,  ..., 12494971,
+                            12497427, 12500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4996, 4997, 4999]),
+       values=tensor([0.5398, 0.0111, 0.2025,  ..., 0.8909, 0.4439, 0.2288]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.4801, 0.0235, 0.0420,  ..., 0.5930, 0.2408, 0.0610])
+tensor([0.7554, 0.3335, 0.4109,  ..., 0.1756, 0.4233, 0.6660])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +33,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 26.49390745162964 seconds
+Time: 26.608420610427856 seconds
 
-[20.72, 20.56, 20.56, 20.36, 20.36, 20.36, 20.28, 20.56, 20.72, 21.08]
-[21.48, 21.52, 24.72, 26.76, 28.04, 29.8, 31.48, 31.48, 29.76, 28.44, 27.2, 25.96, 25.12, 24.52, 24.64, 24.64, 24.72, 24.52, 24.56, 24.68, 24.68, 24.6, 24.52, 24.2, 24.32, 24.24, 24.16, 24.4, 24.44, 24.44, 24.44, 24.6, 24.36]
-34.611889600753784
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.49390745162964, 'TIME_S_1KI': 264.9390745162964, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 833.4256306743622, 'W': 24.07917164557845}
-[20.72, 20.56, 20.56, 20.36, 20.36, 20.36, 20.28, 20.56, 20.72, 21.08, 20.64, 20.64, 20.36, 20.36, 20.56, 20.64, 20.68, 20.56, 20.56, 20.36]
-369.52
-18.476
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.49390745162964, 'TIME_S_1KI': 264.9390745162964, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 833.4256306743622, 'W': 24.07917164557845, 'J_1KI': 8334.256306743622, 'W_1KI': 240.79171645578452, 'W_D': 5.603171645578453, 'J_D': 193.9363584108353, 'W_D_1KI': 56.031716455784526, 'J_D_1KI': 560.3171645578453}
+[20.48, 20.76, 20.88, 20.88, 21.04, 20.8, 20.8, 20.8, 20.68, 20.52]
+[20.68, 20.72, 21.2, 23.28, 25.28, 26.92, 28.84, 29.56, 28.68, 27.68, 26.12, 25.32, 24.6, 24.56, 24.48, 24.4, 24.4, 24.48, 24.6, 24.64, 24.76, 24.72, 24.48, 24.2, 24.08, 24.0, 24.16, 24.2, 24.08, 24.4, 24.24, 24.16, 24.24]
+34.60064101219177
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.608420610427856, 'TIME_S_1KI': 266.08420610427856, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 808.2595381355286, 'W': 23.359669488514182}
+[20.48, 20.76, 20.88, 20.88, 21.04, 20.8, 20.8, 20.8, 20.68, 20.52, 21.08, 21.0, 20.68, 20.72, 20.72, 20.64, 20.72, 20.6, 20.52, 20.6]
+373.58000000000004
+18.679000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.608420610427856, 'TIME_S_1KI': 266.08420610427856, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 808.2595381355286, 'W': 23.359669488514182, 'J_1KI': 8082.595381355286, 'W_1KI': 233.5966948851418, 'W_D': 4.68066948851418, 'J_D': 161.95416466879843, 'W_D_1KI': 46.8066948851418, 'J_D_1KI': 468.06694885141803}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.json
index 732c4d4..db06094 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 289937, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.948570489883423, "TIME_S_1KI": 0.037761894790535266, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 337.0917576313019, "W": 22.992700250818316, "J_1KI": 1.1626379442130597, "W_1KI": 0.07930240104166876, "W_D": 4.582700250818316, "J_D": 67.18612713575365, "W_D_1KI": 0.015805848342289243, "J_D_1KI": 5.4514768181671336e-05}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 290608, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.898369789123535, "TIME_S_1KI": 0.037501960679415344, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 329.4187112140655, "W": 22.471646126593484, "J_1KI": 1.1335500440939874, "W_1KI": 0.07732631629753305, "W_D": 4.110646126593483, "J_D": 60.259214730024304, "W_D_1KI": 0.014144986120800127, "J_D_1KI": 4.867376713923955e-05}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.output
index 90b44fb..8746e29 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,75 +1,102 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.012730121612548828}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.01248311996459961}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([ 601, 2647,  820, 2703, 4832, 2905, 4036,  996, 2456,
-                           2955, 1267, 3283, 2251, 4368, 2032, 1143, 1874, 4481,
-                           3312,  337, 3271, 2673, 2707,  912, 1355,  576, 4171,
-                           1338, 4509, 2894, 3669,  436,  629, 3354, 3800, 1938,
-                           3841, 3356, 3452, 2739,  639, 4726, 1185, 2215, 4780,
-                           2365,  813, 3529, 3133, 2027, 1430, 1360, 1560, 4504,
-                           4891,  990,  715, 1174, 4133, 1335, 2115, 2803,  644,
-                           1222,  359, 4157, 1393,  961, 2251, 4773,  836, 2710,
-                            702, 1699, 2540, 3245, 4452, 1916, 3004, 2947, 4627,
-                           2897, 3573, 4136, 3724, 2543, 4225, 4206, 1697, 2467,
-                           4746, 2725, 1632, 1936, 4003, 4667,  837, 3403, 2009,
-                           3106, 4241, 1632,  143, 2900, 2184, 2389,  134, 1255,
-                            455, 3988, 4547, 3113, 3784,  246, 1055, 1579, 2608,
-                           1441, 4808, 3117, 2320, 2723, 2732, 1022, 4658,  752,
-                           2092, 3461,  403, 1092, 2475, 1500, 4745, 1977, 2013,
-                           1043, 1615, 2577,  280, 4124, 4918, 3583, 3155, 4834,
-                           2410, 1454, 4710, 2436, 3776, 3032, 3163,  332, 2083,
-                           3800,  348,  742, 2269, 4249, 2249,  388, 3899, 1000,
-                           3276, 3790,  482, 1626, 3791,  386, 1380,  385,  518,
-                           3261, 3414, 3411, 1506,  372, 2674, 2798, 1667, 3725,
-                            190, 3734, 1581, 3514, 2910, 3386, 1246, 4529, 2936,
-                           3830, 2148, 4608, 2142,  767, 3320, 1467,  118,  736,
-                           1289, 3485,  482, 1713,  920,  523, 2549, 1394, 1179,
-                           1453, 3629,  477,  885, 4060, 1379, 4354, 1610, 3955,
-                           4389, 2465,  337,   97, 1261, 1276,  880, 2430, 2803,
-                           1291, 2721,  585, 2387, 4856,  993,  177, 4024, 1337,
-                           4378,  435,  408, 1205,    4, 2496, 4066,  296,  288,
-                           2154, 1297, 3984, 4892, 1675, 3223, 2466]),
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+       layout=torch.sparse_csr)
+tensor([0.6617, 0.9328, 0.8652,  ..., 0.8174, 0.3929, 0.3392])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -77,80 +104,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.012730121612548828 seconds
+Time: 0.01248311996459961 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 82481 -ss 5000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.9870200157165527}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 84113 -ss 5000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.191556692123413}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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+                      0.4822, 0.6524, 0.6384, 0.9953, 0.0509, 0.4987, 0.0171,
+                      0.4272, 0.5973, 0.2318, 0.8370, 0.4338, 0.3625, 0.3612,
+                      0.0107, 0.9389, 0.2647, 0.8948, 0.1513, 0.4188, 0.1808,
+                      0.2512, 0.8313, 0.9627, 0.2460, 0.6266, 0.5196, 0.6184,
+                      0.5321, 0.3380, 0.0267, 0.8089, 0.6366]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.1901, 0.2382, 0.6937,  ..., 0.7632, 0.8746, 0.1540])
+tensor([0.1216, 0.3140, 0.3620,  ..., 0.0196, 0.1616, 0.5885])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -158,80 +185,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 2.9870200157165527 seconds
+Time: 3.191556692123413 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 289937 -ss 5000 -sd 1e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.948570489883423}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 276725 -ss 5000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.998385429382324}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([4712, 4178, 2903,   24, 4753,   51, 2819, 4572, 4453,
-                           3780, 2899, 3226, 3780, 3989,  722, 4731, 1139, 1109,
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-                           1005, 4276, 2954, 4491, 2491, 2981,  645, 4461, 2128,
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-                            589, 3305, 4000, 4212,  790, 4927, 4076, 2238, 4107,
-                           3701, 3348, 1617, 1179, 3888, 4445, 2667, 3215, 4009,
-                           4710,  219, 2800,  233, 1521, 2319,  680, 1854, 4750,
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-                           3309, 3121, 4585, 2996, 3358,  502, 4286, 4572, 2864,
-                           1049,  469,  825, 1143,  635, 2773, 2543, 3425, 3473,
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-                           3941, 2845, 1360, 1763, 3589, 3716, 2365,  196, 1112,
-                            123, 2267, 4731,  228, 4673, 1590, 3794, 3816, 2846,
-                            863, 3759, 1182,  304, 2540,   66, 4385, 3694, 3525,
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-                            370,  101, 3253, 4145, 1994, 1358,  981, 2203, 2167,
-                           3742, 4696,  614, 2733,  396, 4399,  427, 1682, 4896,
-                           3429,  693,  870, 4939, 3305, 4250, 3680]),
-       values=tensor([0.3479, 0.5084, 0.6603, 0.8257, 0.8683, 0.9247, 0.0338,
-                      0.9486, 0.8504, 0.5745, 0.3925, 0.3196, 0.6449, 0.2119,
-                      0.0164, 0.7309, 0.7682, 0.1461, 0.7397, 0.9951, 0.7123,
-                      0.4571, 0.7549, 0.0282, 0.5968, 0.6667, 0.3749, 0.3789,
-                      0.4293, 0.3353, 0.3273, 0.0531, 0.5787, 0.8917, 0.4198,
-                      0.7695, 0.7895, 0.7926, 0.6654, 0.0192, 0.0703, 0.9096,
-                      0.9289, 0.6077, 0.6990, 0.6780, 0.1687, 0.0557, 0.0641,
-                      0.1726, 0.7968, 0.1192, 0.9982, 0.1104, 0.3778, 0.1311,
-                      0.0584, 0.9615, 0.6551, 0.7173, 0.4827, 0.9281, 0.2508,
-                      0.5901, 0.8616, 0.6261, 0.7668, 0.8880, 0.5680, 0.6476,
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-                      0.4951, 0.3638, 0.3016, 0.7272, 0.7832, 0.5085, 0.1101,
-                      0.2648, 0.6399, 0.4137, 0.5843, 0.7184]),
+       col_indices=tensor([2380, 3613, 1075, 3246, 4042, 3374, 1478, 2979, 2276,
+                           2930, 2086, 4202, 3737, 1516, 3862,  142, 4015, 1357,
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+                           3927, 4001, 4085, 2667, 3409, 1518, 1515, 3367, 3354,
+                           1687, 4445, 2699, 2676, 1307,  165, 1692]),
+       values=tensor([0.3654, 0.9688, 0.7037, 0.1671, 0.3181, 0.5966, 0.6794,
+                      0.3505, 0.6371, 0.3737, 0.9079, 0.6073, 0.9177, 0.5556,
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+                      0.8692, 0.3165, 0.9487, 0.9210, 0.0809, 0.9652, 0.2990,
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+                      0.4571, 0.5904, 0.1948, 0.0242, 0.2200, 0.7531, 0.9807,
+                      0.0340, 0.6445, 0.3538, 0.5381, 0.1875, 0.2701, 0.0340,
+                      0.1739, 0.4145, 0.0100, 0.7463, 0.7750, 0.8434, 0.0880,
+                      0.6662, 0.7618, 0.0356, 0.4252, 0.8131, 0.6804, 0.5433,
+                      0.5757, 0.4288, 0.6083, 0.2401, 0.7393, 0.1894, 0.8963,
+                      0.1437, 0.2457, 0.0570, 0.7376, 0.0470, 0.9104, 0.3679,
+                      0.1543, 0.8914, 0.3423, 0.3481, 0.6670, 0.5277, 0.9388,
+                      0.7501, 0.8032, 0.4265, 0.1515, 0.1156, 0.2383, 0.9250,
+                      0.6618, 0.8262, 0.6467, 0.7861, 0.5413, 0.4775, 0.3128,
+                      0.3404, 0.0688, 0.5764, 0.2346, 0.9806, 0.8393, 0.8113,
+                      0.0170, 0.3215, 0.3506, 0.2192, 0.6468, 0.7892, 0.6495,
+                      0.5652, 0.4589, 0.2573, 0.9179, 0.9147, 0.1216, 0.8679,
+                      0.8657, 0.0404, 0.8722, 0.6530, 0.6209]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.0011, 0.9357, 0.8539,  ..., 0.1995, 0.1479, 0.1616])
+tensor([0.6910, 0.0217, 0.5495,  ..., 0.8566, 0.3088, 0.4862])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -239,77 +266,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.948570489883423 seconds
+Time: 9.998385429382324 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 290608 -ss 5000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.898369789123535}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([4712, 4178, 2903,   24, 4753,   51, 2819, 4572, 4453,
-                           3780, 2899, 3226, 3780, 3989,  722, 4731, 1139, 1109,
-                           4512, 1669, 4522, 2228, 2733, 1441, 2781,  363, 3766,
-                           2188, 1770,  795, 1224, 1803, 4910, 1370, 4516, 2224,
-                           2678, 4365,  692, 1811, 1383, 2901,  749, 3344, 1016,
-                           4896, 4731,  857, 4171, 1998, 4569, 2011, 3832, 2691,
-                           1005, 4276, 2954, 4491, 2491, 2981,  645, 4461, 2128,
-                           3675, 4293, 1741, 3314, 1065, 1939, 1615, 3365, 3901,
-                            589, 3305, 4000, 4212,  790, 4927, 4076, 2238, 4107,
-                           3701, 3348, 1617, 1179, 3888, 4445, 2667, 3215, 4009,
-                           4710,  219, 2800,  233, 1521, 2319,  680, 1854, 4750,
-                           3077, 1721, 3819, 3579, 2334, 2886, 4510, 1278, 1666,
-                           4749, 4910, 1969, 2508,  532, 1736, 4315, 1491,  537,
-                           3309, 3121, 4585, 2996, 3358,  502, 4286, 4572, 2864,
-                           1049,  469,  825, 1143,  635, 2773, 2543, 3425, 3473,
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-                             96, 3029, 2790, 1834, 4863,  978, 4811, 3677, 2912,
-                           1938, 2797,  895, 1501, 2558, 1230,  534, 2633, 3017,
-                           4982, 4618, 4241, 2899, 2098, 2010, 1636, 2502, 2716,
-                           4980,  363,  466,   23, 1737, 1476, 1286, 4720,  833,
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-                           3941, 2845, 1360, 1763, 3589, 3716, 2365,  196, 1112,
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-                            863, 3759, 1182,  304, 2540,   66, 4385, 3694, 3525,
-                             31, 4315, 4266, 4089, 2728, 1405, 1294, 4022, 2222,
-                            370,  101, 3253, 4145, 1994, 1358,  981, 2203, 2167,
-                           3742, 4696,  614, 2733,  396, 4399,  427, 1682, 4896,
-                           3429,  693,  870, 4939, 3305, 4250, 3680]),
-       values=tensor([0.3479, 0.5084, 0.6603, 0.8257, 0.8683, 0.9247, 0.0338,
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-                      0.7695, 0.7895, 0.7926, 0.6654, 0.0192, 0.0703, 0.9096,
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-                      0.2648, 0.6399, 0.4137, 0.5843, 0.7184]),
+       col_indices=tensor([ 262, 2035,  485,  251, 4016, 1058, 3070, 4726, 3354,
+                           1939,  656, 3827, 2792, 2167, 1494, 4192, 4713, 3379,
+                           2457, 4009, 3893, 2667,  534, 1122,  570, 1229, 1463,
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+                      0.6790, 0.8139, 0.3896, 0.4613, 0.8865, 0.0387, 0.7094,
+                      0.4145, 0.9294, 0.9785, 0.5763, 0.3234, 0.5144, 0.3400,
+                      0.5427, 0.5328, 0.8371, 0.3451, 0.4750, 0.4464, 0.2861,
+                      0.3037, 0.0063, 0.7639, 0.0990, 0.0874, 0.0368, 0.1610,
+                      0.6825, 0.5116, 0.9108, 0.7259, 0.0932, 0.4235, 0.3752,
+                      0.9322, 0.3307, 0.7276, 0.7046, 0.2464, 0.5850, 0.2349,
+                      0.3961, 0.8183, 0.7883, 0.0990, 0.1263, 0.1382, 0.2157,
+                      0.1476, 0.2670, 0.7789, 0.2413, 0.2428, 0.7730, 0.2374,
+                      0.6779, 0.6296, 0.3705, 0.7172, 0.2121]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.0011, 0.9357, 0.8539,  ..., 0.1995, 0.1479, 0.1616])
+tensor([0.9230, 0.6870, 0.7737,  ..., 0.0152, 0.4575, 0.0315])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -317,13 +347,91 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.948570489883423 seconds
+Time: 10.898369789123535 seconds
 
-[20.16, 20.24, 20.28, 20.24, 20.08, 20.16, 20.2, 20.28, 20.28, 20.36]
-[20.72, 20.56, 20.44, 24.96, 27.52, 28.24, 29.2, 26.64, 25.12, 24.04, 23.96, 23.92, 24.04, 24.2]
-14.660816431045532
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 289937, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.948570489883423, 'TIME_S_1KI': 0.037761894790535266, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 337.0917576313019, 'W': 22.992700250818316}
-[20.16, 20.24, 20.28, 20.24, 20.08, 20.16, 20.2, 20.28, 20.28, 20.36, 20.4, 20.4, 20.44, 20.84, 20.84, 20.76, 20.64, 20.8, 20.84, 20.84]
-368.2
-18.41
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 289937, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.948570489883423, 'TIME_S_1KI': 0.037761894790535266, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 337.0917576313019, 'W': 22.992700250818316, 'J_1KI': 1.1626379442130597, 'W_1KI': 0.07930240104166876, 'W_D': 4.582700250818316, 'J_D': 67.18612713575365, 'W_D_1KI': 0.015805848342289243, 'J_D_1KI': 5.4514768181671336e-05}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([ 262, 2035,  485,  251, 4016, 1058, 3070, 4726, 3354,
+                           1939,  656, 3827, 2792, 2167, 1494, 4192, 4713, 3379,
+                           2457, 4009, 3893, 2667,  534, 1122,  570, 1229, 1463,
+                           1765, 2099, 1423, 4621,  340,  797, 1441,   64, 2918,
+                            554, 4698, 4070, 3140,  583, 2602, 1349, 1767,  934,
+                           4043, 2088, 3735, 4900,  920, 1738, 2586, 1766, 1119,
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+                            741, 4832,  644, 4777, 4297, 1070, 1878, 1639, 1265,
+                           2829, 4815, 1097,  123, 3210, 3906, 4032,  409, 1440,
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+                           3720, 3584, 1884,  464, 1313, 1150, 1993, 1991, 3764,
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+                           3352, 1118,  968, 2723,  119, 3558, 2137, 4256, 4030,
+                           4468,  967, 4265, 2344, 2803, 1802, 4304]),
+       values=tensor([0.0871, 0.3484, 0.2566, 0.9283, 0.1741, 0.9571, 0.8521,
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+                      0.5427, 0.5328, 0.8371, 0.3451, 0.4750, 0.4464, 0.2861,
+                      0.3037, 0.0063, 0.7639, 0.0990, 0.0874, 0.0368, 0.1610,
+                      0.6825, 0.5116, 0.9108, 0.7259, 0.0932, 0.4235, 0.3752,
+                      0.9322, 0.3307, 0.7276, 0.7046, 0.2464, 0.5850, 0.2349,
+                      0.3961, 0.8183, 0.7883, 0.0990, 0.1263, 0.1382, 0.2157,
+                      0.1476, 0.2670, 0.7789, 0.2413, 0.2428, 0.7730, 0.2374,
+                      0.6779, 0.6296, 0.3705, 0.7172, 0.2121]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.9230, 0.6870, 0.7737,  ..., 0.0152, 0.4575, 0.0315])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.898369789123535 seconds
+
+[20.8, 20.84, 20.72, 20.68, 20.44, 20.36, 20.4, 20.44, 20.64, 20.88]
+[20.88, 20.96, 20.84, 23.76, 25.48, 26.72, 27.4, 27.92, 24.28, 23.32, 23.56, 23.68, 23.56, 23.72]
+14.659304857254028
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 290608, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.898369789123535, 'TIME_S_1KI': 0.037501960679415344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.4187112140655, 'W': 22.471646126593484}
+[20.8, 20.84, 20.72, 20.68, 20.44, 20.36, 20.4, 20.44, 20.64, 20.88, 20.6, 20.44, 20.4, 20.4, 20.2, 20.2, 20.08, 20.0, 19.88, 19.92]
+367.22
+18.361
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 290608, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.898369789123535, 'TIME_S_1KI': 0.037501960679415344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 329.4187112140655, 'W': 22.471646126593484, 'J_1KI': 1.1335500440939874, 'W_1KI': 0.07732631629753305, 'W_D': 4.110646126593483, 'J_D': 60.259214730024304, 'W_D_1KI': 0.014144986120800127, 'J_D_1KI': 4.867376713923955e-05}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_5e-05.json
index 8ad2558..68fb904 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_5e-05.json
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 1, "ITERATIONS": 154350, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.289572715759277, "TIME_S_1KI": 0.0666638983852237, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 297.04585779190063, "W": 21.84801552939114, "J_1KI": 1.9244953533650835, "W_1KI": 0.14154852950690727, "W_D": 3.3270155293911436, "J_D": 45.23413947987558, "W_D_1KI": 0.0215550082888963, "J_D_1KI": 0.0001396501994745468}
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 149910, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.487581491470337, "TIME_S_1KI": 0.06995918545440823, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 308.13872550964356, "W": 22.64443234695719, "J_1KI": 2.0554914649432563, "W_1KI": 0.15105351442170092, "W_D": 4.297432346957187, "J_D": 58.478185986995705, "W_D_1KI": 0.02866674902913206, "J_D_1KI": 0.00019122639603183283}
diff --git a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_5e-05.output
index f253283..4ad6b06 100644
--- a/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_5e-05.output
+++ b/pytorch/output_synthetic_1core/altra_1_csr_10_10_10_synthetic_5000_5e-05.output
@@ -1,13 +1,51 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 5e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.014810323715209961}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.014495134353637695}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([2527, 1057, 2629,  ..., 4578,  222, 4522]),
+       values=tensor([0.8818, 0.3903, 0.1399,  ..., 0.1466, 0.3063, 0.0529]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.6522, 0.5583, 0.7059,  ..., 0.6700, 0.4586, 0.5971])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.014495134353637695 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 72438 -ss 5000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 5.073688983917236}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([2776, 3385, 4122,  ..., 2958, 1775, 4424]),
+       values=tensor([0.3033, 0.9980, 0.9637,  ..., 0.4776, 0.5824, 0.9810]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.2995, 0.4250, 0.8077,  ..., 0.7167, 0.6502, 0.4555])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 5.073688983917236 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 149910 -ss 5000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.487581491470337}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([4902, 4751,  573,  ..., 1409, 1871,  577]),
-       values=tensor([0.0874, 0.7756, 0.4965,  ..., 0.1251, 0.3364, 0.3476]),
+       col_indices=tensor([4220, 2132, 4487,  ..., 2448, 3646, 2184]),
+       values=tensor([0.6318, 0.3725, 0.8565,  ..., 0.5203, 0.4829, 0.8123]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.4221, 0.7918, 0.4416,  ..., 0.8475, 0.7362, 0.1103])
+tensor([0.5845, 0.5120, 0.6003,  ..., 0.5543, 0.5465, 0.9228])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 0.014810323715209961 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 70896 -ss 5000 -sd 5e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.822846412658691}
+Time: 10.487581491470337 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1250, 1250]),
-       col_indices=tensor([1594, 2931, 2652,  ..., 4428,  449, 1795]),
-       values=tensor([0.3058, 0.1710, 0.0965,  ..., 0.7799, 0.8373, 0.5140]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4220, 2132, 4487,  ..., 2448, 3646, 2184]),
+       values=tensor([0.6318, 0.3725, 0.8565,  ..., 0.5203, 0.4829, 0.8123]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.0899, 0.4612, 0.1283,  ..., 0.7452, 0.2953, 0.1670])
+tensor([0.5845, 0.5120, 0.6003,  ..., 0.5543, 0.5465, 0.9228])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,48 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 4.822846412658691 seconds
+Time: 10.487581491470337 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 154350 -ss 5000 -sd 5e-05 -c 1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.289572715759277}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1250, 1250]),
-       col_indices=tensor([ 621, 1968, 1113,  ..., 1968,  726, 3393]),
-       values=tensor([0.9316, 0.3440, 0.3874,  ..., 0.4845, 0.3520, 0.3225]),
-       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.4702, 0.8122, 0.0166,  ..., 0.1291, 0.0008, 0.5220])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 1250
-Density: 5e-05
-Time: 10.289572715759277 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1250, 1250]),
-       col_indices=tensor([ 621, 1968, 1113,  ..., 1968,  726, 3393]),
-       values=tensor([0.9316, 0.3440, 0.3874,  ..., 0.4845, 0.3520, 0.3225]),
-       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.4702, 0.8122, 0.0166,  ..., 0.1291, 0.0008, 0.5220])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 1250
-Density: 5e-05
-Time: 10.289572715759277 seconds
-
-[20.4, 20.44, 20.52, 20.68, 20.64, 20.48, 20.24, 20.36, 20.2, 20.16]
-[20.36, 20.44, 21.32, 23.28, 23.28, 24.96, 25.6, 26.08, 24.88, 23.92, 23.64, 23.6, 23.6]
-13.59601092338562
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 154350, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.289572715759277, 'TIME_S_1KI': 0.0666638983852237, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 297.04585779190063, 'W': 21.84801552939114}
-[20.4, 20.44, 20.52, 20.68, 20.64, 20.48, 20.24, 20.36, 20.2, 20.16, 20.52, 20.64, 20.96, 21.2, 21.24, 21.04, 20.76, 20.36, 20.08, 20.08]
-370.41999999999996
-18.520999999999997
-{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 154350, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.289572715759277, 'TIME_S_1KI': 0.0666638983852237, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 297.04585779190063, 'W': 21.84801552939114, 'J_1KI': 1.9244953533650835, 'W_1KI': 0.14154852950690727, 'W_D': 3.3270155293911436, 'J_D': 45.23413947987558, 'W_D_1KI': 0.0215550082888963, 'J_D_1KI': 0.0001396501994745468}
+[20.2, 20.44, 20.36, 20.28, 20.4, 20.4, 20.28, 20.28, 20.52, 20.8]
+[20.88, 20.88, 21.44, 25.44, 27.32, 28.04, 28.68, 25.96, 24.48, 23.4, 23.16, 23.2, 23.2]
+13.607703685760498
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 149910, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.487581491470337, 'TIME_S_1KI': 0.06995918545440823, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 308.13872550964356, 'W': 22.64443234695719}
+[20.2, 20.44, 20.36, 20.28, 20.4, 20.4, 20.28, 20.28, 20.52, 20.8, 20.4, 20.4, 20.48, 20.64, 20.52, 20.44, 20.4, 20.2, 20.16, 20.08]
+366.94000000000005
+18.347
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 149910, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.487581491470337, 'TIME_S_1KI': 0.06995918545440823, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 308.13872550964356, 'W': 22.64443234695719, 'J_1KI': 2.0554914649432563, 'W_1KI': 0.15105351442170092, 'W_D': 4.297432346957187, 'J_D': 58.478185986995705, 'W_D_1KI': 0.02866674902913206, 'J_D_1KI': 0.00019122639603183283}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..6e8d15a
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 362, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.458970785140991, "TIME_S_1KI": 28.892184489339755, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 843.6801159381868, "W": 64.68, "J_1KI": 2330.608055077864, "W_1KI": 178.6740331491713, "W_D": 29.0225, "J_D": 378.5668856650591, "W_D_1KI": 80.17265193370166, "J_D_1KI": 221.4714141814963}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..a4e45b0
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.8928701877593994}
+
+tensor(indices=tensor([[58791, 73131, 88126,  ...,  5351, 19697,  4548],
+                       [86236, 31656, 15988,  ..., 30767, 69117, 58161]]),
+       values=tensor([0.1204, 0.6176, 0.3154,  ..., 0.2086, 0.4659, 0.5503]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.7281, 0.2344, 0.6036,  ..., 0.9404, 0.0725, 0.5355])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 2.8928701877593994 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '362', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.458970785140991}
+
+tensor(indices=tensor([[ 2973, 76526, 18432,  ..., 12133, 71602, 59722],
+                       [76574, 54783, 34116,  ..., 41113, 64908, 71871]]),
+       values=tensor([0.0072, 0.9730, 0.8159,  ..., 0.7707, 0.6456, 0.2969]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.6834, 0.2829, 0.7273,  ..., 0.6780, 0.1842, 0.8230])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.458970785140991 seconds
+
+tensor(indices=tensor([[ 2973, 76526, 18432,  ..., 12133, 71602, 59722],
+                       [76574, 54783, 34116,  ..., 41113, 64908, 71871]]),
+       values=tensor([0.0072, 0.9730, 0.8159,  ..., 0.7707, 0.6456, 0.2969]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.6834, 0.2829, 0.7273,  ..., 0.6780, 0.1842, 0.8230])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.458970785140991 seconds
+
+[39.79, 39.54, 39.38, 38.96, 39.24, 38.89, 39.23, 38.89, 39.37, 45.8]
+[64.68]
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..203ccfb
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.001.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..0c536cd
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.001.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..91927fd
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.01.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..dee42a0
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_0.01.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..d09a424
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_1e-05.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..b265bb6
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_1e-05.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..deac0f8
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 725, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.478241443634033, "TIME_S_1KI": 14.452746818805561, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 850.4818459033967, "W": 64.84, "J_1KI": 1173.0784081426161, "W_1KI": 89.43448275862069, "W_D": 29.61025, "J_D": 388.38649101883175, "W_D_1KI": 40.84172413793104, "J_D_1KI": 56.333412604042806}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..dcc17d6
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,57 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '5e-05', '-c', '1']
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+                       [76031, 87894, 99380,  ..., 88344, 80962, 74135]]),
+       values=tensor([1.5908e-01, 7.3565e-01, 6.5693e-02,  ...,
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+tensor([0.6217, 0.2257, 0.6077,  ..., 0.4263, 0.5923, 0.1812])
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..9f03841
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.0001.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..f3ec4bc
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.0001.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..7b702e9
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.001.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..5aa9fc1
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.001.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..2d11147
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.01.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..69bffce
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.01.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..2c63f6c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.05.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..1de9227
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.05.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..a223447
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.1.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..a64905d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.1.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..670bc2f
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.2.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..a04fa89
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.2.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..61ee2ab
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.3.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..fc29758
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.3.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..a0e49dd
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.4.json
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+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 113.74189758300781, "TIME_S_1KI": 1137.4189758300781, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 8411.817435126306, "W": 68.18, "J_1KI": 84118.17435126306, "W_1KI": 681.8000000000001, "W_D": 32.47800000000001, "J_D": 4007.0256183342944, "W_D_1KI": 324.7800000000001, "J_D_1KI": 3247.8000000000006}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..18d4d04
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.4.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.4', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 113.74189758300781}
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+tensor(indices=tensor([[  98, 5717, 9207,  ..., 2831, 9478, 5788],
+                       [6304, 7772, 6308,  ..., 7248, 5124, 5480]]),
+       values=tensor([0.1634, 0.1665, 0.1616,  ..., 0.5220, 0.6003, 0.7750]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_coo)
+tensor([0.2208, 0.2901, 0.3618,  ..., 0.1796, 0.2169, 0.5409])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 113.74189758300781 seconds
+
+tensor(indices=tensor([[  98, 5717, 9207,  ..., 2831, 9478, 5788],
+                       [6304, 7772, 6308,  ..., 7248, 5124, 5480]]),
+       values=tensor([0.1634, 0.1665, 0.1616,  ..., 0.5220, 0.6003, 0.7750]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_coo)
+tensor([0.2208, 0.2901, 0.3618,  ..., 0.1796, 0.2169, 0.5409])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 113.74189758300781 seconds
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+[39.8, 39.22, 39.1, 39.56, 39.3, 39.32, 39.71, 39.69, 40.77, 44.07]
+[68.18]
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..876dfc3
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 140.71093130111694, "TIME_S_1KI": 1407.1093130111694, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 10166.688220214843, "W": 65.6, "J_1KI": 101666.88220214842, "W_1KI": 655.9999999999999, "W_D": 30.132499999999993, "J_D": 4669.934951152801, "W_D_1KI": 301.32499999999993, "J_D_1KI": 3013.249999999999}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..7da6c28
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_0.5.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.5', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 140.71093130111694}
+
+tensor(indices=tensor([[9534, 8557, 4354,  ..., 3037, 7198, 1930],
+                       [5851, 8260, 2146,  ..., 8789, 6190, 8909]]),
+       values=tensor([0.0336, 0.5969, 0.5898,  ..., 0.6856, 0.1994, 0.9040]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_coo)
+tensor([0.6607, 0.7149, 0.2750,  ..., 0.7398, 0.0558, 0.8929])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 140.71093130111694 seconds
+
+tensor(indices=tensor([[9534, 8557, 4354,  ..., 3037, 7198, 1930],
+                       [5851, 8260, 2146,  ..., 8789, 6190, 8909]]),
+       values=tensor([0.0336, 0.5969, 0.5898,  ..., 0.6856, 0.1994, 0.9040]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_coo)
+tensor([0.6607, 0.7149, 0.2750,  ..., 0.7398, 0.0558, 0.8929])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 140.71093130111694 seconds
+
+[39.87, 39.52, 39.22, 39.19, 39.33, 39.15, 39.09, 39.71, 39.26, 39.43]
+[65.6]
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..0f8e038
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 312424, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.427929401397705, "TIME_S_1KI": 0.03337749149040312, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 847.5212089419365, "W": 64.75, "J_1KI": 2.712727603967482, "W_1KI": 0.20725040329808211, "W_D": 29.04875, "J_D": 380.2228836795687, "W_D_1KI": 0.09297861239853532, "J_D_1KI": 0.0002976039369527799}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..9a8df2c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,962 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.012271881103515625}
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+                       [5276,  199, 9672,  ..., 1948, 9889, 3256]]),
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+Time: 10.427929401397705 seconds
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..e7e344a
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 70776, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.457204341888428, "TIME_S_1KI": 0.14775071128473533, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 843.2878910303115, "W": 64.57, "J_1KI": 11.914884862528421, "W_1KI": 0.9123149090087034, "W_D": 28.59675, "J_D": 373.4751896828413, "W_D_1KI": 0.4040458630044083, "J_D_1KI": 0.0057087976574602736}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..91bc8a2
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,73 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.02408146858215332}
+
+tensor(indices=tensor([[4932, 9298, 6973,  ...,  430, 6814,  307],
+                       [6790, 8296, 5102,  ..., 7681, 8109, 9670]]),
+       values=tensor([0.4381, 0.0121, 0.9377,  ..., 0.2212, 0.9658, 0.2274]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_coo)
+tensor([0.2308, 0.5790, 0.9172,  ..., 0.4919, 0.9399, 0.7488])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.02408146858215332 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '43601', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.468353033065796}
+
+tensor(indices=tensor([[3074, 7371, 7778,  ..., 4885, 8899, 4794],
+                       [1726, 2812,  228,  ...,   25, 3403, 1703]]),
+       values=tensor([0.7489, 0.8834, 0.3612,  ..., 0.9061, 0.2816, 0.9175]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_coo)
+tensor([0.6264, 0.1482, 0.9244,  ..., 0.3300, 0.2380, 0.1920])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 6.468353033065796 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '70776', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.457204341888428}
+
+tensor(indices=tensor([[3104, 6962, 9843,  ..., 4567, 9740, 7799],
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..3691946
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_0.0001.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..a712061
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_0.0001.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..25d52a3
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..a49e42f
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+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_1e-05.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..98aaf1d
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..5d3f407
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+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_500000_5e-05.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..6a5f3f6
--- /dev/null
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..5d1c601
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_0.0001.output
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new file mode 100644
index 0000000..2c3e35e
--- /dev/null
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..dbfe5c7
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new file mode 100644
index 0000000..61832df
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index 0000000..fb12be8
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new file mode 100644
index 0000000..fce4712
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_0.05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_0.05.output
new file mode 100644
index 0000000..c1394b0
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..6bfdaad
--- /dev/null
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..6dd2d76
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_1e-05.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..ce8f9d0
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+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_5e-05.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..f7938cc
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_50000_5e-05.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..98f55da
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..2672d91
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..e2a7bf3
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..dd5917a
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.001.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..ec96c42
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.01.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..8ae1484
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.01.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '0.01', '-c', '1']
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..577e414
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.05.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..9b2e914
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.05.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..895c0c1
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.1.json
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+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 148, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.523432970046997, "TIME_S_1KI": 71.10427682464187, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 874.4558725833892, "W": 64.86, "J_1KI": 5908.48562556344, "W_1KI": 438.2432432432433, "W_D": 29.656499999999994, "J_D": 399.83503831744184, "W_D_1KI": 200.38175675675672, "J_D_1KI": 1353.930788897005}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..8de306d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 7.055205345153809}
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..3b66878
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.2.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..f07ad96
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.2.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..0be25ea
--- /dev/null
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..1051597
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+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.3.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..629775e
--- /dev/null
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..c5c5ef5
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+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.4.output
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..637a4a1
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.5.json
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..5ba1765
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_0.5.output
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+tensor([0.7940, 0.0782, 0.3674,  ..., 0.7831, 0.0641, 0.5356])
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diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..91a48e8
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 904172, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.473444938659668, "TIME_S_1KI": 0.011583465246280207, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 839.7985753679276, "W": 65.14, "J_1KI": 0.9288040056183199, "W_1KI": 0.07204381467242958, "W_D": 29.481250000000003, "J_D": 380.078473289311, "W_D_1KI": 0.0326057984542764, "J_D_1KI": 3.6061499863163644e-05}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..adf1553
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,456 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '1e-05', '-c', '1']
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+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 904172, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.473444938659668, 'TIME_S_1KI': 0.011583465246280207, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 839.7985753679276, 'W': 65.14, 'J_1KI': 0.9288040056183199, 'W_1KI': 0.07204381467242958, 'W_D': 29.481250000000003, 'J_D': 380.078473289311, 'W_D_1KI': 0.0326057984542764, 'J_D_1KI': 3.6061499863163644e-05}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..e559329
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 262660, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.607843160629272, "TIME_S_1KI": 0.04038621472865786, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 846.3253393292428, "W": 64.87, "J_1KI": 3.222132564262708, "W_1KI": 0.24697327343333586, "W_D": 29.246500000000005, "J_D": 381.5639592522383, "W_D_1KI": 0.11134736922256912, "J_D_1KI": 0.00042392206359007507}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..bab70c0
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_coo_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,73 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.013487577438354492}
+
+tensor(indices=tensor([[3458, 3296,  222,  ..., 1444, 2251, 3846],
+                       [4299, 1410, 1932,  ..., 3706, 1279, 2032]]),
+       values=tensor([0.4478, 0.3309, 0.1032,  ..., 0.1760, 0.8945, 0.2442]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.6830, 0.5755, 0.7439,  ..., 0.3425, 0.0047, 0.9929])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.013487577438354492 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '77849', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.1120543479919434}
+
+tensor(indices=tensor([[1587, 3871, 1326,  ..., 3783, 1439, 1937],
+                       [1157, 3622, 4030,  ..., 4369, 2134, 1156]]),
+       values=tensor([0.3704, 0.8984, 0.6075,  ..., 0.7516, 0.5164, 0.7911]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.4942, 0.1126, 0.2663,  ..., 0.4271, 0.4829, 0.3873])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 3.1120543479919434 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '262660', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.607843160629272}
+
+tensor(indices=tensor([[1848, 1441, 3244,  ...,  995, 3529,  801],
+                       [2000, 3321, 1860,  ..., 4284,  416, 3750]]),
+       values=tensor([0.8242, 0.0835, 0.8195,  ..., 0.4255, 0.6275, 0.4442]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.2894, 0.0717, 0.9555,  ..., 0.7076, 0.4099, 0.6837])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.607843160629272 seconds
+
+tensor(indices=tensor([[1848, 1441, 3244,  ...,  995, 3529,  801],
+                       [2000, 3321, 1860,  ..., 4284,  416, 3750]]),
+       values=tensor([0.8242, 0.0835, 0.8195,  ..., 0.4255, 0.6275, 0.4442]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.2894, 0.0717, 0.9555,  ..., 0.7076, 0.4099, 0.6837])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.607843160629272 seconds
+
+[39.83, 39.12, 39.36, 39.7, 39.56, 39.75, 39.63, 39.61, 39.2, 39.05]
+[64.87]
+13.046482801437378
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 262660, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.607843160629272, 'TIME_S_1KI': 0.04038621472865786, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.3253393292428, 'W': 64.87}
+[39.83, 39.12, 39.36, 39.7, 39.56, 39.75, 39.63, 39.61, 39.2, 39.05, 39.72, 38.94, 39.56, 39.07, 39.16, 38.92, 39.18, 39.41, 40.79, 44.42]
+712.47
+35.6235
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 262660, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.607843160629272, 'TIME_S_1KI': 0.04038621472865786, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 846.3253393292428, 'W': 64.87, 'J_1KI': 3.222132564262708, 'W_1KI': 0.24697327343333586, 'W_D': 29.246500000000005, 'J_D': 381.5639592522383, 'W_D_1KI': 0.11134736922256912, 'J_D_1KI': 0.00042392206359007507}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json
index 57ab5e0..b51440d 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 6238, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.769784212112427, "TIME_S_1KI": 1.7264803161449866, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 915.7679015994072, "W": 67.11, "J_1KI": 146.8047293362307, "W_1KI": 10.75825585123437, "W_D": 31.7095, "J_D": 432.70067465007304, "W_D_1KI": 5.083279897403013, "J_D_1KI": 0.8148893711771422}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 5996, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.181211709976196, "TIME_S_1KI": 1.6980006187418606, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 899.0054618740082, "W": 66.87, "J_1KI": 149.93419977885395, "W_1KI": 11.152434956637759, "W_D": 31.423750000000005, "J_D": 422.4633300817014, "W_D_1KI": 5.240785523682455, "J_D_1KI": 0.8740469519150192}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output
index fe560b9..f9c7f08 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.18373441696166992}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.18697094917297363}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       9,      18,  ...,  999979,
-                             999991, 1000000]),
-       col_indices=tensor([ 9419, 17690, 19775,  ..., 65904, 78906, 97730]),
-       values=tensor([0.1002, 0.0063, 0.1334,  ..., 0.8477, 0.2339, 0.2955]),
+tensor(crow_indices=tensor([      0,      11,      24,  ...,  999982,
+                             999994, 1000000]),
+       col_indices=tensor([ 9983, 15946, 21045,  ..., 77894, 88648, 89497]),
+       values=tensor([0.1086, 0.7102, 0.2104,  ..., 0.2072, 0.6130, 0.1097]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9261, 0.9567, 0.5751,  ..., 0.3199, 0.0262, 0.3042])
+tensor([0.1361, 0.7050, 0.5435,  ..., 0.1985, 0.6376, 0.3340])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,39 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 0.18373441696166992 seconds
+Time: 0.18697094917297363 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '5714', '-ss', '100000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.617395401000977}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '5615', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.832083702087402}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       8,      15,  ...,  999976,
-                             999989, 1000000]),
-       col_indices=tensor([12342, 20602, 31374,  ..., 83399, 88988, 97850]),
-       values=tensor([0.3594, 0.1684, 0.5761,  ..., 0.4601, 0.3694, 0.1608]),
-       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.5848, 0.1566, 0.7046,  ..., 0.5634, 0.8550, 0.2097])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 1000000
-Density: 0.0001
-Time: 9.617395401000977 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '6238', '-ss', '100000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.769784212112427}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       7,      15,  ...,  999972,
+tensor(crow_indices=tensor([      0,      15,      27,  ...,  999979,
                              999990, 1000000]),
-       col_indices=tensor([ 1977,  7363, 16479,  ..., 91067, 93957, 95744]),
-       values=tensor([0.8934, 0.4616, 0.7140,  ..., 0.3224, 0.7140, 0.9696]),
+       col_indices=tensor([ 2643,  3189,  5240,  ..., 68705, 72961, 97726]),
+       values=tensor([0.7160, 0.7950, 0.9929,  ..., 0.3041, 0.4414, 0.2180]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0584, 0.0097, 0.6336,  ..., 0.7366, 0.8575, 0.7006])
+tensor([0.1777, 0.1946, 0.6010,  ..., 0.0098, 0.9741, 0.9528])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.769784212112427 seconds
+Time: 9.832083702087402 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '5996', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.181211709976196}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       7,      15,  ...,  999972,
-                             999990, 1000000]),
-       col_indices=tensor([ 1977,  7363, 16479,  ..., 91067, 93957, 95744]),
-       values=tensor([0.8934, 0.4616, 0.7140,  ..., 0.3224, 0.7140, 0.9696]),
+tensor(crow_indices=tensor([      0,       7,      18,  ...,  999987,
+                             999992, 1000000]),
+       col_indices=tensor([23061, 33848, 40530,  ..., 69911, 92654, 99136]),
+       values=tensor([0.3534, 0.8273, 0.6908,  ..., 0.2268, 0.3390, 0.9257]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0584, 0.0097, 0.6336,  ..., 0.7366, 0.8575, 0.7006])
+tensor([0.2997, 0.5907, 0.9368,  ..., 0.3220, 0.5731, 0.8724])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.769784212112427 seconds
+Time: 10.181211709976196 seconds
 
-[39.95, 40.61, 39.69, 39.43, 39.55, 38.89, 39.33, 39.12, 39.11, 38.94]
-[67.11]
-13.645774126052856
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6238, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.769784212112427, 'TIME_S_1KI': 1.7264803161449866, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 915.7679015994072, 'W': 67.11}
-[39.95, 40.61, 39.69, 39.43, 39.55, 38.89, 39.33, 39.12, 39.11, 38.94, 39.57, 38.89, 39.02, 39.08, 39.5, 39.31, 39.6, 39.05, 39.01, 39.18]
-708.01
-35.4005
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6238, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.769784212112427, 'TIME_S_1KI': 1.7264803161449866, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 915.7679015994072, 'W': 67.11, 'J_1KI': 146.8047293362307, 'W_1KI': 10.75825585123437, 'W_D': 31.7095, 'J_D': 432.70067465007304, 'W_D_1KI': 5.083279897403013, 'J_D_1KI': 0.8148893711771422}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       7,      18,  ...,  999987,
+                             999992, 1000000]),
+       col_indices=tensor([23061, 33848, 40530,  ..., 69911, 92654, 99136]),
+       values=tensor([0.3534, 0.8273, 0.6908,  ..., 0.2268, 0.3390, 0.9257]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2997, 0.5907, 0.9368,  ..., 0.3220, 0.5731, 0.8724])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.181211709976196 seconds
+
+[40.21, 39.43, 39.39, 39.55, 39.46, 39.42, 38.94, 39.03, 39.08, 39.01]
+[66.87]
+13.444077491760254
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 5996, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.181211709976196, 'TIME_S_1KI': 1.6980006187418606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 899.0054618740082, 'W': 66.87}
+[40.21, 39.43, 39.39, 39.55, 39.46, 39.42, 38.94, 39.03, 39.08, 39.01, 39.76, 41.07, 39.06, 38.89, 39.56, 39.31, 39.45, 39.12, 38.97, 39.41]
+708.925
+35.44625
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 5996, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.181211709976196, 'TIME_S_1KI': 1.6980006187418606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 899.0054618740082, 'W': 66.87, 'J_1KI': 149.93419977885395, 'W_1KI': 11.152434956637759, 'W_D': 31.423750000000005, 'J_D': 422.4633300817014, 'W_D_1KI': 5.240785523682455, 'J_D_1KI': 0.8740469519150192}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.json
index a6d9713..d2b5b2f 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 631, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.591248035430908, "TIME_S_1KI": 16.78486217976372, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1175.510341129303, "W": 76.44, "J_1KI": 1862.932394816645, "W_1KI": 121.14104595879556, "W_D": 40.61775, "J_D": 624.6282726112604, "W_D_1KI": 64.37044374009508, "J_D_1KI": 102.0133815215453}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 646, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.64788269996643, "TIME_S_1KI": 16.482790557223577, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1248.8031814670564, "W": 77.37, "J_1KI": 1933.1318598561245, "W_1KI": 119.76780185758516, "W_D": 41.77675000000001, "J_D": 674.3044889667035, "W_D_1KI": 64.66989164086688, "J_D_1KI": 100.10819139453076}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.output
index 4526ab4..6e9f9fd 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 1.6621592044830322}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 1.6243762969970703}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       87,      180,  ...,  9999810,
-                             9999900, 10000000]),
-       col_indices=tensor([ 1316,  2180,  2488,  ..., 99391, 99679, 99852]),
-       values=tensor([0.4838, 0.8512, 0.8260,  ..., 0.7772, 0.9919, 0.5400]),
+tensor(crow_indices=tensor([       0,      105,      209,  ...,  9999808,
+                             9999917, 10000000]),
+       col_indices=tensor([  518,  1117,  2607,  ..., 97815, 97945, 98368]),
+       values=tensor([0.8433, 0.9852, 0.6731,  ..., 0.0306, 0.9795, 0.2346]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.7127, 0.0015, 0.2736,  ..., 0.7345, 0.7377, 0.4477])
+tensor([0.4117, 0.6083, 0.5875,  ..., 0.6399, 0.8751, 0.5908])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 1.6621592044830322 seconds
+Time: 1.6243762969970703 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '631', '-ss', '100000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.591248035430908}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '646', '-ss', '100000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.64788269996643}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      104,      198,  ...,  9999801,
-                             9999900, 10000000]),
-       col_indices=tensor([ 1720,  2057,  4608,  ..., 98148, 99667, 99757]),
-       values=tensor([0.5091, 0.6981, 0.1321,  ..., 0.4342, 0.6647, 0.6565]),
+tensor(crow_indices=tensor([       0,       95,      203,  ...,  9999789,
+                             9999904, 10000000]),
+       col_indices=tensor([  253,  1563,  3273,  ..., 97045, 97783, 98258]),
+       values=tensor([0.1838, 0.7315, 0.7723,  ..., 0.9826, 0.1322, 0.0476]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3567, 0.4501, 0.1430,  ..., 0.3086, 0.4387, 0.0746])
+tensor([0.6150, 0.7553, 0.5500,  ..., 0.1787, 0.2534, 0.8601])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 10.591248035430908 seconds
+Time: 10.64788269996643 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      104,      198,  ...,  9999801,
-                             9999900, 10000000]),
-       col_indices=tensor([ 1720,  2057,  4608,  ..., 98148, 99667, 99757]),
-       values=tensor([0.5091, 0.6981, 0.1321,  ..., 0.4342, 0.6647, 0.6565]),
+tensor(crow_indices=tensor([       0,       95,      203,  ...,  9999789,
+                             9999904, 10000000]),
+       col_indices=tensor([  253,  1563,  3273,  ..., 97045, 97783, 98258]),
+       values=tensor([0.1838, 0.7315, 0.7723,  ..., 0.9826, 0.1322, 0.0476]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3567, 0.4501, 0.1430,  ..., 0.3086, 0.4387, 0.0746])
+tensor([0.6150, 0.7553, 0.5500,  ..., 0.1787, 0.2534, 0.8601])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 10.591248035430908 seconds
+Time: 10.64788269996643 seconds
 
-[39.78, 39.0, 39.24, 38.88, 39.37, 39.28, 39.34, 38.84, 41.87, 41.9]
-[76.44]
-15.378209590911865
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 631, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.591248035430908, 'TIME_S_1KI': 16.78486217976372, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1175.510341129303, 'W': 76.44}
-[39.78, 39.0, 39.24, 38.88, 39.37, 39.28, 39.34, 38.84, 41.87, 41.9, 39.74, 39.44, 39.02, 44.31, 40.1, 38.93, 39.71, 39.12, 39.62, 39.33]
-716.4449999999999
-35.82225
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 631, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.591248035430908, 'TIME_S_1KI': 16.78486217976372, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1175.510341129303, 'W': 76.44, 'J_1KI': 1862.932394816645, 'W_1KI': 121.14104595879556, 'W_D': 40.61775, 'J_D': 624.6282726112604, 'W_D_1KI': 64.37044374009508, 'J_D_1KI': 102.0133815215453}
+[39.61, 39.03, 39.15, 39.24, 38.96, 39.2, 39.13, 39.19, 39.04, 39.38]
+[77.37]
+16.140664100646973
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 646, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.64788269996643, 'TIME_S_1KI': 16.482790557223577, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1248.8031814670564, 'W': 77.37}
+[39.61, 39.03, 39.15, 39.24, 38.96, 39.2, 39.13, 39.19, 39.04, 39.38, 39.64, 40.35, 39.48, 38.96, 38.99, 39.05, 44.37, 39.04, 39.56, 39.62]
+711.865
+35.59325
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 646, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.64788269996643, 'TIME_S_1KI': 16.482790557223577, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1248.8031814670564, 'W': 77.37, 'J_1KI': 1933.1318598561245, 'W_1KI': 119.76780185758516, 'W_D': 41.77675000000001, 'J_D': 674.3044889667035, 'W_D_1KI': 64.66989164086688, 'J_D_1KI': 100.10819139453076}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..c4d6a14
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 15.186817169189453, "TIME_S_1KI": 151.86817169189453, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3281.6506419181824, "W": 75.41, "J_1KI": 32816.506419181824, "W_1KI": 754.1, "W_D": 39.74225, "J_D": 1729.4812388777732, "W_D_1KI": 397.4225, "J_D_1KI": 3974.225}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..12a82de
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 15.186817169189453}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      1054,      2106,  ...,
+                             99998025,  99998968, 100000000]),
+       col_indices=tensor([   33,    76,    94,  ..., 99342, 99803, 99929]),
+       values=tensor([0.0074, 0.7661, 0.5151,  ..., 0.3205, 0.9444, 0.4858]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.3776, 0.7244, 0.4309,  ..., 0.7745, 0.1365, 0.6548])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 15.186817169189453 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      1054,      2106,  ...,
+                             99998025,  99998968, 100000000]),
+       col_indices=tensor([   33,    76,    94,  ..., 99342, 99803, 99929]),
+       values=tensor([0.0074, 0.7661, 0.5151,  ..., 0.3205, 0.9444, 0.4858]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.3776, 0.7244, 0.4309,  ..., 0.7745, 0.1365, 0.6548])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 15.186817169189453 seconds
+
+[39.84, 39.14, 44.16, 39.53, 39.81, 39.87, 39.21, 39.12, 39.4, 39.04]
+[75.41]
+43.517446517944336
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 15.186817169189453, 'TIME_S_1KI': 151.86817169189453, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3281.6506419181824, 'W': 75.41}
+[39.84, 39.14, 44.16, 39.53, 39.81, 39.87, 39.21, 39.12, 39.4, 39.04, 40.31, 39.84, 39.22, 39.29, 39.14, 39.05, 39.21, 39.02, 39.08, 39.34]
+713.3549999999999
+35.66775
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 15.186817169189453, 'TIME_S_1KI': 151.86817169189453, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3281.6506419181824, 'W': 75.41, 'J_1KI': 32816.506419181824, 'W_1KI': 754.1, 'W_D': 39.74225, 'J_D': 1729.4812388777732, 'W_D_1KI': 397.4225, 'J_D_1KI': 3974.225}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json
index 87f4ecf..098fbe2 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 12301, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.471284627914429, "TIME_S_1KI": 0.8512547457860685, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 857.4546840524674, "W": 65.29, "J_1KI": 69.70609576883729, "W_1KI": 5.307698561092595, "W_D": 29.40700000000001, "J_D": 386.2026327757837, "W_D_1KI": 2.3906186488903347, "J_D_1KI": 0.19434343946755017}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 12317, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.456599712371826, "TIME_S_1KI": 0.8489567031234737, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 856.4664033508301, "W": 65.12, "J_1KI": 69.53530919467647, "W_1KI": 5.287001704960624, "W_D": 29.49900000000001, "J_D": 387.9745459527971, "W_D_1KI": 2.39498254445076, "J_D_1KI": 0.1944452824917399}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output
index e82259e..4658af1 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.09777641296386719}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.09817862510681152}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      0,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99998,  99998,
                             100000]),
-       col_indices=tensor([10415, 34481, 41161,  ..., 69185,  8793, 68858]),
-       values=tensor([0.7697, 0.4410, 0.3075,  ..., 0.8657, 0.1828, 0.6667]),
+       col_indices=tensor([53095, 59802, 83624,  ..., 47814, 38432, 88674]),
+       values=tensor([0.4970, 0.8457, 0.0545,  ..., 0.0462, 0.7885, 0.8704]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2533, 0.9138, 0.2717,  ..., 0.2019, 0.7103, 0.0862])
+tensor([0.9011, 0.7106, 0.3845,  ..., 0.4694, 0.1392, 0.4036])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 0.09777641296386719 seconds
+Time: 0.09817862510681152 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '10738', '-ss', '100000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.165135860443115}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '10694', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.116411209106445}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      2,  ...,  99996,  99996,
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99998,  99999,
                             100000]),
-       col_indices=tensor([17140, 55127, 70380,  ...,  9005, 21920, 77148]),
-       values=tensor([0.4913, 0.5196, 0.1867,  ..., 0.0903, 0.8718, 0.1023]),
+       col_indices=tensor([41985, 49475, 77186,  ..., 66378, 39934, 68538]),
+       values=tensor([0.1520, 0.5404, 0.6250,  ..., 0.3363, 0.9694, 0.5844]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0174, 0.3477, 0.7027,  ..., 0.9312, 0.2138, 0.3974])
+tensor([0.4130, 0.6319, 0.4955,  ..., 0.6021, 0.2338, 0.9434])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 9.165135860443115 seconds
+Time: 9.116411209106445 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12301', '-ss', '100000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.471284627914429}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12317', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.456599712371826}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      5,  ...,  99999, 100000,
+tensor(crow_indices=tensor([     0,      0,      3,  ...,  99997,  99998,
                             100000]),
-       col_indices=tensor([46597,   403, 54918,  ..., 58141, 94085, 20979]),
-       values=tensor([0.5040, 0.7325, 0.7996,  ..., 0.9839, 0.2631, 0.4936]),
+       col_indices=tensor([13901, 18701, 64861,  ...,  8515, 17451, 56478]),
+       values=tensor([0.6004, 0.7779, 0.2413,  ..., 0.1569, 0.9416, 0.1782]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2071, 0.7418, 0.9347,  ..., 0.4731, 0.1489, 0.5724])
+tensor([0.5455, 0.6312, 0.2012,  ..., 0.1711, 0.1722, 0.6250])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.471284627914429 seconds
+Time: 10.456599712371826 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      5,  ...,  99999, 100000,
+tensor(crow_indices=tensor([     0,      0,      3,  ...,  99997,  99998,
                             100000]),
-       col_indices=tensor([46597,   403, 54918,  ..., 58141, 94085, 20979]),
-       values=tensor([0.5040, 0.7325, 0.7996,  ..., 0.9839, 0.2631, 0.4936]),
+       col_indices=tensor([13901, 18701, 64861,  ...,  8515, 17451, 56478]),
+       values=tensor([0.6004, 0.7779, 0.2413,  ..., 0.1569, 0.9416, 0.1782]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2071, 0.7418, 0.9347,  ..., 0.4731, 0.1489, 0.5724])
+tensor([0.5455, 0.6312, 0.2012,  ..., 0.1711, 0.1722, 0.6250])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.471284627914429 seconds
+Time: 10.456599712371826 seconds
 
-[39.92, 39.64, 39.53, 39.59, 39.42, 39.38, 39.39, 39.02, 44.4, 39.0]
-[65.29]
-13.13301706314087
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12301, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.471284627914429, 'TIME_S_1KI': 0.8512547457860685, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.4546840524674, 'W': 65.29}
-[39.92, 39.64, 39.53, 39.59, 39.42, 39.38, 39.39, 39.02, 44.4, 39.0, 39.77, 38.96, 39.1, 39.29, 38.97, 44.08, 39.5, 39.1, 39.5, 38.89]
-717.66
-35.882999999999996
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12301, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.471284627914429, 'TIME_S_1KI': 0.8512547457860685, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.4546840524674, 'W': 65.29, 'J_1KI': 69.70609576883729, 'W_1KI': 5.307698561092595, 'W_D': 29.40700000000001, 'J_D': 386.2026327757837, 'W_D_1KI': 2.3906186488903347, 'J_D_1KI': 0.19434343946755017}
+[39.71, 39.49, 39.44, 39.4, 39.16, 39.02, 39.36, 39.0, 39.17, 39.0]
+[65.12]
+13.152125358581543
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12317, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.456599712371826, 'TIME_S_1KI': 0.8489567031234737, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.4664033508301, 'W': 65.12}
+[39.71, 39.49, 39.44, 39.4, 39.16, 39.02, 39.36, 39.0, 39.17, 39.0, 39.89, 39.65, 38.99, 40.22, 39.01, 38.97, 39.04, 44.06, 39.51, 39.26]
+712.4199999999998
+35.620999999999995
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12317, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.456599712371826, 'TIME_S_1KI': 0.8489567031234737, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.4664033508301, 'W': 65.12, 'J_1KI': 69.53530919467647, 'W_1KI': 5.287001704960624, 'W_D': 29.49900000000001, 'J_D': 387.9745459527971, 'W_D_1KI': 2.39498254445076, 'J_D_1KI': 0.1944452824917399}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.json
index 96abffc..2035104 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 7670, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.499921083450317, "TIME_S_1KI": 1.3689597240482814, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 873.9648419952392, "W": 65.94, "J_1KI": 113.94587248960094, "W_1KI": 8.597131681877444, "W_D": 30.4285, "J_D": 403.29753100776674, "W_D_1KI": 3.9672099087353327, "J_D_1KI": 0.5172372762366796}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 7457, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.19743800163269, "TIME_S_1KI": 1.3674987262481815, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 852.7737856292725, "W": 65.84, "J_1KI": 114.35882870179329, "W_1KI": 8.829287917393053, "W_D": 30.381500000000003, "J_D": 393.5076969637871, "W_D_1KI": 4.074225559876627, "J_D_1KI": 0.5463625532890742}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.output
index 7c108ed..1570d36 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.15105009078979492}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.1507415771484375}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      7,  ..., 499997, 499999,
+tensor(crow_indices=tensor([     0,      4,      8,  ..., 499987, 499993,
                             500000]),
-       col_indices=tensor([ 4363, 49954, 63940,  ...,   740, 19551, 36085]),
-       values=tensor([0.7532, 0.6946, 0.3669,  ..., 0.0744, 0.6590, 0.6868]),
+       col_indices=tensor([ 7791, 16626, 25734,  ..., 44751, 53769, 83372]),
+       values=tensor([0.4260, 0.8528, 0.7551,  ..., 0.4583, 0.2926, 0.1239]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.0672, 0.6383, 0.6761,  ..., 0.1188, 0.9489, 0.0863])
+tensor([0.3576, 0.1829, 0.1325,  ..., 0.8642, 0.2263, 0.3102])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 0.15105009078979492 seconds
+Time: 0.1507415771484375 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '6951', '-ss', '100000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.51450252532959}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '6965', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.80711817741394}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,     13,  ..., 499996, 499997,
+tensor(crow_indices=tensor([     0,      5,     10,  ..., 499992, 499995,
                             500000]),
-       col_indices=tensor([ 4260, 42899, 54575,  ...,  5425, 31756, 61151]),
-       values=tensor([0.4952, 0.8247, 0.2969,  ..., 0.2331, 0.9267, 0.2319]),
+       col_indices=tensor([16269, 59598, 69161,  ..., 42613, 67997, 86249]),
+       values=tensor([0.8036, 0.6259, 0.8288,  ..., 0.8919, 0.7503, 0.7598]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.7470, 0.2926, 0.2731,  ..., 0.9830, 0.8295, 0.9958])
+tensor([0.9828, 0.5323, 0.9879,  ..., 0.1523, 0.5476, 0.9879])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 9.51450252532959 seconds
+Time: 9.80711817741394 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '7670', '-ss', '100000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.499921083450317}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '7457', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.19743800163269}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     18,  ..., 499989, 499996,
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 499986, 499995,
                             500000]),
-       col_indices=tensor([16754, 23077, 28797,  ..., 22620, 46442, 72952]),
-       values=tensor([0.6737, 0.8129, 0.9335,  ..., 0.4581, 0.1021, 0.2391]),
+       col_indices=tensor([11558, 50648, 13976,  ..., 64781, 65283, 76485]),
+       values=tensor([0.0367, 0.1968, 0.7333,  ..., 0.2503, 0.6323, 0.5776]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.9975, 0.9245, 0.4309,  ..., 0.4303, 0.6144, 0.3183])
+tensor([0.0500, 0.8091, 0.0490,  ..., 0.0151, 0.5519, 0.7523])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.499921083450317 seconds
+Time: 10.19743800163269 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     18,  ..., 499989, 499996,
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 499986, 499995,
                             500000]),
-       col_indices=tensor([16754, 23077, 28797,  ..., 22620, 46442, 72952]),
-       values=tensor([0.6737, 0.8129, 0.9335,  ..., 0.4581, 0.1021, 0.2391]),
+       col_indices=tensor([11558, 50648, 13976,  ..., 64781, 65283, 76485]),
+       values=tensor([0.0367, 0.1968, 0.7333,  ..., 0.2503, 0.6323, 0.5776]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.9975, 0.9245, 0.4309,  ..., 0.4303, 0.6144, 0.3183])
+tensor([0.0500, 0.8091, 0.0490,  ..., 0.0151, 0.5519, 0.7523])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.499921083450317 seconds
+Time: 10.19743800163269 seconds
 
-[39.66, 38.93, 40.25, 39.53, 39.7, 39.41, 40.1, 39.55, 39.0, 39.05]
-[65.94]
-13.25394058227539
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 7670, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.499921083450317, 'TIME_S_1KI': 1.3689597240482814, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 873.9648419952392, 'W': 65.94}
-[39.66, 38.93, 40.25, 39.53, 39.7, 39.41, 40.1, 39.55, 39.0, 39.05, 39.72, 39.51, 39.22, 39.24, 39.32, 39.23, 40.03, 39.22, 39.23, 39.09]
-710.23
-35.5115
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 7670, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.499921083450317, 'TIME_S_1KI': 1.3689597240482814, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 873.9648419952392, 'W': 65.94, 'J_1KI': 113.94587248960094, 'W_1KI': 8.597131681877444, 'W_D': 30.4285, 'J_D': 403.29753100776674, 'W_D_1KI': 3.9672099087353327, 'J_D_1KI': 0.5172372762366796}
+[40.05, 39.37, 39.66, 39.38, 39.07, 40.14, 39.07, 39.02, 40.82, 38.87]
+[65.84]
+12.952214241027832
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 7457, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.19743800163269, 'TIME_S_1KI': 1.3674987262481815, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 852.7737856292725, 'W': 65.84}
+[40.05, 39.37, 39.66, 39.38, 39.07, 40.14, 39.07, 39.02, 40.82, 38.87, 39.59, 39.1, 39.82, 38.89, 39.48, 38.91, 39.49, 39.19, 38.92, 39.17]
+709.1700000000001
+35.4585
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 7457, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.19743800163269, 'TIME_S_1KI': 1.3674987262481815, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 852.7737856292725, 'W': 65.84, 'J_1KI': 114.35882870179329, 'W_1KI': 8.829287917393053, 'W_D': 30.381500000000003, 'J_D': 393.5076969637871, 'W_D_1KI': 4.074225559876627, 'J_D_1KI': 0.5463625532890742}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json
index a0173f2..f3e1ac9 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 237172, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.292231321334839, "TIME_S_1KI": 0.04339564249293693, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 862.8344623732567, "W": 65.91, "J_1KI": 3.638011495342016, "W_1KI": 0.2778995834246875, "W_D": 30.572500000000005, "J_D": 400.22768321812157, "W_D_1KI": 0.12890433946671614, "J_D_1KI": 0.0005435057235538602}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 237686, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.452977180480957, "TIME_S_1KI": 0.043978093705481, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 865.2581436538696, "W": 66.08, "J_1KI": 3.640341221838348, "W_1KI": 0.2780138502057336, "W_D": 29.752000000000002, "J_D": 389.57567024803166, "W_D_1KI": 0.12517354829480912, "J_D_1KI": 0.0005266340814974762}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output
index 104c518..50b6048 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.016118526458740234}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.016066312789916992}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     3,  ...,  9995,  9998, 10000]),
-       col_indices=tensor([3770, 7218, 7901,  ..., 7147, 2189, 2422]),
-       values=tensor([0.0682, 0.4925, 0.4932,  ..., 0.9859, 0.2682, 0.5675]),
+tensor(crow_indices=tensor([    0,     3,     3,  ...,  9997,  9999, 10000]),
+       col_indices=tensor([1468, 1838, 1987,  ...,   60, 1530, 9720]),
+       values=tensor([0.8272, 0.8780, 0.7097,  ..., 0.5113, 0.4922, 0.1493]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1703, 0.4753, 0.7272,  ..., 0.9852, 0.8357, 0.1698])
+tensor([0.2034, 0.3353, 0.9918,  ..., 0.5313, 0.4103, 0.3120])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 0.016118526458740234 seconds
+Time: 0.016066312789916992 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65142', '-ss', '10000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.8839359283447266}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65354', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.8870651721954346}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ...,  9999, 10000, 10000]),
-       col_indices=tensor([6160, 1315,  448,  ..., 9882, 6598, 7658]),
-       values=tensor([0.4764, 0.2622, 0.7017,  ..., 0.9860, 0.1866, 0.7529]),
+tensor(crow_indices=tensor([    0,     0,     0,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([6857, 6728, 4748,  ...,   94, 5167, 5766]),
+       values=tensor([0.3641, 0.8896, 0.0675,  ..., 0.3656, 0.2913, 0.3223]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.4338, 0.9515, 0.6308,  ..., 0.9365, 0.1556, 0.4912])
+tensor([0.3076, 0.2027, 0.0704,  ..., 0.9202, 0.3518, 0.8650])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 2.8839359283447266 seconds
+Time: 2.8870651721954346 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '237172', '-ss', '10000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.292231321334839}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '237686', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.452977180480957}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     3,  ...,  9999, 10000, 10000]),
-       col_indices=tensor([1278, 7265, 6993,  ..., 9863, 6468, 3133]),
-       values=tensor([0.6288, 0.8682, 0.0748,  ..., 0.3062, 0.2031, 0.3525]),
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([6668, 9602, 2407,  ..., 1580, 8139, 9959]),
+       values=tensor([0.7282, 0.8866, 0.9389,  ..., 0.3308, 0.0411, 0.7608]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1078, 0.8244, 0.8698,  ..., 0.0830, 0.2322, 0.6518])
+tensor([0.6674, 0.2718, 0.8109,  ..., 0.1456, 0.2717, 0.0651])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.292231321334839 seconds
+Time: 10.452977180480957 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     3,  ...,  9999, 10000, 10000]),
-       col_indices=tensor([1278, 7265, 6993,  ..., 9863, 6468, 3133]),
-       values=tensor([0.6288, 0.8682, 0.0748,  ..., 0.3062, 0.2031, 0.3525]),
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([6668, 9602, 2407,  ..., 1580, 8139, 9959]),
+       values=tensor([0.7282, 0.8866, 0.9389,  ..., 0.3308, 0.0411, 0.7608]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1078, 0.8244, 0.8698,  ..., 0.0830, 0.2322, 0.6518])
+tensor([0.6674, 0.2718, 0.8109,  ..., 0.1456, 0.2717, 0.0651])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.292231321334839 seconds
+Time: 10.452977180480957 seconds
 
-[40.27, 39.31, 39.28, 38.86, 41.16, 39.32, 39.02, 39.09, 38.81, 39.11]
-[65.91]
-13.091100931167603
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 237172, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.292231321334839, 'TIME_S_1KI': 0.04339564249293693, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.8344623732567, 'W': 65.91}
-[40.27, 39.31, 39.28, 38.86, 41.16, 39.32, 39.02, 39.09, 38.81, 39.11, 39.9, 39.01, 39.35, 39.15, 39.0, 38.86, 39.09, 38.89, 38.89, 40.04]
-706.7499999999999
-35.33749999999999
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 237172, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.292231321334839, 'TIME_S_1KI': 0.04339564249293693, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.8344623732567, 'W': 65.91, 'J_1KI': 3.638011495342016, 'W_1KI': 0.2778995834246875, 'W_D': 30.572500000000005, 'J_D': 400.22768321812157, 'W_D_1KI': 0.12890433946671614, 'J_D_1KI': 0.0005435057235538602}
+[40.22, 39.25, 40.45, 42.27, 43.15, 39.86, 45.08, 39.41, 39.35, 39.02]
+[66.08]
+13.0941002368927
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 237686, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.452977180480957, 'TIME_S_1KI': 0.043978093705481, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 865.2581436538696, 'W': 66.08}
+[40.22, 39.25, 40.45, 42.27, 43.15, 39.86, 45.08, 39.41, 39.35, 39.02, 39.91, 38.89, 39.02, 44.29, 39.06, 39.59, 39.64, 38.87, 39.01, 39.59]
+726.56
+36.327999999999996
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 237686, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.452977180480957, 'TIME_S_1KI': 0.043978093705481, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 865.2581436538696, 'W': 66.08, 'J_1KI': 3.640341221838348, 'W_1KI': 0.2780138502057336, 'W_D': 29.752000000000002, 'J_D': 389.57567024803166, 'W_D_1KI': 0.12517354829480912, 'J_D_1KI': 0.0005266340814974762}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json
index deb9985..dec33aa 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 75716, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.424914360046387, "TIME_S_1KI": 0.1376844307682179, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 940.2084917426108, "W": 66.49, "J_1KI": 12.41756685169067, "W_1KI": 0.8781499286808601, "W_D": 31.17374999999999, "J_D": 440.81552819162596, "W_D_1KI": 0.4117194516350572, "J_D_1KI": 0.005437680960894093}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 68823, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.113624572753906, "TIME_S_1KI": 0.1469512310238424, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 874.6530011487007, "W": 66.63, "J_1KI": 12.708731109493929, "W_1KI": 0.9681356523255307, "W_D": 31.388249999999992, "J_D": 412.0340246631502, "W_D_1KI": 0.45607209799049725, "J_D_1KI": 0.006626739578200562}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output
index 169706c..d5877e6 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.02692556381225586}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.02646350860595703}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     22,  ...,  99984,  99990,
+tensor(crow_indices=tensor([     0,     10,     29,  ...,  99979,  99986,
                             100000]),
-       col_indices=tensor([ 947, 1869, 5338,  ..., 6268, 7050, 7942]),
-       values=tensor([0.2237, 0.7540, 0.0617,  ..., 0.6862, 0.3906, 0.7890]),
+       col_indices=tensor([ 215, 5321, 5688,  ..., 8927, 9338, 9590]),
+       values=tensor([0.5862, 0.8692, 0.4231,  ..., 0.9338, 0.8910, 0.1386]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6838, 0.4222, 0.9597,  ..., 0.5474, 0.0680, 0.5394])
+tensor([0.2934, 0.9397, 0.3873,  ..., 0.8200, 0.8199, 0.8958])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 0.02692556381225586 seconds
+Time: 0.02646350860595703 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '38996', '-ss', '10000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.952186584472656}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '39677', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 6.0533013343811035}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     11,     19,  ...,  99978,  99990,
+tensor(crow_indices=tensor([     0,     14,     24,  ...,  99988,  99991,
                             100000]),
-       col_indices=tensor([   7,  556,  703,  ..., 8117, 8865, 9056]),
-       values=tensor([0.2495, 0.4435, 0.2550,  ..., 0.5409, 0.7823, 0.3947]),
+       col_indices=tensor([ 978, 1764, 2285,  ..., 6145, 6169, 8642]),
+       values=tensor([0.7268, 0.3214, 0.9698,  ..., 0.0439, 0.1225, 0.5111]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8603, 0.6651, 0.2785,  ..., 0.2036, 0.7755, 0.1415])
+tensor([0.3012, 0.3329, 0.3234,  ..., 0.5299, 0.8683, 0.4760])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 5.952186584472656 seconds
+Time: 6.0533013343811035 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '68791', '-ss', '10000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.539567232131958}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '68823', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.113624572753906}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      9,     19,  ...,  99987,  99995,
+tensor(crow_indices=tensor([     0,      9,     16,  ...,  99982,  99993,
                             100000]),
-       col_indices=tensor([ 696,  997, 2062,  ..., 1211, 1590, 9690]),
-       values=tensor([0.0377, 0.1568, 0.2160,  ..., 0.8237, 0.6309, 0.0587]),
+       col_indices=tensor([1859, 2396, 2947,  ..., 5703, 6678, 8489]),
+       values=tensor([0.4084, 0.9747, 0.1669,  ..., 0.3937, 0.7409, 0.3744]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0823, 0.1873, 0.3356,  ..., 0.2591, 0.5771, 0.7059])
+tensor([0.3062, 0.5448, 0.1896,  ..., 0.4888, 0.7194, 0.5362])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,19 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 9.539567232131958 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '75716', '-ss', '10000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.424914360046387}
+Time: 10.113624572753906 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      9,     17,  ...,  99986,  99994,
+tensor(crow_indices=tensor([     0,      9,     16,  ...,  99982,  99993,
                             100000]),
-       col_indices=tensor([1284, 3776, 5103,  ..., 6955, 7171, 8445]),
-       values=tensor([0.9684, 0.2053, 0.3935,  ..., 0.8592, 0.0314, 0.3677]),
+       col_indices=tensor([1859, 2396, 2947,  ..., 5703, 6678, 8489]),
+       values=tensor([0.4084, 0.9747, 0.1669,  ..., 0.3937, 0.7409, 0.3744]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4306, 0.9725, 0.6597,  ..., 0.5969, 0.7821, 0.5134])
+tensor([0.3062, 0.5448, 0.1896,  ..., 0.4888, 0.7194, 0.5362])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -76,30 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.424914360046387 seconds
+Time: 10.113624572753906 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      9,     17,  ...,  99986,  99994,
-                            100000]),
-       col_indices=tensor([1284, 3776, 5103,  ..., 6955, 7171, 8445]),
-       values=tensor([0.9684, 0.2053, 0.3935,  ..., 0.8592, 0.0314, 0.3677]),
-       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4306, 0.9725, 0.6597,  ..., 0.5969, 0.7821, 0.5134])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 100000
-Density: 0.001
-Time: 10.424914360046387 seconds
-
-[40.22, 38.81, 38.98, 39.63, 38.95, 39.23, 38.84, 39.29, 39.31, 38.77]
-[66.49]
-14.140599966049194
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75716, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.424914360046387, 'TIME_S_1KI': 0.1376844307682179, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 940.2084917426108, 'W': 66.49}
-[40.22, 38.81, 38.98, 39.63, 38.95, 39.23, 38.84, 39.29, 39.31, 38.77, 41.54, 39.08, 39.06, 38.87, 39.0, 38.96, 39.2, 38.82, 39.06, 41.94]
-706.325
-35.316250000000004
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75716, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.424914360046387, 'TIME_S_1KI': 0.1376844307682179, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 940.2084917426108, 'W': 66.49, 'J_1KI': 12.41756685169067, 'W_1KI': 0.8781499286808601, 'W_D': 31.17374999999999, 'J_D': 440.81552819162596, 'W_D_1KI': 0.4117194516350572, 'J_D_1KI': 0.005437680960894093}
+[39.41, 38.89, 39.07, 39.08, 38.94, 39.24, 38.94, 39.57, 39.65, 39.28]
+[66.63]
+13.127014875411987
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 68823, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.113624572753906, 'TIME_S_1KI': 0.1469512310238424, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 874.6530011487007, 'W': 66.63}
+[39.41, 38.89, 39.07, 39.08, 38.94, 39.24, 38.94, 39.57, 39.65, 39.28, 39.77, 39.15, 39.52, 38.76, 38.91, 38.99, 38.81, 39.14, 39.28, 39.33]
+704.835
+35.24175
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 68823, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.113624572753906, 'TIME_S_1KI': 0.1469512310238424, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 874.6530011487007, 'W': 66.63, 'J_1KI': 12.708731109493929, 'W_1KI': 0.9681356523255307, 'W_D': 31.388249999999992, 'J_D': 412.0340246631502, 'W_D_1KI': 0.45607209799049725, 'J_D_1KI': 0.006626739578200562}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json
index db6444c..1feaf62 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 10206, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.48923373222351, "TIME_S_1KI": 1.0277516884404774, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 888.1208229660987, "W": 67.21, "J_1KI": 87.01948098825189, "W_1KI": 6.585341955712326, "W_D": 31.27349999999999, "J_D": 413.25169702470293, "W_D_1KI": 3.0642269253380356, "J_D_1KI": 0.30023779397785966}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 10198, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.494524955749512, "TIME_S_1KI": 1.0290767754216035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 885.1674857521058, "W": 66.76, "J_1KI": 86.79814529830415, "W_1KI": 6.546381643459502, "W_D": 31.493750000000006, "J_D": 417.5740488976241, "W_D_1KI": 3.0882280839380276, "J_D_1KI": 0.30282683702079105}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output
index 191d2ac..6557113 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.1162419319152832}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.11575102806091309}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     100,     185,  ...,  999774,
-                             999893, 1000000]),
-       col_indices=tensor([  36,  100,  149,  ..., 9802, 9836, 9872]),
-       values=tensor([0.2938, 0.2320, 0.9118,  ..., 0.8681, 0.8272, 0.2716]),
+tensor(crow_indices=tensor([      0,     102,     219,  ...,  999795,
+                             999887, 1000000]),
+       col_indices=tensor([ 239,  334,  368,  ..., 9762, 9953, 9977]),
+       values=tensor([0.6967, 0.7485, 0.2350,  ..., 0.4077, 0.3876, 0.6539]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9985, 0.1887, 0.5488,  ..., 0.6608, 0.9222, 0.7055])
+tensor([0.7350, 0.4858, 0.9581,  ..., 0.0149, 0.9326, 0.8066])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 0.1162419319152832 seconds
+Time: 0.11575102806091309 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '9032', '-ss', '10000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.29158329963684}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '9071', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.338836908340454}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      95,     205,  ...,  999782,
-                             999891, 1000000]),
-       col_indices=tensor([  54,  212,  264,  ..., 9693, 9804, 9961]),
-       values=tensor([0.9421, 0.7916, 0.1774,  ..., 0.7420, 0.5713, 0.3525]),
+tensor(crow_indices=tensor([      0,      87,     197,  ...,  999796,
+                             999896, 1000000]),
+       col_indices=tensor([  36,  195,  517,  ..., 9721, 9955, 9963]),
+       values=tensor([0.4399, 0.3851, 0.1811,  ..., 0.7063, 0.8034, 0.6024]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0899, 0.7410, 0.9990,  ..., 0.5022, 0.0295, 0.8248])
+tensor([0.8618, 0.7147, 0.8393,  ..., 0.8094, 0.9600, 0.5578])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 9.29158329963684 seconds
+Time: 9.338836908340454 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '10206', '-ss', '10000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.48923373222351}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '10198', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.494524955749512}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     108,     196,  ...,  999774,
-                             999884, 1000000]),
-       col_indices=tensor([  16,  259,  309,  ..., 9528, 9603, 9788]),
-       values=tensor([0.1649, 0.9890, 0.6907,  ..., 0.8956, 0.0145, 0.7596]),
+tensor(crow_indices=tensor([      0,     107,     223,  ...,  999793,
+                             999918, 1000000]),
+       col_indices=tensor([ 283,  671,  673,  ..., 9768, 9909, 9973]),
+       values=tensor([0.9594, 0.8276, 0.7039,  ..., 0.5970, 0.0478, 0.2843]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.7287, 0.8351, 0.4943,  ..., 0.5583, 0.1274, 0.9823])
+tensor([0.1523, 0.7589, 0.9241,  ..., 0.2822, 0.2741, 0.3703])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.48923373222351 seconds
+Time: 10.494524955749512 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     108,     196,  ...,  999774,
-                             999884, 1000000]),
-       col_indices=tensor([  16,  259,  309,  ..., 9528, 9603, 9788]),
-       values=tensor([0.1649, 0.9890, 0.6907,  ..., 0.8956, 0.0145, 0.7596]),
+tensor(crow_indices=tensor([      0,     107,     223,  ...,  999793,
+                             999918, 1000000]),
+       col_indices=tensor([ 283,  671,  673,  ..., 9768, 9909, 9973]),
+       values=tensor([0.9594, 0.8276, 0.7039,  ..., 0.5970, 0.0478, 0.2843]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.7287, 0.8351, 0.4943,  ..., 0.5583, 0.1274, 0.9823])
+tensor([0.1523, 0.7589, 0.9241,  ..., 0.2822, 0.2741, 0.3703])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.48923373222351 seconds
+Time: 10.494524955749512 seconds
 
-[39.9, 39.07, 39.52, 38.94, 39.35, 44.79, 39.52, 39.98, 39.47, 39.28]
-[67.21]
-13.214117288589478
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10206, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.48923373222351, 'TIME_S_1KI': 1.0277516884404774, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.1208229660987, 'W': 67.21}
-[39.9, 39.07, 39.52, 38.94, 39.35, 44.79, 39.52, 39.98, 39.47, 39.28, 40.29, 39.27, 43.98, 38.92, 39.16, 39.39, 39.32, 39.34, 39.08, 39.79]
-718.73
-35.9365
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10206, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.48923373222351, 'TIME_S_1KI': 1.0277516884404774, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.1208229660987, 'W': 67.21, 'J_1KI': 87.01948098825189, 'W_1KI': 6.585341955712326, 'W_D': 31.27349999999999, 'J_D': 413.25169702470293, 'W_D_1KI': 3.0642269253380356, 'J_D_1KI': 0.30023779397785966}
+[40.0, 39.04, 39.52, 39.47, 39.38, 38.92, 39.0, 38.85, 38.88, 38.94]
+[66.76]
+13.258949756622314
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10198, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.494524955749512, 'TIME_S_1KI': 1.0290767754216035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 885.1674857521058, 'W': 66.76}
+[40.0, 39.04, 39.52, 39.47, 39.38, 38.92, 39.0, 38.85, 38.88, 38.94, 40.44, 40.88, 39.28, 38.74, 38.95, 38.82, 38.85, 38.83, 38.87, 38.71]
+705.325
+35.26625
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10198, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.494524955749512, 'TIME_S_1KI': 1.0290767754216035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 885.1674857521058, 'W': 66.76, 'J_1KI': 86.79814529830415, 'W_1KI': 6.546381643459502, 'W_D': 31.493750000000006, 'J_D': 417.5740488976241, 'W_D_1KI': 3.0882280839380276, 'J_D_1KI': 0.30282683702079105}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json
index f94ac11..7714234 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1725, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.28298306465149, "TIME_S_1KI": 5.961149602696516, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1049.8662384581567, "W": 75.33, "J_1KI": 608.6181092511052, "W_1KI": 43.66956521739131, "W_D": 39.905, "J_D": 556.151762188673, "W_D_1KI": 23.133333333333336, "J_D_1KI": 13.410628019323674}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1726, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.259830713272095, "TIME_S_1KI": 5.944281989149533, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1045.9503919887543, "W": 74.94, "J_1KI": 605.9967508625459, "W_1KI": 43.418308227114714, "W_D": 39.623, "J_D": 553.0249850783348, "W_D_1KI": 22.956546929316335, "J_D_1KI": 13.300432751631712}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output
index bf02e51..365d68e 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.6085023880004883}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.608109712600708}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     508,     930,  ..., 4999014,
-                            4999519, 5000000]),
-       col_indices=tensor([  33,   44,   68,  ..., 9921, 9984, 9990]),
-       values=tensor([0.7535, 0.2308, 0.9086,  ..., 0.5781, 0.9835, 0.5048]),
+tensor(crow_indices=tensor([      0,     492,    1019,  ..., 4999012,
+                            4999504, 5000000]),
+       col_indices=tensor([   5,    7,  101,  ..., 9970, 9981, 9995]),
+       values=tensor([0.7093, 0.2660, 0.4595,  ..., 0.3475, 0.1260, 0.7746]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1644, 0.2567, 0.4067,  ..., 0.0618, 0.3860, 0.0437])
+tensor([0.0228, 0.9901, 0.9071,  ..., 0.5110, 0.0509, 0.6618])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 0.6085023880004883 seconds
+Time: 0.608109712600708 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1725', '-ss', '10000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.28298306465149}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1726', '-ss', '10000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.259830713272095}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     453,     934,  ..., 4998993,
-                            4999474, 5000000]),
-       col_indices=tensor([  76,   82,   85,  ..., 9960, 9963, 9989]),
-       values=tensor([0.2757, 0.2788, 0.5904,  ..., 0.0782, 0.3342, 0.9799]),
+tensor(crow_indices=tensor([      0,     494,    1017,  ..., 4999008,
+                            4999500, 5000000]),
+       col_indices=tensor([  10,   13,   31,  ..., 9910, 9981, 9994]),
+       values=tensor([0.9741, 0.2683, 0.9596,  ..., 0.6425, 0.6809, 0.5687]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.0884, 0.4732, 0.8375,  ..., 0.9901, 0.5525, 0.7748])
+tensor([0.4952, 0.1609, 0.9854,  ..., 0.4391, 0.0150, 0.6256])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.28298306465149 seconds
+Time: 10.259830713272095 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     453,     934,  ..., 4998993,
-                            4999474, 5000000]),
-       col_indices=tensor([  76,   82,   85,  ..., 9960, 9963, 9989]),
-       values=tensor([0.2757, 0.2788, 0.5904,  ..., 0.0782, 0.3342, 0.9799]),
+tensor(crow_indices=tensor([      0,     494,    1017,  ..., 4999008,
+                            4999500, 5000000]),
+       col_indices=tensor([  10,   13,   31,  ..., 9910, 9981, 9994]),
+       values=tensor([0.9741, 0.2683, 0.9596,  ..., 0.6425, 0.6809, 0.5687]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.0884, 0.4732, 0.8375,  ..., 0.9901, 0.5525, 0.7748])
+tensor([0.4952, 0.1609, 0.9854,  ..., 0.4391, 0.0150, 0.6256])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.28298306465149 seconds
+Time: 10.259830713272095 seconds
 
-[40.86, 38.91, 38.99, 39.35, 38.92, 38.87, 39.32, 39.52, 39.41, 41.56]
-[75.33]
-13.936894178390503
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1725, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.28298306465149, 'TIME_S_1KI': 5.961149602696516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.8662384581567, 'W': 75.33}
-[40.86, 38.91, 38.99, 39.35, 38.92, 38.87, 39.32, 39.52, 39.41, 41.56, 39.65, 38.96, 39.21, 38.88, 39.0, 40.01, 39.33, 39.71, 39.42, 39.31]
-708.5
-35.425
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1725, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.28298306465149, 'TIME_S_1KI': 5.961149602696516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.8662384581567, 'W': 75.33, 'J_1KI': 608.6181092511052, 'W_1KI': 43.66956521739131, 'W_D': 39.905, 'J_D': 556.151762188673, 'W_D_1KI': 23.133333333333336, 'J_D_1KI': 13.410628019323674}
+[39.52, 39.03, 40.16, 38.88, 39.0, 38.96, 38.96, 39.27, 39.19, 39.27]
+[74.94]
+13.957170963287354
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1726, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.259830713272095, 'TIME_S_1KI': 5.944281989149533, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1045.9503919887543, 'W': 74.94}
+[39.52, 39.03, 40.16, 38.88, 39.0, 38.96, 38.96, 39.27, 39.19, 39.27, 40.28, 39.41, 39.21, 39.32, 38.99, 38.96, 38.82, 40.16, 38.91, 39.15]
+706.34
+35.317
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1726, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.259830713272095, 'TIME_S_1KI': 5.944281989149533, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1045.9503919887543, 'W': 74.94, 'J_1KI': 605.9967508625459, 'W_1KI': 43.418308227114714, 'W_D': 39.623, 'J_D': 553.0249850783348, 'W_D_1KI': 22.956546929316335, 'J_D_1KI': 13.300432751631712}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.json
index 02b08b6..e2a1c88 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 700, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.428780555725098, "TIME_S_1KI": 14.89825793675014, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1164.5532444572448, "W": 76.66, "J_1KI": 1663.6474920817784, "W_1KI": 109.5142857142857, "W_D": 40.888999999999996, "J_D": 621.1507645788192, "W_D_1KI": 58.412857142857135, "J_D_1KI": 83.44693877551019}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 699, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.388232707977295, "TIME_S_1KI": 14.861563244602712, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1164.917454071045, "W": 77.04, "J_1KI": 1666.5485752089344, "W_1KI": 110.21459227467811, "W_D": 41.29950000000001, "J_D": 624.4873882970811, "W_D_1KI": 59.08369098712448, "J_D_1KI": 84.52602430203787}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.output
index f0881c0..6ddcbaf 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 1.498870611190796}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 1.5013470649719238}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      987,     1979,  ...,  9997999,
-                             9999011, 10000000]),
-       col_indices=tensor([   3,    7,   20,  ..., 9954, 9962, 9986]),
-       values=tensor([0.9369, 0.1464, 0.7342,  ..., 0.7208, 0.8895, 0.6454]),
+tensor(crow_indices=tensor([       0,     1032,     2050,  ...,  9997979,
+                             9998966, 10000000]),
+       col_indices=tensor([   2,    6,    9,  ..., 9983, 9984, 9996]),
+       values=tensor([0.6315, 0.3704, 0.1854,  ..., 0.7561, 0.4530, 0.9211]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.7880, 0.5272, 0.7128,  ..., 0.1762, 0.3407, 0.4321])
+tensor([0.8676, 0.2588, 0.7478,  ..., 0.7227, 0.1480, 0.9741])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 1.498870611190796 seconds
+Time: 1.5013470649719238 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '700', '-ss', '10000', '-sd', '0.1', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.428780555725098}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '699', '-ss', '10000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.388232707977295}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      995,     2027,  ...,  9997983,
-                             9998981, 10000000]),
-       col_indices=tensor([  16,   21,   33,  ..., 9977, 9983, 9988]),
-       values=tensor([0.3684, 0.6722, 0.7880,  ..., 0.5048, 0.0966, 0.9792]),
+tensor(crow_indices=tensor([       0,     1042,     2068,  ...,  9998016,
+                             9999002, 10000000]),
+       col_indices=tensor([  11,   14,   17,  ..., 9959, 9962, 9989]),
+       values=tensor([0.7592, 0.3608, 0.3582,  ..., 0.3496, 0.2524, 0.3186]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4422, 0.8800, 0.2165,  ..., 0.4558, 0.6103, 0.1393])
+tensor([0.2502, 0.4370, 0.1745,  ..., 0.0865, 0.5324, 0.5450])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 10.428780555725098 seconds
+Time: 10.388232707977295 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      995,     2027,  ...,  9997983,
-                             9998981, 10000000]),
-       col_indices=tensor([  16,   21,   33,  ..., 9977, 9983, 9988]),
-       values=tensor([0.3684, 0.6722, 0.7880,  ..., 0.5048, 0.0966, 0.9792]),
+tensor(crow_indices=tensor([       0,     1042,     2068,  ...,  9998016,
+                             9999002, 10000000]),
+       col_indices=tensor([  11,   14,   17,  ..., 9959, 9962, 9989]),
+       values=tensor([0.7592, 0.3608, 0.3582,  ..., 0.3496, 0.2524, 0.3186]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4422, 0.8800, 0.2165,  ..., 0.4558, 0.6103, 0.1393])
+tensor([0.2502, 0.4370, 0.1745,  ..., 0.0865, 0.5324, 0.5450])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 10.428780555725098 seconds
+Time: 10.388232707977295 seconds
 
-[40.18, 38.97, 39.04, 39.1, 38.99, 38.9, 38.95, 39.03, 44.22, 39.22]
-[76.66]
-15.191145896911621
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 700, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.428780555725098, 'TIME_S_1KI': 14.89825793675014, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1164.5532444572448, 'W': 76.66}
-[40.18, 38.97, 39.04, 39.1, 38.99, 38.9, 38.95, 39.03, 44.22, 39.22, 40.59, 39.18, 39.25, 44.66, 38.99, 39.16, 38.92, 38.8, 39.17, 40.19]
-715.42
-35.771
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 700, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.428780555725098, 'TIME_S_1KI': 14.89825793675014, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1164.5532444572448, 'W': 76.66, 'J_1KI': 1663.6474920817784, 'W_1KI': 109.5142857142857, 'W_D': 40.888999999999996, 'J_D': 621.1507645788192, 'W_D_1KI': 58.412857142857135, 'J_D_1KI': 83.44693877551019}
+[39.54, 44.4, 39.76, 39.67, 39.33, 39.67, 39.46, 40.09, 39.15, 38.9]
+[77.04]
+15.120943069458008
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 699, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.388232707977295, 'TIME_S_1KI': 14.861563244602712, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1164.917454071045, 'W': 77.04}
+[39.54, 44.4, 39.76, 39.67, 39.33, 39.67, 39.46, 40.09, 39.15, 38.9, 39.79, 39.54, 39.0, 39.44, 39.38, 39.32, 39.61, 38.94, 39.49, 38.89]
+714.81
+35.7405
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 699, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.388232707977295, 'TIME_S_1KI': 14.861563244602712, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1164.917454071045, 'W': 77.04, 'J_1KI': 1666.5485752089344, 'W_1KI': 110.21459227467811, 'W_D': 41.29950000000001, 'J_D': 624.4873882970811, 'W_D_1KI': 59.08369098712448, 'J_D_1KI': 84.52602430203787}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.json
index a0b1b52..f376b37 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 343, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.229193925857544, "TIME_S_1KI": 29.822722815911206, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1277.0280195808411, "W": 76.08, "J_1KI": 3723.1137597109073, "W_1KI": 221.8075801749271, "W_D": 40.66575, "J_D": 682.5880939441324, "W_D_1KI": 118.55903790087464, "J_D_1KI": 345.65317172266657}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 347, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.493296146392822, "TIME_S_1KI": 30.24004653139142, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1291.474730873108, "W": 76.2, "J_1KI": 3721.8291955997347, "W_1KI": 219.59654178674353, "W_D": 39.95825, "J_D": 677.2318919279576, "W_D_1KI": 115.15345821325649, "J_D_1KI": 331.8543464358977}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.output
index 9529016..2154422 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 3.0540103912353516}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 3.020411968231201}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2046,     4073,  ..., 19996013,
-                            19997969, 20000000]),
-       col_indices=tensor([   3,    8,   17,  ..., 9981, 9984, 9987]),
-       values=tensor([0.5017, 0.4094, 0.1287,  ..., 0.4741, 0.2195, 0.3916]),
+tensor(crow_indices=tensor([       0,     2001,     4067,  ..., 19995976,
+                            19997958, 20000000]),
+       col_indices=tensor([  14,   17,   24,  ..., 9980, 9989, 9991]),
+       values=tensor([0.6447, 0.5588, 0.5080,  ..., 0.9506, 0.2098, 0.9501]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.6824, 0.3340, 0.1820,  ..., 0.2779, 0.3641, 0.6445])
+tensor([0.6065, 0.5137, 0.6769,  ..., 0.0627, 0.1758, 0.0050])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 3.0540103912353516 seconds
+Time: 3.020411968231201 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '343', '-ss', '10000', '-sd', '0.2', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.229193925857544}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '347', '-ss', '10000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.493296146392822}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1942,     3919,  ..., 19996087,
-                            19998013, 20000000]),
-       col_indices=tensor([   9,   10,   17,  ..., 9985, 9988, 9989]),
-       values=tensor([0.3594, 0.3340, 0.0020,  ..., 0.8034, 0.9201, 0.1838]),
+tensor(crow_indices=tensor([       0,     2004,     4078,  ..., 19996056,
+                            19998039, 20000000]),
+       col_indices=tensor([   0,    5,   15,  ..., 9986, 9987, 9997]),
+       values=tensor([0.9556, 0.4662, 0.8624,  ..., 0.4531, 0.2100, 0.2819]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.0654, 0.1078, 0.4601,  ..., 0.8409, 0.3729, 0.1721])
+tensor([0.7299, 0.4019, 0.2401,  ..., 0.0700, 0.4893, 0.3168])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 10.229193925857544 seconds
+Time: 10.493296146392822 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1942,     3919,  ..., 19996087,
-                            19998013, 20000000]),
-       col_indices=tensor([   9,   10,   17,  ..., 9985, 9988, 9989]),
-       values=tensor([0.3594, 0.3340, 0.0020,  ..., 0.8034, 0.9201, 0.1838]),
+tensor(crow_indices=tensor([       0,     2004,     4078,  ..., 19996056,
+                            19998039, 20000000]),
+       col_indices=tensor([   0,    5,   15,  ..., 9986, 9987, 9997]),
+       values=tensor([0.9556, 0.4662, 0.8624,  ..., 0.4531, 0.2100, 0.2819]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.0654, 0.1078, 0.4601,  ..., 0.8409, 0.3729, 0.1721])
+tensor([0.7299, 0.4019, 0.2401,  ..., 0.0700, 0.4893, 0.3168])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 10.229193925857544 seconds
+Time: 10.493296146392822 seconds
 
-[39.53, 39.95, 39.42, 39.3, 39.16, 38.94, 39.07, 38.8, 39.46, 38.91]
-[76.08]
-16.78533148765564
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 343, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.229193925857544, 'TIME_S_1KI': 29.822722815911206, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1277.0280195808411, 'W': 76.08}
-[39.53, 39.95, 39.42, 39.3, 39.16, 38.94, 39.07, 38.8, 39.46, 38.91, 39.85, 38.96, 39.07, 39.05, 39.38, 38.91, 39.06, 39.15, 39.29, 44.34]
-708.285
-35.414249999999996
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 343, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.229193925857544, 'TIME_S_1KI': 29.822722815911206, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1277.0280195808411, 'W': 76.08, 'J_1KI': 3723.1137597109073, 'W_1KI': 221.8075801749271, 'W_D': 40.66575, 'J_D': 682.5880939441324, 'W_D_1KI': 118.55903790087464, 'J_D_1KI': 345.65317172266657}
+[40.29, 39.04, 38.94, 40.03, 39.28, 39.25, 38.97, 39.24, 43.24, 53.53]
+[76.2]
+16.948487281799316
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 347, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.493296146392822, 'TIME_S_1KI': 30.24004653139142, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1291.474730873108, 'W': 76.2}
+[40.29, 39.04, 38.94, 40.03, 39.28, 39.25, 38.97, 39.24, 43.24, 53.53, 39.99, 41.6, 41.99, 38.96, 39.44, 39.27, 39.32, 39.27, 40.66, 38.86]
+724.835
+36.24175
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 347, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.493296146392822, 'TIME_S_1KI': 30.24004653139142, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1291.474730873108, 'W': 76.2, 'J_1KI': 3721.8291955997347, 'W_1KI': 219.59654178674353, 'W_D': 39.95825, 'J_D': 677.2318919279576, 'W_D_1KI': 115.15345821325649, 'J_D_1KI': 331.8543464358977}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.json
index a1f4a32..6cb2bfc 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 233, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.295273542404175, "TIME_S_1KI": 44.18572335795783, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1458.0208019256593, "W": 76.48, "J_1KI": 6257.6000082646315, "W_1KI": 328.2403433476395, "W_D": 40.760999999999996, "J_D": 777.0709454405307, "W_D_1KI": 174.9399141630901, "J_D_1KI": 750.8150822450219}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 232, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.506343603134155, "TIME_S_1KI": 45.28596380661274, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1468.0002479553223, "W": 76.65, "J_1KI": 6327.587275669493, "W_1KI": 330.3879310344828, "W_D": 40.9075, "J_D": 783.4601453781128, "W_D_1KI": 176.32543103448276, "J_D_1KI": 760.0234096313912}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.output
index d790296..2622466 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 4.5049638748168945}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 4.508720636367798}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     3029,     6057,  ..., 29993789,
-                            29996881, 30000000]),
-       col_indices=tensor([   0,    1,    2,  ..., 9988, 9991, 9998]),
-       values=tensor([0.8599, 0.6300, 0.6697,  ..., 0.0214, 0.0757, 0.9206]),
+tensor(crow_indices=tensor([       0,     3017,     5967,  ..., 29993882,
+                            29996942, 30000000]),
+       col_indices=tensor([   5,    6,    9,  ..., 9995, 9996, 9997]),
+       values=tensor([0.9031, 0.7519, 0.5389,  ..., 0.9658, 0.5505, 0.5403]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.5404, 0.3446, 0.4295,  ..., 0.2969, 0.5137, 0.1316])
+tensor([0.1266, 0.8827, 0.7696,  ..., 0.4615, 0.0898, 0.8625])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 4.5049638748168945 seconds
+Time: 4.508720636367798 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '233', '-ss', '10000', '-sd', '0.3', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.295273542404175}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '232', '-ss', '10000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.506343603134155}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2988,     6007,  ..., 29993964,
-                            29997046, 30000000]),
-       col_indices=tensor([   9,   16,   24,  ..., 9996, 9997, 9999]),
-       values=tensor([0.2433, 0.7720, 0.0178,  ..., 0.3342, 0.8303, 0.6867]),
+tensor(crow_indices=tensor([       0,     3001,     5941,  ..., 29994005,
+                            29996945, 30000000]),
+       col_indices=tensor([   0,   12,   13,  ..., 9992, 9998, 9999]),
+       values=tensor([0.9006, 0.0187, 0.2224,  ..., 0.0969, 0.3958, 0.1522]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.4151, 0.6857, 0.4615,  ..., 0.0665, 0.4824, 0.1217])
+tensor([0.4529, 0.0055, 0.3353,  ..., 0.7911, 0.9251, 0.0376])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 10.295273542404175 seconds
+Time: 10.506343603134155 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2988,     6007,  ..., 29993964,
-                            29997046, 30000000]),
-       col_indices=tensor([   9,   16,   24,  ..., 9996, 9997, 9999]),
-       values=tensor([0.2433, 0.7720, 0.0178,  ..., 0.3342, 0.8303, 0.6867]),
+tensor(crow_indices=tensor([       0,     3001,     5941,  ..., 29994005,
+                            29996945, 30000000]),
+       col_indices=tensor([   0,   12,   13,  ..., 9992, 9998, 9999]),
+       values=tensor([0.9006, 0.0187, 0.2224,  ..., 0.0969, 0.3958, 0.1522]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.4151, 0.6857, 0.4615,  ..., 0.0665, 0.4824, 0.1217])
+tensor([0.4529, 0.0055, 0.3353,  ..., 0.7911, 0.9251, 0.0376])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 10.295273542404175 seconds
+Time: 10.506343603134155 seconds
 
-[40.6, 39.0, 39.72, 40.47, 39.9, 39.66, 39.13, 38.97, 39.1, 39.62]
-[76.48]
-19.064079523086548
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 233, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.295273542404175, 'TIME_S_1KI': 44.18572335795783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1458.0208019256593, 'W': 76.48}
-[40.6, 39.0, 39.72, 40.47, 39.9, 39.66, 39.13, 38.97, 39.1, 39.62, 40.89, 39.12, 39.47, 39.11, 38.93, 39.6, 38.9, 43.73, 39.55, 38.93]
-714.3800000000001
-35.71900000000001
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 233, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.295273542404175, 'TIME_S_1KI': 44.18572335795783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1458.0208019256593, 'W': 76.48, 'J_1KI': 6257.6000082646315, 'W_1KI': 328.2403433476395, 'W_D': 40.760999999999996, 'J_D': 777.0709454405307, 'W_D_1KI': 174.9399141630901, 'J_D_1KI': 750.8150822450219}
+[42.42, 39.55, 39.56, 39.28, 39.37, 39.05, 39.32, 38.9, 38.94, 44.02]
+[76.65]
+19.151992797851562
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 232, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.506343603134155, 'TIME_S_1KI': 45.28596380661274, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1468.0002479553223, 'W': 76.65}
+[42.42, 39.55, 39.56, 39.28, 39.37, 39.05, 39.32, 38.9, 38.94, 44.02, 44.66, 39.3, 39.73, 39.13, 39.41, 39.83, 39.61, 39.68, 39.17, 38.94]
+714.8500000000001
+35.74250000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 232, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.506343603134155, 'TIME_S_1KI': 45.28596380661274, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1468.0002479553223, 'W': 76.65, 'J_1KI': 6327.587275669493, 'W_1KI': 330.3879310344828, 'W_D': 40.9075, 'J_D': 783.4601453781128, 'W_D_1KI': 176.32543103448276, 'J_D_1KI': 760.0234096313912}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..bd3a181
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 176, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.287486791610718, "TIME_S_1KI": 58.45162949778817, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1621.081920976639, "W": 75.64, "J_1KI": 9210.692732821812, "W_1KI": 429.77272727272725, "W_D": 39.956, "J_D": 856.3187365751268, "W_D_1KI": 227.02272727272728, "J_D_1KI": 1289.9018595041323}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..898ee39
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.4.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.4', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 5.9488725662231445}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3981,     8065,  ..., 39992085,
+                            39996040, 40000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9994, 9997, 9999]),
+       values=tensor([0.2873, 0.1638, 0.8406,  ..., 0.3659, 0.7817, 0.0229]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.4990, 0.3865, 0.7076,  ..., 0.9531, 0.9187, 0.3072])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 5.9488725662231445 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '176', '-ss', '10000', '-sd', '0.4', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.287486791610718}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4082,     8140,  ..., 39991887,
+                            39995972, 40000000]),
+       col_indices=tensor([   0,    1,    6,  ..., 9993, 9996, 9997]),
+       values=tensor([0.9803, 0.3049, 0.7804,  ..., 0.2695, 0.0229, 0.9383]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.4744, 0.9806, 0.3276,  ..., 0.7259, 0.8906, 0.4456])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 10.287486791610718 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4082,     8140,  ..., 39991887,
+                            39995972, 40000000]),
+       col_indices=tensor([   0,    1,    6,  ..., 9993, 9996, 9997]),
+       values=tensor([0.9803, 0.3049, 0.7804,  ..., 0.2695, 0.0229, 0.9383]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.4744, 0.9806, 0.3276,  ..., 0.7259, 0.8906, 0.4456])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 10.287486791610718 seconds
+
+[39.92, 39.12, 45.04, 39.04, 40.25, 39.54, 39.53, 39.2, 39.47, 39.49]
+[75.64]
+21.431543111801147
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 176, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.287486791610718, 'TIME_S_1KI': 58.45162949778817, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1621.081920976639, 'W': 75.64}
+[39.92, 39.12, 45.04, 39.04, 40.25, 39.54, 39.53, 39.2, 39.47, 39.49, 40.02, 39.33, 39.41, 39.17, 38.95, 39.09, 39.39, 38.98, 39.01, 38.89]
+713.68
+35.684
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 176, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.287486791610718, 'TIME_S_1KI': 58.45162949778817, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1621.081920976639, 'W': 75.64, 'J_1KI': 9210.692732821812, 'W_1KI': 429.77272727272725, 'W_D': 39.956, 'J_D': 856.3187365751268, 'W_D_1KI': 227.02272727272728, 'J_D_1KI': 1289.9018595041323}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..c5c872d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 143, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.688217878341675, "TIME_S_1KI": 74.74278236602571, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2030.4548524188997, "W": 75.51, "J_1KI": 14198.984981950349, "W_1KI": 528.0419580419581, "W_D": 40.12375000000001, "J_D": 1078.9228298866751, "W_D_1KI": 280.5856643356644, "J_D_1KI": 1962.1375128368138}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..495a024
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.5.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.5', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 7.325762033462524}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4990,     9987,  ..., 49990040,
+                            49995005, 50000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9991, 9992, 9996]),
+       values=tensor([0.4301, 0.9179, 0.0090,  ..., 0.3709, 0.0037, 0.9527]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.4939, 0.2110, 0.5925,  ..., 0.4889, 0.3078, 0.7014])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 7.325762033462524 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '143', '-ss', '10000', '-sd', '0.5', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.688217878341675}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     5014,    10078,  ..., 49989929,
+                            49994973, 50000000]),
+       col_indices=tensor([   1,    2,    4,  ..., 9993, 9995, 9998]),
+       values=tensor([0.6119, 0.3838, 0.8264,  ..., 0.1450, 0.7688, 0.5541]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.4407, 0.3516, 0.4034,  ..., 0.4996, 0.2834, 0.2999])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 10.688217878341675 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     5014,    10078,  ..., 49989929,
+                            49994973, 50000000]),
+       col_indices=tensor([   1,    2,    4,  ..., 9993, 9995, 9998]),
+       values=tensor([0.6119, 0.3838, 0.8264,  ..., 0.1450, 0.7688, 0.5541]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.4407, 0.3516, 0.4034,  ..., 0.4996, 0.2834, 0.2999])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 10.688217878341675 seconds
+
+[39.74, 39.17, 39.93, 38.97, 39.15, 39.1, 39.19, 39.33, 39.49, 39.47]
+[75.51]
+26.889880180358887
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 143, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.688217878341675, 'TIME_S_1KI': 74.74278236602571, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2030.4548524188997, 'W': 75.51}
+[39.74, 39.17, 39.93, 38.97, 39.15, 39.1, 39.19, 39.33, 39.49, 39.47, 39.67, 39.11, 39.41, 38.97, 39.33, 39.11, 39.98, 39.12, 39.44, 38.97]
+707.7249999999999
+35.38625
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 143, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.688217878341675, 'TIME_S_1KI': 74.74278236602571, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2030.4548524188997, 'W': 75.51, 'J_1KI': 14198.984981950349, 'W_1KI': 528.0419580419581, 'W_D': 40.12375000000001, 'J_D': 1078.9228298866751, 'W_D_1KI': 280.5856643356644, 'J_D_1KI': 1962.1375128368138}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json
index 9c3e1bc..f0b8d48 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 362205, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.420795440673828, "TIME_S_1KI": 0.02877043508696409, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 858.7538476467132, "W": 65.88, "J_1KI": 2.3709055580312617, "W_1KI": 0.18188594856503915, "W_D": 30.515249999999995, "J_D": 397.7700113752484, "W_D_1KI": 0.08424856089783408, "J_D_1KI": 0.00023259911071860987}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 363895, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.603893041610718, "TIME_S_1KI": 0.02913998005361634, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 855.6058946228027, "W": 65.88, "J_1KI": 2.351243887997369, "W_1KI": 0.18104123442201733, "W_D": 29.987000000000002, "J_D": 389.45133518600466, "W_D_1KI": 0.08240563898926888, "J_D_1KI": 0.00022645444149897327}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output
index 3b7f750..0a09de2 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output
@@ -1,1131 +1,373 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.01423192024230957}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ...,  998,  999, 1000]),
-       col_indices=tensor([3651, 8143, 8284, 1201, 8802, 9084,  518, 1318, 7113,
-                           4198, 6659,  361, 3967, 2631,  475, 1422, 3709, 9745,
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-                           6075]),
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-       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.1442, 0.3479, 0.0736,  ..., 0.7713, 0.5566, 0.1675])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 0.01423192024230957 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '73777', '-ss', '10000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.1387269496917725}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
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-       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.0752, 0.0265, 0.5369,  ..., 0.1368, 0.6161, 0.4463])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 2.1387269496917725 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '362205', '-ss', '10000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.420795440673828}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.014298200607299805}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      4.5646e-01, 9.1031e-01, 7.1849e-01, 8.5889e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.8046, 0.6398, 0.4516,  ..., 0.6060, 0.1172, 0.9615])
+tensor([0.5663, 0.3065, 0.2094,  ..., 0.8818, 0.3165, 0.1028])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1133,375 +375,757 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.420795440673828 seconds
+Time: 0.014298200607299805 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '73435', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.1189255714416504}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ...,  998, 1000, 1000]),
+       col_indices=tensor([3268, 1940, 9160, 6136, 8886, 1331, 7256,  398, 9383,
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0103, 0.5434, 0.7885,  ..., 0.2340, 0.5166, 0.1015])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 2.1189255714416504 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '363895', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.603893041610718}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.8046, 0.6398, 0.4516,  ..., 0.6060, 0.1172, 0.9615])
+tensor([0.6225, 0.9544, 0.4026,  ..., 0.0448, 0.5411, 0.3638])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1509,13 +1133,389 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.420795440673828 seconds
+Time: 10.603893041610718 seconds
 
-[40.1, 39.18, 39.44, 38.84, 39.26, 38.78, 38.88, 39.27, 39.5, 39.18]
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      6.1953e-01, 8.9656e-01, 1.5362e-01, 4.5501e-01,
+                      1.1305e-01, 1.5899e-01, 7.4418e-01, 8.8016e-01,
+                      4.7632e-01, 9.5522e-01, 4.7101e-01, 2.8447e-01,
+                      3.8704e-01, 8.4621e-01, 5.5945e-01, 5.1588e-02,
+                      7.6581e-02, 1.0814e-01, 6.1450e-01, 2.6132e-02,
+                      6.9289e-01, 1.3583e-01, 9.2203e-01, 2.6151e-02,
+                      2.8007e-01, 5.5882e-01, 6.1010e-02, 2.0485e-01,
+                      7.9007e-01, 1.2710e-01, 5.2246e-01, 1.4720e-01,
+                      9.0733e-02, 4.9059e-01, 5.5002e-01, 6.9097e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.6225, 0.9544, 0.4026,  ..., 0.0448, 0.5411, 0.3638])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.603893041610718 seconds
+
+[40.44, 38.96, 39.19, 39.04, 39.08, 44.06, 39.06, 39.22, 39.35, 39.22]
 [65.88]
-13.035122156143188
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 362205, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.420795440673828, 'TIME_S_1KI': 0.02877043508696409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.7538476467132, 'W': 65.88}
-[40.1, 39.18, 39.44, 38.84, 39.26, 38.78, 38.88, 39.27, 39.5, 39.18, 39.55, 39.36, 39.34, 38.83, 39.02, 39.04, 38.92, 39.01, 41.73, 38.96]
-707.295
-35.36475
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 362205, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.420795440673828, 'TIME_S_1KI': 0.02877043508696409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.7538476467132, 'W': 65.88, 'J_1KI': 2.3709055580312617, 'W_1KI': 0.18188594856503915, 'W_D': 30.515249999999995, 'J_D': 397.7700113752484, 'W_D_1KI': 0.08424856089783408, 'J_D_1KI': 0.00023259911071860987}
+12.98733901977539
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 363895, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.603893041610718, 'TIME_S_1KI': 0.02913998005361634, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.6058946228027, 'W': 65.88}
+[40.44, 38.96, 39.19, 39.04, 39.08, 44.06, 39.06, 39.22, 39.35, 39.22, 40.11, 39.4, 44.83, 38.97, 39.52, 39.43, 38.91, 39.25, 40.22, 38.97]
+717.8599999999999
+35.892999999999994
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 363895, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.603893041610718, 'TIME_S_1KI': 0.02913998005361634, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.6058946228027, 'W': 65.88, 'J_1KI': 2.351243887997369, 'W_1KI': 0.18104123442201733, 'W_D': 29.987000000000002, 'J_D': 389.45133518600466, 'W_D_1KI': 0.08240563898926888, 'J_D_1KI': 0.00022645444149897327}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.json
index 44b0019..4e28c71 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 288650, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.542811155319214, "TIME_S_1KI": 0.036524549299564224, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 872.9266104698181, "W": 66.0, "J_1KI": 3.0241697920312425, "W_1KI": 0.22865061493157804, "W_D": 30.442750000000004, "J_D": 402.64070561939485, "W_D_1KI": 0.10546596223800452, "J_D_1KI": 0.00036537662303136846}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 288310, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.569395542144775, "TIME_S_1KI": 0.03665982984337961, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 854.3347172141076, "W": 65.79, "J_1KI": 2.9632503805421515, "W_1KI": 0.22819187679927858, "W_D": 30.430750000000003, "J_D": 395.167140839994, "W_D_1KI": 0.10554871492490722, "J_D_1KI": 0.0003660945334012252}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.output
index ec44bfd..917d1d1 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.014984130859375}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.01482248306274414}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    2,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([5455, 6096, 7620,  ..., 8334, 1515, 9556]),
-       values=tensor([0.3295, 0.9699, 0.1085,  ..., 0.1358, 0.2338, 0.9968]),
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 4998, 4999, 5000]),
+       col_indices=tensor([9316, 7684, 6114,  ..., 4797, 7259, 5250]),
+       values=tensor([0.2419, 0.8047, 0.2870,  ..., 0.0548, 0.5069, 0.3709]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.0525, 0.2160, 0.5197,  ..., 0.9729, 0.0490, 0.2973])
+tensor([0.9532, 0.7093, 0.0307,  ..., 0.3681, 0.3122, 0.7966])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 0.014984130859375 seconds
+Time: 0.01482248306274414 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '70074', '-ss', '10000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.549025535583496}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '70838', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.579850435256958}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([3016, 6372, 3284,  ..., 9865, 6936, 5486]),
-       values=tensor([0.2981, 0.0450, 0.6145,  ..., 0.3998, 0.9695, 0.2536]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([1876, 3469, 1702,  ..., 5400, 8275, 8211]),
+       values=tensor([0.2455, 0.2245, 0.5061,  ..., 0.2422, 0.9251, 0.2949]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.7165, 0.5810, 0.9668,  ..., 0.2745, 0.2690, 0.0815])
+tensor([0.1745, 0.7196, 0.3176,  ..., 0.6620, 0.3214, 0.1312])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 2.549025535583496 seconds
+Time: 2.579850435256958 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '288650', '-ss', '10000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.542811155319214}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '288310', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.569395542144775}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([4532, 8082, 1862,  ..., 2662, 2473, 4062]),
-       values=tensor([0.2290, 0.0977, 0.7273,  ..., 0.3334, 0.1586, 0.6128]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4998, 4998, 5000]),
+       col_indices=tensor([1328, 8485, 8629,  ..., 6703, 7772, 8456]),
+       values=tensor([0.6066, 0.9936, 0.2970,  ..., 0.6397, 0.3202, 0.6133]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.0256, 0.2861, 0.7976,  ..., 0.1212, 0.6310, 0.3680])
+tensor([0.1682, 0.1407, 0.6835,  ..., 0.2839, 0.3386, 0.4473])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.542811155319214 seconds
+Time: 10.569395542144775 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([4532, 8082, 1862,  ..., 2662, 2473, 4062]),
-       values=tensor([0.2290, 0.0977, 0.7273,  ..., 0.3334, 0.1586, 0.6128]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4998, 4998, 5000]),
+       col_indices=tensor([1328, 8485, 8629,  ..., 6703, 7772, 8456]),
+       values=tensor([0.6066, 0.9936, 0.2970,  ..., 0.6397, 0.3202, 0.6133]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.0256, 0.2861, 0.7976,  ..., 0.1212, 0.6310, 0.3680])
+tensor([0.1682, 0.1407, 0.6835,  ..., 0.2839, 0.3386, 0.4473])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.542811155319214 seconds
+Time: 10.569395542144775 seconds
 
-[40.67, 38.88, 39.03, 38.97, 39.55, 38.85, 39.41, 38.82, 39.41, 38.86]
-[66.0]
-13.226160764694214
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 288650, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.542811155319214, 'TIME_S_1KI': 0.036524549299564224, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 872.9266104698181, 'W': 66.0}
-[40.67, 38.88, 39.03, 38.97, 39.55, 38.85, 39.41, 38.82, 39.41, 38.86, 39.72, 38.82, 38.97, 39.27, 39.27, 39.76, 38.89, 38.89, 45.01, 39.44]
-711.145
-35.557249999999996
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 288650, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.542811155319214, 'TIME_S_1KI': 0.036524549299564224, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 872.9266104698181, 'W': 66.0, 'J_1KI': 3.0241697920312425, 'W_1KI': 0.22865061493157804, 'W_D': 30.442750000000004, 'J_D': 402.64070561939485, 'W_D_1KI': 0.10546596223800452, 'J_D_1KI': 0.00036537662303136846}
+[40.32, 38.86, 38.98, 39.06, 39.07, 38.89, 40.67, 39.24, 39.32, 39.29]
+[65.79]
+12.985783815383911
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 288310, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.569395542144775, 'TIME_S_1KI': 0.03665982984337961, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 854.3347172141076, 'W': 65.79}
+[40.32, 38.86, 38.98, 39.06, 39.07, 38.89, 40.67, 39.24, 39.32, 39.29, 40.79, 39.23, 38.81, 40.07, 38.85, 38.94, 39.21, 39.01, 39.18, 39.19]
+707.1850000000001
+35.35925
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 288310, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.569395542144775, 'TIME_S_1KI': 0.03665982984337961, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 854.3347172141076, 'W': 65.79, 'J_1KI': 2.9632503805421515, 'W_1KI': 0.22819187679927858, 'W_D': 30.430750000000003, 'J_D': 395.167140839994, 'W_D_1KI': 0.10554871492490722, 'J_D_1KI': 0.0003660945334012252}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.json
index c479701..ca5cee1 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 203, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.8138906955719, "TIME_S_1KI": 53.27039751513251, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1609.7031775331498, "W": 81.98, "J_1KI": 7929.572303118964, "W_1KI": 403.8423645320197, "W_D": 30.52650000000002, "J_D": 599.3974633930925, "W_D_1KI": 150.37684729064048, "J_D_1KI": 740.7726467519235}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 196, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.51739501953125, "TIME_S_1KI": 53.66017867107781, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1496.0578999996185, "W": 76.6, "J_1KI": 7632.9484693858085, "W_1KI": 390.81632653061223, "W_D": 40.777249999999995, "J_D": 796.4115796704888, "W_D_1KI": 208.047193877551, "J_D_1KI": 1061.4652748854642}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.output
index e1438f6..23fce00 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.424488306045532}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.346543788909912}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       44,       99,  ..., 24999917,
-                            24999946, 25000000]),
-       col_indices=tensor([  4827,  10869,  14232,  ..., 471243, 483745,
-                           496563]),
-       values=tensor([0.8207, 0.6147, 0.2995,  ..., 0.3197, 0.5880, 0.5650]),
+tensor(crow_indices=tensor([       0,       44,      102,  ..., 24999894,
+                            24999947, 25000000]),
+       col_indices=tensor([ 15535,  26957,  66973,  ..., 491167, 491900,
+                           494241]),
+       values=tensor([0.1063, 0.6792, 0.3672,  ..., 0.6022, 0.3872, 0.3278]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.7008, 0.9045, 0.7559,  ..., 0.2377, 0.3193, 0.3380])
+tensor([0.6784, 0.4374, 0.6493,  ..., 0.2948, 0.2498, 0.9677])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 5.424488306045532 seconds
+Time: 5.346543788909912 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '193', '-ss', '500000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.982287168502808}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '196', '-ss', '500000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.51739501953125}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       53,      103,  ..., 24999914,
-                            24999954, 25000000]),
-       col_indices=tensor([   956,  25275,  30712,  ..., 470941, 489379,
-                           489461]),
-       values=tensor([0.2897, 0.9352, 0.3996,  ..., 0.3187, 0.8556, 0.7054]),
+tensor(crow_indices=tensor([       0,       41,       93,  ..., 24999884,
+                            24999939, 25000000]),
+       col_indices=tensor([  6721,  21503,  60987,  ..., 477439, 477750,
+                           498940]),
+       values=tensor([0.1736, 0.2933, 0.3189,  ..., 0.5685, 0.2838, 0.4704]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.6888, 0.9226, 0.2376,  ..., 0.2155, 0.1168, 0.1817])
+tensor([0.1626, 0.9151, 0.1900,  ..., 0.6685, 0.4369, 0.9470])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,20 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 9.982287168502808 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '203', '-ss', '500000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.8138906955719}
+Time: 10.51739501953125 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       60,      106,  ..., 24999892,
-                            24999946, 25000000]),
-       col_indices=tensor([ 18694,  24514,  28811,  ..., 477104, 482132,
-                           483877]),
-       values=tensor([0.0999, 0.2209, 0.5662,  ..., 0.8643, 0.1918, 0.8434]),
+tensor(crow_indices=tensor([       0,       41,       93,  ..., 24999884,
+                            24999939, 25000000]),
+       col_indices=tensor([  6721,  21503,  60987,  ..., 477439, 477750,
+                           498940]),
+       values=tensor([0.1736, 0.2933, 0.3189,  ..., 0.5685, 0.2838, 0.4704]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.7305, 0.0261, 0.0866,  ..., 0.4657, 0.9743, 0.1757])
+tensor([0.1626, 0.9151, 0.1900,  ..., 0.6685, 0.4369, 0.9470])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -59,31 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 10.8138906955719 seconds
+Time: 10.51739501953125 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       60,      106,  ..., 24999892,
-                            24999946, 25000000]),
-       col_indices=tensor([ 18694,  24514,  28811,  ..., 477104, 482132,
-                           483877]),
-       values=tensor([0.0999, 0.2209, 0.5662,  ..., 0.8643, 0.1918, 0.8434]),
-       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.7305, 0.0261, 0.0866,  ..., 0.4657, 0.9743, 0.1757])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 25000000
-Density: 0.0001
-Time: 10.8138906955719 seconds
-
-[48.25, 66.08, 64.62, 65.48, 60.76, 53.27, 65.63, 72.69, 68.45, 64.01]
-[81.98]
-19.635315656661987
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 203, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.8138906955719, 'TIME_S_1KI': 53.27039751513251, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1609.7031775331498, 'W': 81.98}
-[48.25, 66.08, 64.62, 65.48, 60.76, 53.27, 65.63, 72.69, 68.45, 64.01, 67.95, 64.4, 68.38, 60.78, 45.98, 39.37, 40.58, 39.5, 41.17, 43.65]
-1029.0699999999997
-51.453499999999984
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 203, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.8138906955719, 'TIME_S_1KI': 53.27039751513251, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1609.7031775331498, 'W': 81.98, 'J_1KI': 7929.572303118964, 'W_1KI': 403.8423645320197, 'W_D': 30.52650000000002, 'J_D': 599.3974633930925, 'W_D_1KI': 150.37684729064048, 'J_D_1KI': 740.7726467519235}
+[40.69, 39.06, 39.39, 44.33, 39.17, 39.34, 39.99, 39.57, 39.73, 39.44]
+[76.6]
+19.530781984329224
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 196, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.51739501953125, 'TIME_S_1KI': 53.66017867107781, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1496.0578999996185, 'W': 76.6}
+[40.69, 39.06, 39.39, 44.33, 39.17, 39.34, 39.99, 39.57, 39.73, 39.44, 39.7, 39.13, 39.59, 39.43, 39.58, 39.07, 40.03, 39.42, 39.65, 40.12]
+716.4549999999999
+35.82275
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 196, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.51739501953125, 'TIME_S_1KI': 53.66017867107781, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1496.0578999996185, 'W': 76.6, 'J_1KI': 7632.9484693858085, 'W_1KI': 390.81632653061223, 'W_D': 40.777249999999995, 'J_D': 796.4115796704888, 'W_D_1KI': 208.047193877551, 'J_D_1KI': 1061.4652748854642}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json
index 9445831..212baaa 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1357, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.301345348358154, "TIME_S_1KI": 7.591264073955898, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 974.2936608052254, "W": 72.47, "J_1KI": 717.9761686110725, "W_1KI": 53.404568901989684, "W_D": 37.03175, "J_D": 497.85841415101294, "W_D_1KI": 27.28942520265291, "J_D_1KI": 20.11011437188866}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1357, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.248650312423706, "TIME_S_1KI": 7.5524320651611685, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1022.5026794672012, "W": 76.5, "J_1KI": 753.5023430119389, "W_1KI": 56.37435519528371, "W_D": 40.9355, "J_D": 547.1458618997335, "W_D_1KI": 30.166175386882827, "J_D_1KI": 22.23004818488049}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output
index d835f69..72e126c 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.7734920978546143}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.7732758522033691}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       4,      10,  ..., 2499990,
+tensor(crow_indices=tensor([      0,       2,       8,  ..., 2499985,
                             2499995, 2500000]),
-       col_indices=tensor([ 99860, 161360, 168008,  ..., 375780, 443860,
-                           468048]),
-       values=tensor([0.7731, 0.7975, 0.7314,  ..., 0.7653, 0.4860, 0.2739]),
+       col_indices=tensor([165838, 337341,  38617,  ..., 154134, 161638,
+                           492399]),
+       values=tensor([0.6433, 0.5779, 0.8791,  ..., 0.7984, 0.6408, 0.2732]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.9501, 0.6169, 0.8449,  ..., 0.9228, 0.9726, 0.5004])
+tensor([0.3555, 0.2811, 0.1161,  ..., 0.1603, 0.0646, 0.8128])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 0.7734920978546143 seconds
+Time: 0.7732758522033691 seconds
 
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1357', '-ss', '500000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.301345348358154}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.248650312423706}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       2,      10,  ..., 2499991,
-                            2499994, 2500000]),
-       col_indices=tensor([238281, 305722, 262347,  ..., 326599, 364388,
-                           410788]),
-       values=tensor([0.2261, 0.8621, 0.1222,  ..., 0.7643, 0.7262, 0.6796]),
+tensor(crow_indices=tensor([      0,       7,      10,  ..., 2499987,
+                            2499995, 2500000]),
+       col_indices=tensor([ 15665, 113136, 205303,  ..., 435688, 452170,
+                           496606]),
+       values=tensor([0.2643, 0.1016, 0.1243,  ..., 0.0819, 0.7361, 0.8109]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7756, 0.5142, 0.7476,  ..., 0.1970, 0.9731, 0.3396])
+tensor([0.7385, 0.0080, 0.1289,  ..., 0.5753, 0.7010, 0.8136])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.301345348358154 seconds
+Time: 10.248650312423706 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       2,      10,  ..., 2499991,
-                            2499994, 2500000]),
-       col_indices=tensor([238281, 305722, 262347,  ..., 326599, 364388,
-                           410788]),
-       values=tensor([0.2261, 0.8621, 0.1222,  ..., 0.7643, 0.7262, 0.6796]),
+tensor(crow_indices=tensor([      0,       7,      10,  ..., 2499987,
+                            2499995, 2500000]),
+       col_indices=tensor([ 15665, 113136, 205303,  ..., 435688, 452170,
+                           496606]),
+       values=tensor([0.2643, 0.1016, 0.1243,  ..., 0.0819, 0.7361, 0.8109]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7756, 0.5142, 0.7476,  ..., 0.1970, 0.9731, 0.3396])
+tensor([0.7385, 0.0080, 0.1289,  ..., 0.5753, 0.7010, 0.8136])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.301345348358154 seconds
+Time: 10.248650312423706 seconds
 
-[40.45, 39.15, 39.07, 39.07, 39.08, 39.31, 39.47, 39.12, 39.58, 39.08]
-[72.47]
-13.444096326828003
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.301345348358154, 'TIME_S_1KI': 7.591264073955898, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 974.2936608052254, 'W': 72.47}
-[40.45, 39.15, 39.07, 39.07, 39.08, 39.31, 39.47, 39.12, 39.58, 39.08, 39.87, 38.95, 39.89, 38.93, 39.13, 39.06, 39.33, 39.5, 40.16, 40.53]
-708.7649999999999
-35.43825
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.301345348358154, 'TIME_S_1KI': 7.591264073955898, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 974.2936608052254, 'W': 72.47, 'J_1KI': 717.9761686110725, 'W_1KI': 53.404568901989684, 'W_D': 37.03175, 'J_D': 497.85841415101294, 'W_D_1KI': 27.28942520265291, 'J_D_1KI': 20.11011437188866}
+[40.95, 39.1, 39.07, 39.06, 39.74, 39.13, 39.28, 40.31, 39.91, 39.48]
+[76.5]
+13.366048097610474
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.248650312423706, 'TIME_S_1KI': 7.5524320651611685, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1022.5026794672012, 'W': 76.5}
+[40.95, 39.1, 39.07, 39.06, 39.74, 39.13, 39.28, 40.31, 39.91, 39.48, 40.65, 39.55, 39.56, 40.76, 39.09, 39.0, 39.34, 39.02, 39.1, 39.46]
+711.2900000000001
+35.5645
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.248650312423706, 'TIME_S_1KI': 7.5524320651611685, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1022.5026794672012, 'W': 76.5, 'J_1KI': 753.5023430119389, 'W_1KI': 56.37435519528371, 'W_D': 40.9355, 'J_D': 547.1458618997335, 'W_D_1KI': 30.166175386882827, 'J_D_1KI': 22.23004818488049}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.json
index 016557b..e33888d 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 374, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.481700897216797, "TIME_S_1KI": 28.025938227852397, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1245.1822829723358, "W": 76.84, "J_1KI": 3329.3643929741597, "W_1KI": 205.45454545454547, "W_D": 40.990750000000006, "J_D": 664.2498134532572, "W_D_1KI": 109.60093582887701, "J_D_1KI": 293.0506305584947}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 367, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.611295223236084, "TIME_S_1KI": 28.91361096249614, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1245.9440638375281, "W": 76.77, "J_1KI": 3394.9429532357717, "W_1KI": 209.1825613079019, "W_D": 41.03874999999999, "J_D": 666.041252439916, "W_D_1KI": 111.82220708446864, "J_D_1KI": 304.69266235550043}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.output
index 9fc6dc1..57a96f1 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.8034374713897705}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.85378098487854}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       31,       56,  ..., 12499945,
-                            12499972, 12500000]),
-       col_indices=tensor([ 16534,  21956,  27589,  ..., 400032, 455487,
-                           480702]),
-       values=tensor([0.5221, 0.3710, 0.3411,  ..., 0.2701, 0.3669, 0.2928]),
+tensor(crow_indices=tensor([       0,       27,       47,  ..., 12499954,
+                            12499979, 12500000]),
+       col_indices=tensor([  3727,   4076,   5662,  ..., 478918, 481556,
+                           488053]),
+       values=tensor([0.7295, 0.5074, 0.0514,  ..., 0.7151, 0.3203, 0.1737]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.5983, 0.4656, 0.7235,  ..., 0.5590, 0.7340, 0.5167])
+tensor([0.3494, 0.9842, 0.6199,  ..., 0.2555, 0.1047, 0.7043])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 2.8034374713897705 seconds
+Time: 2.85378098487854 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '374', '-ss', '500000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.481700897216797}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '367', '-ss', '500000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.611295223236084}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       20,       47,  ..., 12499941,
-                            12499971, 12500000]),
-       col_indices=tensor([ 37298,  48174,  79945,  ..., 425979, 429124,
-                           477898]),
-       values=tensor([0.8892, 0.8073, 0.3867,  ..., 0.6750, 0.3130, 0.8587]),
+tensor(crow_indices=tensor([       0,       22,       47,  ..., 12499952,
+                            12499976, 12500000]),
+       col_indices=tensor([ 40600,  52241,  60280,  ..., 462159, 480272,
+                           492405]),
+       values=tensor([0.1478, 0.5447, 0.8572,  ..., 0.8924, 0.4722, 0.1649]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.8772, 0.7830, 0.9014,  ..., 0.3941, 0.0151, 0.6871])
+tensor([0.6153, 0.4540, 0.3734,  ..., 0.2866, 0.0219, 0.2153])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 10.481700897216797 seconds
+Time: 10.611295223236084 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       20,       47,  ..., 12499941,
-                            12499971, 12500000]),
-       col_indices=tensor([ 37298,  48174,  79945,  ..., 425979, 429124,
-                           477898]),
-       values=tensor([0.8892, 0.8073, 0.3867,  ..., 0.6750, 0.3130, 0.8587]),
+tensor(crow_indices=tensor([       0,       22,       47,  ..., 12499952,
+                            12499976, 12500000]),
+       col_indices=tensor([ 40600,  52241,  60280,  ..., 462159, 480272,
+                           492405]),
+       values=tensor([0.1478, 0.5447, 0.8572,  ..., 0.8924, 0.4722, 0.1649]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.8772, 0.7830, 0.9014,  ..., 0.3941, 0.0151, 0.6871])
+tensor([0.6153, 0.4540, 0.3734,  ..., 0.2866, 0.0219, 0.2153])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 10.481700897216797 seconds
+Time: 10.611295223236084 seconds
 
-[39.77, 39.61, 39.09, 39.46, 39.43, 39.2, 39.07, 38.94, 44.28, 39.72]
-[76.84]
-16.20487093925476
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 374, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.481700897216797, 'TIME_S_1KI': 28.025938227852397, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1245.1822829723358, 'W': 76.84}
-[39.77, 39.61, 39.09, 39.46, 39.43, 39.2, 39.07, 38.94, 44.28, 39.72, 40.11, 44.49, 39.49, 38.95, 39.21, 39.06, 39.21, 38.96, 39.02, 39.43]
-716.9849999999999
-35.84925
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 374, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.481700897216797, 'TIME_S_1KI': 28.025938227852397, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1245.1822829723358, 'W': 76.84, 'J_1KI': 3329.3643929741597, 'W_1KI': 205.45454545454547, 'W_D': 40.990750000000006, 'J_D': 664.2498134532572, 'W_D_1KI': 109.60093582887701, 'J_D_1KI': 293.0506305584947}
+[39.76, 39.03, 39.59, 44.84, 39.46, 39.92, 39.47, 39.26, 39.26, 38.97]
+[76.77]
+16.229569673538208
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 367, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.611295223236084, 'TIME_S_1KI': 28.91361096249614, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1245.9440638375281, 'W': 76.77}
+[39.76, 39.03, 39.59, 44.84, 39.46, 39.92, 39.47, 39.26, 39.26, 38.97, 39.57, 39.12, 39.61, 41.28, 39.15, 38.89, 39.25, 38.84, 38.93, 39.15]
+714.625
+35.73125
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 367, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.611295223236084, 'TIME_S_1KI': 28.91361096249614, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1245.9440638375281, 'W': 76.77, 'J_1KI': 3394.9429532357717, 'W_1KI': 209.1825613079019, 'W_D': 41.03874999999999, 'J_D': 666.041252439916, 'W_D_1KI': 111.82220708446864, 'J_D_1KI': 304.69266235550043}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json
index e80dbeb..131a7d9 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 15655, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.832781791687012, "TIME_S_1KI": 0.691969453317599, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 866.9169475746155, "W": 65.84, "J_1KI": 55.37636202967841, "W_1KI": 4.205685084637497, "W_D": 30.269999999999996, "J_D": 398.5658566689491, "W_D_1KI": 1.933567550303417, "J_D_1KI": 0.12351118175045782}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 15623, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.467512369155884, "TIME_S_1KI": 0.6700065524646921, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 863.5877960205078, "W": 65.57, "J_1KI": 55.27669436219085, "W_1KI": 4.197017218203929, "W_D": 29.687999999999995, "J_D": 391.0049487304687, "W_D_1KI": 1.900275235230109, "J_D_1KI": 0.12163318410229208}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output
index da8990f..90bfa00 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.08056378364562988}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.08077287673950195}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      7,  ..., 249990, 249996,
+tensor(crow_indices=tensor([     0,      1,      5,  ..., 249990, 249993,
                             250000]),
-       col_indices=tensor([28795, 30379, 41102,  ...,  5633,  6424, 22447]),
-       values=tensor([0.9841, 0.4564, 0.4138,  ..., 0.4352, 0.7831, 0.6427]),
+       col_indices=tensor([49667,  2847, 11171,  ..., 33210, 37627, 43823]),
+       values=tensor([0.9038, 0.9262, 0.9323,  ..., 0.5485, 0.0953, 0.5119]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.6096, 0.2856, 0.5951,  ..., 0.5564, 0.0665, 0.9869])
+tensor([0.9974, 0.7759, 0.0777,  ..., 0.5009, 0.8013, 0.6672])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 0.08056378364562988 seconds
+Time: 0.08077287673950195 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '13033', '-ss', '50000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.741129398345947}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12999', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.735981464385986}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,     13,  ..., 249987, 249991,
+tensor(crow_indices=tensor([     0,      8,     10,  ..., 249991, 249996,
                             250000]),
-       col_indices=tensor([  370,  1086,  2786,  ..., 43615, 44396, 45243]),
-       values=tensor([0.9664, 0.8693, 0.7422,  ..., 0.8293, 0.8225, 0.1476]),
+       col_indices=tensor([ 1241,  1316, 11333,  ..., 39099, 41509, 45214]),
+       values=tensor([0.5750, 0.7297, 0.2943,  ..., 0.4119, 0.3205, 0.0800]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.9116, 0.5607, 0.8635,  ..., 0.8139, 0.6651, 0.4589])
+tensor([0.2599, 0.8475, 0.1715,  ..., 0.6309, 0.7959, 0.3593])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 8.741129398345947 seconds
+Time: 8.735981464385986 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15655', '-ss', '50000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.832781791687012}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15623', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.467512369155884}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,     10,  ..., 249991, 249998,
+tensor(crow_indices=tensor([     0,      6,      9,  ..., 249988, 249995,
                             250000]),
-       col_indices=tensor([12466, 31687, 41380,  ..., 43099, 30794, 44210]),
-       values=tensor([0.5535, 0.2801, 0.3869,  ..., 0.8607, 0.0342, 0.7001]),
+       col_indices=tensor([16459, 17260, 17489,  ..., 12154, 40898, 45606]),
+       values=tensor([0.4663, 0.9387, 0.6654,  ..., 0.8623, 0.7205, 0.6266]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.1141, 0.0022, 0.7559,  ..., 0.9683, 0.9705, 0.8203])
+tensor([0.6508, 0.5612, 0.5925,  ..., 0.2779, 0.1556, 0.3568])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.832781791687012 seconds
+Time: 10.467512369155884 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,     10,  ..., 249991, 249998,
+tensor(crow_indices=tensor([     0,      6,      9,  ..., 249988, 249995,
                             250000]),
-       col_indices=tensor([12466, 31687, 41380,  ..., 43099, 30794, 44210]),
-       values=tensor([0.5535, 0.2801, 0.3869,  ..., 0.8607, 0.0342, 0.7001]),
+       col_indices=tensor([16459, 17260, 17489,  ..., 12154, 40898, 45606]),
+       values=tensor([0.4663, 0.9387, 0.6654,  ..., 0.8623, 0.7205, 0.6266]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.1141, 0.0022, 0.7559,  ..., 0.9683, 0.9705, 0.8203])
+tensor([0.6508, 0.5612, 0.5925,  ..., 0.2779, 0.1556, 0.3568])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.832781791687012 seconds
+Time: 10.467512369155884 seconds
 
-[39.71, 38.91, 39.08, 39.03, 39.04, 38.89, 39.49, 39.37, 39.35, 39.31]
-[65.84]
-13.167025327682495
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15655, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.832781791687012, 'TIME_S_1KI': 0.691969453317599, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 866.9169475746155, 'W': 65.84}
-[39.71, 38.91, 39.08, 39.03, 39.04, 38.89, 39.49, 39.37, 39.35, 39.31, 40.75, 39.44, 39.59, 39.09, 39.39, 39.11, 38.98, 38.78, 44.38, 39.19]
-711.4000000000001
-35.57000000000001
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15655, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.832781791687012, 'TIME_S_1KI': 0.691969453317599, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 866.9169475746155, 'W': 65.84, 'J_1KI': 55.37636202967841, 'W_1KI': 4.205685084637497, 'W_D': 30.269999999999996, 'J_D': 398.5658566689491, 'W_D_1KI': 1.933567550303417, 'J_D_1KI': 0.12351118175045782}
+[39.73, 38.98, 39.07, 38.93, 44.53, 39.49, 39.88, 39.5, 39.09, 39.05]
+[65.57]
+13.17047119140625
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15623, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.467512369155884, 'TIME_S_1KI': 0.6700065524646921, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 863.5877960205078, 'W': 65.57}
+[39.73, 38.98, 39.07, 38.93, 44.53, 39.49, 39.88, 39.5, 39.09, 39.05, 39.67, 44.12, 39.07, 39.4, 39.1, 38.88, 39.62, 39.01, 40.11, 39.27]
+717.64
+35.882
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15623, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.467512369155884, 'TIME_S_1KI': 0.6700065524646921, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 863.5877960205078, 'W': 65.57, 'J_1KI': 55.27669436219085, 'W_1KI': 4.197017218203929, 'W_D': 29.687999999999995, 'J_D': 391.0049487304687, 'W_D_1KI': 1.900275235230109, 'J_D_1KI': 0.12163318410229208}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json
index 4e67596..f14cabf 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3401, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.135668277740479, "TIME_S_1KI": 2.9802023751074618, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 929.8197621202468, "W": 70.57, "J_1KI": 273.39599003829665, "W_1KI": 20.74977947662452, "W_D": 35.09774999999999, "J_D": 462.44270307433595, "W_D_1KI": 10.319832402234633, "J_D_1KI": 3.0343523676079487}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3392, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.097672939300537, "TIME_S_1KI": 2.9769082957843565, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 929.1495970487595, "W": 70.5, "J_1KI": 273.92381988465786, "W_1KI": 20.784198113207548, "W_D": 35.409, "J_D": 466.6703274028301, "W_D_1KI": 10.438974056603774, "J_D_1KI": 3.0775277289515843}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output
index 73dd96f..dcd3c8d 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.3087193965911865}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.3095407485961914}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      47,     100,  ..., 2499908,
+tensor(crow_indices=tensor([      0,      45,     100,  ..., 2499911,
                             2499955, 2500000]),
-       col_indices=tensor([ 1811,  3820,  5210,  ..., 47398, 47518, 48036]),
-       values=tensor([0.8154, 0.8090, 0.3024,  ..., 0.4722, 0.9116, 0.7561]),
+       col_indices=tensor([  793,  1442,  1576,  ..., 46694, 46780, 48162]),
+       values=tensor([0.1199, 0.8412, 0.1837,  ..., 0.3360, 0.3672, 0.4593]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4260, 0.8795, 0.3202,  ..., 0.3159, 0.0406, 0.9752])
+tensor([0.5589, 0.6592, 0.7865,  ..., 0.1895, 0.4954, 0.2523])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 0.3087193965911865 seconds
+Time: 0.3095407485961914 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3401', '-ss', '50000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.135668277740479}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3392', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.097672939300537}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      41,      88,  ..., 2499883,
-                            2499951, 2500000]),
-       col_indices=tensor([  638,  2365,  2400,  ..., 44467, 46636, 49496]),
-       values=tensor([0.8518, 0.8769, 0.3572,  ..., 0.1360, 0.1673, 0.1097]),
+tensor(crow_indices=tensor([      0,      53,     117,  ..., 2499918,
+                            2499954, 2500000]),
+       col_indices=tensor([  468,   760,  1747,  ..., 45524, 47546, 48668]),
+       values=tensor([0.6628, 0.6341, 0.7211,  ..., 0.0837, 0.7250, 0.8485]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0408, 0.6133, 0.1624,  ..., 0.7272, 0.2583, 0.9038])
+tensor([0.8535, 0.5776, 0.2662,  ..., 0.1591, 0.5920, 0.7540])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.135668277740479 seconds
+Time: 10.097672939300537 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      41,      88,  ..., 2499883,
-                            2499951, 2500000]),
-       col_indices=tensor([  638,  2365,  2400,  ..., 44467, 46636, 49496]),
-       values=tensor([0.8518, 0.8769, 0.3572,  ..., 0.1360, 0.1673, 0.1097]),
+tensor(crow_indices=tensor([      0,      53,     117,  ..., 2499918,
+                            2499954, 2500000]),
+       col_indices=tensor([  468,   760,  1747,  ..., 45524, 47546, 48668]),
+       values=tensor([0.6628, 0.6341, 0.7211,  ..., 0.0837, 0.7250, 0.8485]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0408, 0.6133, 0.1624,  ..., 0.7272, 0.2583, 0.9038])
+tensor([0.8535, 0.5776, 0.2662,  ..., 0.1591, 0.5920, 0.7540])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.135668277740479 seconds
+Time: 10.097672939300537 seconds
 
-[40.86, 44.19, 38.96, 38.91, 39.18, 38.84, 39.26, 38.86, 39.41, 39.32]
-[70.57]
-13.17585039138794
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.135668277740479, 'TIME_S_1KI': 2.9802023751074618, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 929.8197621202468, 'W': 70.57}
-[40.86, 44.19, 38.96, 38.91, 39.18, 38.84, 39.26, 38.86, 39.41, 39.32, 40.03, 39.02, 38.84, 38.98, 38.82, 40.3, 38.82, 38.8, 38.79, 38.72]
-709.445
-35.47225
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.135668277740479, 'TIME_S_1KI': 2.9802023751074618, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 929.8197621202468, 'W': 70.57, 'J_1KI': 273.39599003829665, 'W_1KI': 20.74977947662452, 'W_D': 35.09774999999999, 'J_D': 462.44270307433595, 'W_D_1KI': 10.319832402234633, 'J_D_1KI': 3.0343523676079487}
+[39.8, 38.8, 38.97, 38.81, 38.82, 39.18, 38.86, 39.35, 38.86, 38.73]
+[70.5]
+13.179426908493042
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3392, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.097672939300537, 'TIME_S_1KI': 2.9769082957843565, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 929.1495970487595, 'W': 70.5}
+[39.8, 38.8, 38.97, 38.81, 38.82, 39.18, 38.86, 39.35, 38.86, 38.73, 39.46, 38.86, 38.88, 38.78, 39.03, 38.81, 39.0, 39.3, 38.88, 39.27]
+701.82
+35.091
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3392, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.097672939300537, 'TIME_S_1KI': 2.9769082957843565, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 929.1495970487595, 'W': 70.5, 'J_1KI': 273.92381988465786, 'W_1KI': 20.784198113207548, 'W_D': 35.409, 'J_D': 466.6703274028301, 'W_D_1KI': 10.438974056603774, 'J_D_1KI': 3.0775277289515843}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.json
index 05ef34d..a468b12 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 277, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.601917266845703, "TIME_S_1KI": 38.27406955539965, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1502.4378191399574, "W": 76.47, "J_1KI": 5423.963245992626, "W_1KI": 276.0649819494585, "W_D": 40.946749999999994, "J_D": 804.497786986649, "W_D_1KI": 147.82220216606495, "J_D_1KI": 533.6541594442779}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 275, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.616158246994019, "TIME_S_1KI": 38.60421180725098, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1499.0550417494774, "W": 76.47, "J_1KI": 5451.109242725372, "W_1KI": 278.07272727272726, "W_D": 41.14999999999999, "J_D": 806.6707855105399, "W_D_1KI": 149.6363636363636, "J_D_1KI": 544.1322314049586}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.output
index e414a83..7563719 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.789712905883789}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.8045737743377686}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      551,     1060,  ..., 24998953,
-                            24999494, 25000000]),
-       col_indices=tensor([   18,    53,    90,  ..., 49926, 49944, 49970]),
-       values=tensor([0.6546, 0.1735, 0.7966,  ..., 0.3203, 0.8871, 0.0598]),
+tensor(crow_indices=tensor([       0,      499,     1030,  ..., 24998983,
+                            24999514, 25000000]),
+       col_indices=tensor([   49,   197,   201,  ..., 49750, 49850, 49875]),
+       values=tensor([0.4470, 0.5814, 0.1175,  ..., 0.1864, 0.8893, 0.5129]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.4855, 0.9619, 0.0930,  ..., 0.6959, 0.6112, 0.3764])
+tensor([0.3234, 0.6125, 0.0098,  ..., 0.2894, 0.7048, 0.9675])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 3.789712905883789 seconds
+Time: 3.8045737743377686 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '277', '-ss', '50000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.601917266845703}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '275', '-ss', '50000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.616158246994019}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      523,     1012,  ..., 24998990,
-                            24999509, 25000000]),
-       col_indices=tensor([  171,   246,   332,  ..., 49640, 49825, 49863]),
-       values=tensor([0.1620, 0.2511, 0.7784,  ..., 0.0916, 0.2856, 0.2435]),
+tensor(crow_indices=tensor([       0,      461,      959,  ..., 24999025,
+                            24999527, 25000000]),
+       col_indices=tensor([   62,   115,   156,  ..., 49765, 49783, 49784]),
+       values=tensor([0.8824, 0.1334, 0.7937,  ..., 0.1865, 0.7051, 0.7927]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.9840, 0.8151, 0.6106,  ..., 0.4542, 0.6992, 0.9833])
+tensor([0.8987, 0.8601, 0.5995,  ..., 0.4986, 0.6962, 0.5259])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 10.601917266845703 seconds
+Time: 10.616158246994019 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      523,     1012,  ..., 24998990,
-                            24999509, 25000000]),
-       col_indices=tensor([  171,   246,   332,  ..., 49640, 49825, 49863]),
-       values=tensor([0.1620, 0.2511, 0.7784,  ..., 0.0916, 0.2856, 0.2435]),
+tensor(crow_indices=tensor([       0,      461,      959,  ..., 24999025,
+                            24999527, 25000000]),
+       col_indices=tensor([   62,   115,   156,  ..., 49765, 49783, 49784]),
+       values=tensor([0.8824, 0.1334, 0.7937,  ..., 0.1865, 0.7051, 0.7927]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.9840, 0.8151, 0.6106,  ..., 0.4542, 0.6992, 0.9833])
+tensor([0.8987, 0.8601, 0.5995,  ..., 0.4986, 0.6962, 0.5259])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 10.601917266845703 seconds
+Time: 10.616158246994019 seconds
 
-[39.75, 38.88, 39.04, 38.94, 38.9, 44.6, 39.87, 39.01, 39.47, 38.87]
+[39.75, 39.76, 39.16, 38.84, 39.46, 40.31, 39.13, 39.17, 38.94, 39.02]
 [76.47]
-19.647414922714233
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.601917266845703, 'TIME_S_1KI': 38.27406955539965, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.4378191399574, 'W': 76.47}
-[39.75, 38.88, 39.04, 38.94, 38.9, 44.6, 39.87, 39.01, 39.47, 38.87, 39.65, 39.46, 38.97, 39.37, 39.36, 38.88, 39.18, 38.94, 39.03, 38.86]
-710.465
-35.523250000000004
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.601917266845703, 'TIME_S_1KI': 38.27406955539965, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.4378191399574, 'W': 76.47, 'J_1KI': 5423.963245992626, 'W_1KI': 276.0649819494585, 'W_D': 40.946749999999994, 'J_D': 804.497786986649, 'W_D_1KI': 147.82220216606495, 'J_D_1KI': 533.6541594442779}
+19.60317826271057
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 275, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.616158246994019, 'TIME_S_1KI': 38.60421180725098, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1499.0550417494774, 'W': 76.47}
+[39.75, 39.76, 39.16, 38.84, 39.46, 40.31, 39.13, 39.17, 38.94, 39.02, 39.62, 38.87, 39.31, 38.86, 39.4, 39.85, 38.89, 38.85, 38.98, 38.85]
+706.4000000000001
+35.32000000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 275, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.616158246994019, 'TIME_S_1KI': 38.60421180725098, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1499.0550417494774, 'W': 76.47, 'J_1KI': 5451.109242725372, 'W_1KI': 278.07272727272726, 'W_D': 41.14999999999999, 'J_D': 806.6707855105399, 'W_D_1KI': 149.6363636363636, 'J_D_1KI': 544.1322314049586}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.05.json
new file mode 100644
index 0000000..c434506
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 18.401755332946777, "TIME_S_1KI": 184.01755332946777, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3785.108492267132, "W": 75.15, "J_1KI": 37851.08492267132, "W_1KI": 751.5, "W_D": 39.52975000000001, "J_D": 1991.009879204214, "W_D_1KI": 395.29750000000007, "J_D_1KI": 3952.975000000001}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.05.output
new file mode 100644
index 0000000..18ebdf4
--- /dev/null
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.05.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 18.401755332946777}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2445,      4988,  ...,
+                            124994983, 124997456, 125000000]),
+       col_indices=tensor([    8,    12,    48,  ..., 49949, 49951, 49988]),
+       values=tensor([0.9557, 0.9594, 0.4511,  ..., 0.5325, 0.7193, 0.5719]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.0493, 0.8291, 0.8551,  ..., 0.3701, 0.3910, 0.0950])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 18.401755332946777 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2445,      4988,  ...,
+                            124994983, 124997456, 125000000]),
+       col_indices=tensor([    8,    12,    48,  ..., 49949, 49951, 49988]),
+       values=tensor([0.9557, 0.9594, 0.4511,  ..., 0.5325, 0.7193, 0.5719]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.0493, 0.8291, 0.8551,  ..., 0.3701, 0.3910, 0.0950])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 18.401755332946777 seconds
+
+[40.13, 39.81, 39.2, 39.05, 39.17, 39.04, 39.25, 39.01, 39.25, 44.34]
+[75.15]
+50.36737847328186
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 18.401755332946777, 'TIME_S_1KI': 184.01755332946777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3785.108492267132, 'W': 75.15}
+[40.13, 39.81, 39.2, 39.05, 39.17, 39.04, 39.25, 39.01, 39.25, 44.34, 41.29, 39.67, 39.65, 39.33, 39.15, 40.25, 39.42, 39.16, 39.57, 39.09]
+712.405
+35.62025
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 18.401755332946777, 'TIME_S_1KI': 184.01755332946777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3785.108492267132, 'W': 75.15, 'J_1KI': 37851.08492267132, 'W_1KI': 751.5, 'W_D': 39.52975000000001, 'J_D': 1991.009879204214, 'W_D_1KI': 395.29750000000007, 'J_D_1KI': 3952.975000000001}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json
index 41355a8..7be222f 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 35925, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.468754291534424, "TIME_S_1KI": 0.29140582579079816, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 855.3263425445557, "W": 65.12, "J_1KI": 23.808666459138642, "W_1KI": 1.8126652748782186, "W_D": 29.625000000000007, "J_D": 389.1130666136743, "W_D_1KI": 0.8246346555323593, "J_D_1KI": 0.022954339750378826}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 35826, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.541781902313232, "TIME_S_1KI": 0.2942494808885511, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 853.0015280151367, "W": 65.44, "J_1KI": 23.809566460535272, "W_1KI": 1.8266063752581922, "W_D": 30.0845, "J_D": 392.14737881374356, "W_D_1KI": 0.8397392954837268, "J_D_1KI": 0.023439381887001808}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output
index 77e4f27..aa71314 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.04216480255126953}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.050371646881103516}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 24999, 25000]),
-       col_indices=tensor([ 8605,  8537, 29290,  ...,  9179, 13978,  1469]),
-       values=tensor([0.2780, 0.8342, 0.8502,  ..., 0.9082, 0.5496, 0.9536]),
+       col_indices=tensor([25964, 21843,  7312,  ..., 22871, 36058, 42523]),
+       values=tensor([0.5989, 0.2502, 0.2611,  ..., 0.6108, 0.6850, 0.5455]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7685, 0.9033, 0.7153,  ..., 0.8654, 0.8274, 0.9503])
+tensor([0.4790, 0.5854, 0.5560,  ..., 0.2153, 0.4221, 0.1522])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,18 +15,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.04216480255126953 seconds
+Time: 0.050371646881103516 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24902', '-ss', '50000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.278246164321899}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '20845', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.109277963638306}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([37031, 32096, 18727,  ..., 44552, 41451,  6296]),
-       values=tensor([0.0751, 0.3287, 0.1662,  ..., 0.5788, 0.0483, 0.4147]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([14323,   781, 26094,  ..., 16343, 47701, 45326]),
+       values=tensor([0.2851, 0.3280, 0.8584,  ..., 0.1683, 0.6282, 0.2761]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5341, 0.4675, 0.3327,  ..., 0.1193, 0.3106, 0.6128])
+tensor([0.5463, 0.7584, 0.3297,  ..., 0.3885, 0.0995, 0.5723])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,18 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 7.278246164321899 seconds
+Time: 6.109277963638306 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35925', '-ss', '50000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.468754291534424}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35826', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.541781902313232}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ..., 24998, 24998, 25000]),
-       col_indices=tensor([44391, 10770, 45928,  ...,  5594,  4079, 17032]),
-       values=tensor([0.6882, 0.4791, 0.4331,  ..., 0.9470, 0.6037, 0.4941]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([10673, 29891, 46300,  ..., 43219, 43038, 35784]),
+       values=tensor([0.4019, 0.4089, 0.6276,  ..., 0.0249, 0.8752, 0.4213]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1889, 0.2559, 0.4696,  ..., 0.5278, 0.4768, 0.4458])
+tensor([0.3571, 0.8012, 0.5494,  ..., 0.5264, 0.9794, 0.7992])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,15 +53,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.468754291534424 seconds
+Time: 10.541781902313232 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ..., 24998, 24998, 25000]),
-       col_indices=tensor([44391, 10770, 45928,  ...,  5594,  4079, 17032]),
-       values=tensor([0.6882, 0.4791, 0.4331,  ..., 0.9470, 0.6037, 0.4941]),
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([10673, 29891, 46300,  ..., 43219, 43038, 35784]),
+       values=tensor([0.4019, 0.4089, 0.6276,  ..., 0.0249, 0.8752, 0.4213]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1889, 0.2559, 0.4696,  ..., 0.5278, 0.4768, 0.4458])
+tensor([0.3571, 0.8012, 0.5494,  ..., 0.5264, 0.9794, 0.7992])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -69,13 +69,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.468754291534424 seconds
+Time: 10.541781902313232 seconds
 
-[39.59, 39.87, 39.3, 39.3, 39.78, 38.89, 39.35, 40.52, 39.13, 38.83]
-[65.12]
-13.134618282318115
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35925, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.468754291534424, 'TIME_S_1KI': 0.29140582579079816, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3263425445557, 'W': 65.12}
-[39.59, 39.87, 39.3, 39.3, 39.78, 38.89, 39.35, 40.52, 39.13, 38.83, 39.59, 39.23, 39.11, 38.91, 39.77, 40.62, 39.49, 39.29, 38.91, 38.85]
-709.9
-35.495
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35925, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.468754291534424, 'TIME_S_1KI': 0.29140582579079816, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3263425445557, 'W': 65.12, 'J_1KI': 23.808666459138642, 'W_1KI': 1.8126652748782186, 'W_D': 29.625000000000007, 'J_D': 389.1130666136743, 'W_D_1KI': 0.8246346555323593, 'J_D_1KI': 0.022954339750378826}
+[40.53, 39.16, 39.31, 39.02, 39.02, 38.84, 39.02, 39.47, 39.01, 39.03]
+[65.44]
+13.03486442565918
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35826, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.541781902313232, 'TIME_S_1KI': 0.2942494808885511, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 853.0015280151367, 'W': 65.44}
+[40.53, 39.16, 39.31, 39.02, 39.02, 38.84, 39.02, 39.47, 39.01, 39.03, 40.83, 39.59, 39.16, 39.59, 39.02, 38.83, 38.9, 40.19, 39.35, 38.87]
+707.11
+35.3555
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35826, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.541781902313232, 'TIME_S_1KI': 0.2942494808885511, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 853.0015280151367, 'W': 65.44, 'J_1KI': 23.809566460535272, 'W_1KI': 1.8266063752581922, 'W_D': 30.0845, 'J_D': 392.14737881374356, 'W_D_1KI': 0.8397392954837268, 'J_D_1KI': 0.023439381887001808}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.json
index 54cfc66..caaa3d4 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 18331, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.45155644416809, "TIME_S_1KI": 0.570157462449844, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 852.8894222688675, "W": 65.62, "J_1KI": 46.52716285357414, "W_1KI": 3.5797283290600626, "W_D": 29.885000000000005, "J_D": 388.42731460690504, "W_D_1KI": 1.6302984016147513, "J_D_1KI": 0.08893668657545967}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 18383, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.474826574325562, "TIME_S_1KI": 0.569810508313418, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 856.8708778381347, "W": 65.6, "J_1KI": 46.612135007242266, "W_1KI": 3.568514388293532, "W_D": 30.095249999999993, "J_D": 393.105842778325, "W_D_1KI": 1.6371239732361418, "J_D_1KI": 0.0890564093584367}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.output
index bef46d2..fb843e8 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.0693967342376709}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.06945943832397461}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      5,  ..., 124992, 124996,
+tensor(crow_indices=tensor([     0,      0,      4,  ..., 124995, 124998,
                             125000]),
-       col_indices=tensor([17438, 29688, 13553,  ..., 36532, 44163, 44855]),
-       values=tensor([0.2242, 0.0224, 0.1461,  ..., 0.6740, 0.5355, 0.8238]),
+       col_indices=tensor([ 8346,  9814, 28925,  ..., 45142, 46623, 48893]),
+       values=tensor([0.3255, 0.7993, 0.2066,  ..., 0.8340, 0.0401, 0.6472]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.2153, 0.3458, 0.3359,  ..., 0.7412, 0.9011, 0.6249])
+tensor([0.4856, 0.9773, 0.6116,  ..., 0.9065, 0.3370, 0.6933])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 0.0693967342376709 seconds
+Time: 0.06945943832397461 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15130', '-ss', '50000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.666174411773682}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15116', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.63381552696228}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      4,  ..., 124994, 124997,
+tensor(crow_indices=tensor([     0,      3,      6,  ..., 124994, 124997,
                             125000]),
-       col_indices=tensor([17329, 36001, 38373,  ...,   475, 21379, 35295]),
-       values=tensor([0.0803, 0.2135, 0.1853,  ..., 0.6523, 0.5299, 0.1396]),
+       col_indices=tensor([ 9498, 22845, 30286,  ..., 10404, 16253, 48049]),
+       values=tensor([0.7056, 0.4273, 0.3718,  ..., 0.2477, 0.7014, 0.4740]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.4435, 0.4362, 0.5554,  ..., 0.5263, 0.8506, 0.5178])
+tensor([0.1815, 0.9730, 0.6971,  ..., 0.9915, 0.0180, 0.6275])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 8.666174411773682 seconds
+Time: 8.63381552696228 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18331', '-ss', '50000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.45155644416809}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18383', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.474826574325562}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      2,  ..., 124997, 124997,
+tensor(crow_indices=tensor([     0,      3,      5,  ..., 124991, 124998,
                             125000]),
-       col_indices=tensor([14185, 16264,  7088,  ..., 33383, 46641, 46645]),
-       values=tensor([0.3059, 0.0880, 0.9320,  ..., 0.7602, 0.8512, 0.4645]),
+       col_indices=tensor([20459, 34680, 37330,  ..., 46670, 18109, 44848]),
+       values=tensor([0.3676, 0.3673, 0.7663,  ..., 0.4241, 0.5790, 0.3050]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.5025, 0.7968, 0.2806,  ..., 0.1024, 0.8091, 0.6972])
+tensor([0.5597, 0.5275, 0.6777,  ..., 0.9335, 0.3355, 0.0117])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.45155644416809 seconds
+Time: 10.474826574325562 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      2,  ..., 124997, 124997,
+tensor(crow_indices=tensor([     0,      3,      5,  ..., 124991, 124998,
                             125000]),
-       col_indices=tensor([14185, 16264,  7088,  ..., 33383, 46641, 46645]),
-       values=tensor([0.3059, 0.0880, 0.9320,  ..., 0.7602, 0.8512, 0.4645]),
+       col_indices=tensor([20459, 34680, 37330,  ..., 46670, 18109, 44848]),
+       values=tensor([0.3676, 0.3673, 0.7663,  ..., 0.4241, 0.5790, 0.3050]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.5025, 0.7968, 0.2806,  ..., 0.1024, 0.8091, 0.6972])
+tensor([0.5597, 0.5275, 0.6777,  ..., 0.9335, 0.3355, 0.0117])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.45155644416809 seconds
+Time: 10.474826574325562 seconds
 
-[39.86, 38.92, 44.85, 39.01, 39.6, 39.39, 38.97, 39.8, 41.08, 39.48]
-[65.62]
-12.997400522232056
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 18331, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.45155644416809, 'TIME_S_1KI': 0.570157462449844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 852.8894222688675, 'W': 65.62}
-[39.86, 38.92, 44.85, 39.01, 39.6, 39.39, 38.97, 39.8, 41.08, 39.48, 40.67, 39.02, 39.09, 39.05, 39.17, 38.87, 39.48, 39.3, 39.44, 39.31]
-714.7
-35.735
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 18331, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.45155644416809, 'TIME_S_1KI': 0.570157462449844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 852.8894222688675, 'W': 65.62, 'J_1KI': 46.52716285357414, 'W_1KI': 3.5797283290600626, 'W_D': 29.885000000000005, 'J_D': 388.42731460690504, 'W_D_1KI': 1.6302984016147513, 'J_D_1KI': 0.08893668657545967}
+[40.75, 38.89, 41.81, 39.09, 39.01, 39.1, 39.32, 39.89, 39.11, 38.97]
+[65.6]
+13.062056064605713
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 18383, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.474826574325562, 'TIME_S_1KI': 0.569810508313418, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.8708778381347, 'W': 65.6}
+[40.75, 38.89, 41.81, 39.09, 39.01, 39.1, 39.32, 39.89, 39.11, 38.97, 39.62, 38.92, 38.94, 39.58, 39.13, 39.33, 39.44, 39.74, 39.35, 39.55]
+710.095
+35.50475
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 18383, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.474826574325562, 'TIME_S_1KI': 0.569810508313418, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.8708778381347, 'W': 65.6, 'J_1KI': 46.612135007242266, 'W_1KI': 3.568514388293532, 'W_D': 30.095249999999993, 'J_D': 393.105842778325, 'W_D_1KI': 1.6371239732361418, 'J_D_1KI': 0.0890564093584367}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.json
index 2d402b4..e88993f 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 461205, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.546576023101807, "TIME_S_1KI": 0.022867436439548153, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 854.1005449390411, "W": 65.22, "J_1KI": 1.8518891706270337, "W_1KI": 0.1414121702930367, "W_D": 29.99125, "J_D": 392.75594861090184, "W_D_1KI": 0.065028024414306, "J_D_1KI": 0.0001409959224516343}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 466510, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.420932292938232, "TIME_S_1KI": 0.022338068407833127, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 867.0800234699249, "W": 65.24, "J_1KI": 1.8586525979505797, "W_1KI": 0.13984694861846475, "W_D": 30.02799999999999, "J_D": 399.09072570133196, "W_D_1KI": 0.06436732331568452, "J_D_1KI": 0.00013797629914832378}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.output
index a788520..9e46e33 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.0131683349609375}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.013342857360839844}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
-       col_indices=tensor([1743,  190, 2771,  ..., 2075, 3388, 3957]),
-       values=tensor([0.1814, 0.3494, 0.7591,  ..., 0.1503, 0.3935, 0.5274]),
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([ 467, 3498, 4566,  ..., 3900,  759, 3936]),
+       values=tensor([0.2546, 0.8323, 0.6213,  ..., 0.2937, 0.3931, 0.2918]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.3288, 0.8511, 0.6239,  ..., 0.9211, 0.6649, 0.5940])
+tensor([0.8048, 0.7031, 0.1236,  ..., 0.7176, 0.5601, 0.1346])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.0131683349609375 seconds
+Time: 0.013342857360839844 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '79736', '-ss', '5000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.8153021335601807}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '78693', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.771183967590332}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
-       col_indices=tensor([3060, 1065, 3686,  ...,  959,  268, 4999]),
-       values=tensor([0.4441, 0.5663, 0.4237,  ..., 0.7927, 0.1815, 0.5098]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([4956, 1185, 2120,  ..., 4420, 3904, 4456]),
+       values=tensor([0.8759, 0.0506, 0.8723,  ..., 0.0545, 0.1125, 0.7584]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.5827, 0.3654, 0.8856,  ..., 0.0793, 0.3634, 0.4632])
+tensor([0.0884, 0.5653, 0.4168,  ..., 0.9870, 0.2343, 0.4597])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 1.8153021335601807 seconds
+Time: 1.771183967590332 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '461205', '-ss', '5000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.546576023101807}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '466510', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.420932292938232}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
-       col_indices=tensor([ 400,  983, 3289,  ..., 2520,  735,  710]),
-       values=tensor([0.5782, 0.5847, 0.2189,  ..., 0.6822, 0.4901, 0.5172]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([1055,  721, 2056,  ...,  172, 3588, 4261]),
+       values=tensor([0.6923, 0.0689, 0.1686,  ..., 0.9272, 0.3017, 0.9913]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.8041, 0.1477, 0.3593,  ..., 0.2372, 0.8803, 0.6540])
+tensor([0.3960, 0.9874, 0.3648,  ..., 0.2104, 0.2644, 0.1773])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.546576023101807 seconds
+Time: 10.420932292938232 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
-       col_indices=tensor([ 400,  983, 3289,  ..., 2520,  735,  710]),
-       values=tensor([0.5782, 0.5847, 0.2189,  ..., 0.6822, 0.4901, 0.5172]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([1055,  721, 2056,  ...,  172, 3588, 4261]),
+       values=tensor([0.6923, 0.0689, 0.1686,  ..., 0.9272, 0.3017, 0.9913]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.8041, 0.1477, 0.3593,  ..., 0.2372, 0.8803, 0.6540])
+tensor([0.3960, 0.9874, 0.3648,  ..., 0.2104, 0.2644, 0.1773])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.546576023101807 seconds
+Time: 10.420932292938232 seconds
 
-[42.91, 38.91, 39.2, 38.78, 38.91, 38.88, 39.21, 39.32, 39.19, 38.86]
-[65.22]
-13.09568452835083
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 461205, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.546576023101807, 'TIME_S_1KI': 0.022867436439548153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 854.1005449390411, 'W': 65.22}
-[42.91, 38.91, 39.2, 38.78, 38.91, 38.88, 39.21, 39.32, 39.19, 38.86, 39.94, 38.72, 39.13, 38.84, 39.27, 38.74, 39.16, 38.84, 39.21, 38.82]
-704.5749999999999
-35.22875
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 461205, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.546576023101807, 'TIME_S_1KI': 0.022867436439548153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 854.1005449390411, 'W': 65.22, 'J_1KI': 1.8518891706270337, 'W_1KI': 0.1414121702930367, 'W_D': 29.99125, 'J_D': 392.75594861090184, 'W_D_1KI': 0.065028024414306, 'J_D_1KI': 0.0001409959224516343}
+[40.48, 39.11, 41.11, 39.09, 39.06, 38.72, 38.7, 38.64, 39.16, 39.15]
+[65.24]
+13.290619611740112
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 466510, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.420932292938232, 'TIME_S_1KI': 0.022338068407833127, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 867.0800234699249, 'W': 65.24}
+[40.48, 39.11, 41.11, 39.09, 39.06, 38.72, 38.7, 38.64, 39.16, 39.15, 40.21, 39.12, 38.73, 38.98, 38.96, 38.81, 38.73, 38.84, 39.18, 38.76]
+704.24
+35.212
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 466510, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.420932292938232, 'TIME_S_1KI': 0.022338068407833127, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 867.0800234699249, 'W': 65.24, 'J_1KI': 1.8586525979505797, 'W_1KI': 0.13984694861846475, 'W_D': 30.02799999999999, 'J_D': 399.09072570133196, 'W_D_1KI': 0.06436732331568452, 'J_D_1KI': 0.00013797629914832378}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.json
index 89f214d..22fb7e0 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 244335, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.417778253555298, "TIME_S_1KI": 0.04263727363478543, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 855.0210603523253, "W": 66.32, "J_1KI": 3.4993801966657467, "W_1KI": 0.27143061779933286, "W_D": 30.753, "J_D": 396.4786289055347, "W_D_1KI": 0.1258640800540242, "J_D_1KI": 0.0005151291466798624}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 246954, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.452702045440674, "TIME_S_1KI": 0.04232651443362194, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 872.7546772527694, "W": 66.74, "J_1KI": 3.534077914319142, "W_1KI": 0.2702527596232497, "W_D": 31.230999999999995, "J_D": 408.4057735283374, "W_D_1KI": 0.12646484770443076, "J_D_1KI": 0.0005120988026289542}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.output
index b5c1026..4a43c23 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.01594710350036621}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.016002416610717773}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     8,    12,  ..., 24992, 24999, 25000]),
-       col_indices=tensor([ 675, 1985, 2689,  ..., 3047, 3313, 3022]),
-       values=tensor([0.4390, 0.7079, 0.1777,  ..., 0.9145, 0.2520, 0.4929]),
+tensor(crow_indices=tensor([    0,     5,    11,  ..., 24990, 24996, 25000]),
+       col_indices=tensor([ 911, 1463, 3145,  ..., 1571, 1933, 2090]),
+       values=tensor([0.0181, 0.5592, 0.9261,  ..., 0.7648, 0.3954, 0.5475]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8163, 0.8334, 0.7201,  ..., 0.8897, 0.9022, 0.1765])
+tensor([0.4688, 0.8015, 0.1374,  ..., 0.6427, 0.0862, 0.7007])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.01594710350036621 seconds
+Time: 0.016002416610717773 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65842', '-ss', '5000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.8294758796691895}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65615', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.7898170948028564}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     9,  ..., 24993, 24997, 25000]),
-       col_indices=tensor([  33, 1908, 3594,  ...,  299,  386, 4209]),
-       values=tensor([0.2193, 0.5619, 0.9883,  ..., 0.1847, 0.2793, 0.9697]),
+tensor(crow_indices=tensor([    0,     6,     8,  ..., 24994, 24996, 25000]),
+       col_indices=tensor([ 939, 1014, 1959,  ..., 2032, 2340, 3396]),
+       values=tensor([0.4320, 0.5074, 0.9233,  ..., 0.9013, 0.0461, 0.5547]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.6444, 0.1854, 0.4415,  ..., 0.6088, 0.2901, 0.0299])
+tensor([0.8806, 0.5054, 0.0674,  ..., 0.2584, 0.6926, 0.9912])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 2.8294758796691895 seconds
+Time: 2.7898170948028564 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '244335', '-ss', '5000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.417778253555298}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '246954', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.452702045440674}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     8,  ..., 24990, 24997, 25000]),
-       col_indices=tensor([2557, 3235, 4228,  ...,  486, 3364, 4712]),
-       values=tensor([0.2079, 0.9240, 0.7430,  ..., 0.9071, 0.8940, 0.1396]),
+tensor(crow_indices=tensor([    0,     9,    13,  ..., 24993, 24995, 25000]),
+       col_indices=tensor([  74,  935, 1178,  ..., 2445, 4655, 4967]),
+       values=tensor([0.5811, 0.4060, 0.5355,  ..., 0.4068, 0.4273, 0.6206]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.3103, 0.6909, 0.1567,  ..., 0.3064, 0.4398, 0.1480])
+tensor([0.7726, 0.0975, 0.1934,  ..., 0.7258, 0.0094, 0.2425])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.417778253555298 seconds
+Time: 10.452702045440674 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     8,  ..., 24990, 24997, 25000]),
-       col_indices=tensor([2557, 3235, 4228,  ...,  486, 3364, 4712]),
-       values=tensor([0.2079, 0.9240, 0.7430,  ..., 0.9071, 0.8940, 0.1396]),
+tensor(crow_indices=tensor([    0,     9,    13,  ..., 24993, 24995, 25000]),
+       col_indices=tensor([  74,  935, 1178,  ..., 2445, 4655, 4967]),
+       values=tensor([0.5811, 0.4060, 0.5355,  ..., 0.4068, 0.4273, 0.6206]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.3103, 0.6909, 0.1567,  ..., 0.3064, 0.4398, 0.1480])
+tensor([0.7726, 0.0975, 0.1934,  ..., 0.7258, 0.0094, 0.2425])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.417778253555298 seconds
+Time: 10.452702045440674 seconds
 
-[45.51, 38.78, 39.51, 38.99, 39.48, 39.2, 39.32, 39.33, 39.41, 38.75]
-[66.32]
-12.892356157302856
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 244335, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.417778253555298, 'TIME_S_1KI': 0.04263727363478543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.0210603523253, 'W': 66.32}
-[45.51, 38.78, 39.51, 38.99, 39.48, 39.2, 39.32, 39.33, 39.41, 38.75, 39.41, 38.8, 39.14, 39.28, 40.7, 39.8, 39.35, 39.07, 39.32, 40.05]
-711.3399999999999
-35.56699999999999
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 244335, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.417778253555298, 'TIME_S_1KI': 0.04263727363478543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.0210603523253, 'W': 66.32, 'J_1KI': 3.4993801966657467, 'W_1KI': 0.27143061779933286, 'W_D': 30.753, 'J_D': 396.4786289055347, 'W_D_1KI': 0.1258640800540242, 'J_D_1KI': 0.0005151291466798624}
+[39.42, 38.72, 38.82, 38.89, 39.34, 39.2, 40.59, 38.72, 39.01, 38.68]
+[66.74]
+13.076935529708862
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 246954, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.452702045440674, 'TIME_S_1KI': 0.04232651443362194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 872.7546772527694, 'W': 66.74}
+[39.42, 38.72, 38.82, 38.89, 39.34, 39.2, 40.59, 38.72, 39.01, 38.68, 39.74, 40.05, 39.22, 38.96, 38.83, 38.91, 39.32, 38.82, 44.18, 39.36]
+710.1800000000001
+35.509
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 246954, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.452702045440674, 'TIME_S_1KI': 0.04232651443362194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 872.7546772527694, 'W': 66.74, 'J_1KI': 3.534077914319142, 'W_1KI': 0.2702527596232497, 'W_D': 31.230999999999995, 'J_D': 408.4057735283374, 'W_D_1KI': 0.12646484770443076, 'J_D_1KI': 0.0005120988026289542}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.json
index d1e9eee..e3c6133 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 41575, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.472304821014404, "TIME_S_1KI": 0.2518894725439424, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 875.4065453624726, "W": 66.47, "J_1KI": 21.056080465723934, "W_1KI": 1.5987973541791942, "W_D": 30.839750000000002, "J_D": 406.1579510657788, "W_D_1KI": 0.7417859290438966, "J_D_1KI": 0.017842114949943394}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 41474, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.47754454612732, "TIME_S_1KI": 0.2526292266510903, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 870.6883107089997, "W": 66.37, "J_1KI": 20.99359383490861, "W_1KI": 1.600279693301828, "W_D": 30.931000000000004, "J_D": 405.7745990438462, "W_D_1KI": 0.7457925447268169, "J_D_1KI": 0.017982170630438755}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.output
index 9d6f728..b039ea4 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.03741025924682617}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.03760981559753418}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     50,     95,  ..., 249917, 249960,
+tensor(crow_indices=tensor([     0,     44,     97,  ..., 249905, 249953,
                             250000]),
-       col_indices=tensor([  56,  131,  133,  ..., 4645, 4665, 4841]),
-       values=tensor([0.2594, 0.3669, 0.3309,  ..., 0.9204, 0.7750, 0.3008]),
+       col_indices=tensor([ 104,  120,  133,  ..., 4848, 4973, 4994]),
+       values=tensor([0.9158, 0.4016, 0.6409,  ..., 0.0985, 0.9307, 0.4768]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7286, 0.8651, 0.2961,  ..., 0.7120, 0.4132, 0.7079])
+tensor([0.3635, 0.2613, 0.6667,  ..., 0.7993, 0.8141, 0.7335])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 0.03741025924682617 seconds
+Time: 0.03760981559753418 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28067', '-ss', '5000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.088342666625977}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27918', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.06800651550293}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     51,     92,  ..., 249888, 249951,
+tensor(crow_indices=tensor([     0,     45,     97,  ..., 249917, 249970,
                             250000]),
-       col_indices=tensor([ 195,  232,  275,  ..., 4637, 4801, 4910]),
-       values=tensor([0.9933, 0.3255, 0.9817,  ..., 0.2679, 0.2640, 0.0554]),
+       col_indices=tensor([ 188,  618,  867,  ..., 4726, 4860, 4863]),
+       values=tensor([0.1821, 0.6463, 0.0831,  ..., 0.8462, 0.9621, 0.9280]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.2297, 0.2173, 0.6945,  ..., 0.5761, 0.5521, 0.3650])
+tensor([0.3413, 0.0178, 0.3377,  ..., 0.2404, 0.9085, 0.3597])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 7.088342666625977 seconds
+Time: 7.06800651550293 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '41575', '-ss', '5000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.472304821014404}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '41474', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.47754454612732}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     48,     88,  ..., 249902, 249950,
+tensor(crow_indices=tensor([     0,     49,     96,  ..., 249910, 249953,
                             250000]),
-       col_indices=tensor([  75,   83,  302,  ..., 4746, 4941, 4952]),
-       values=tensor([0.9930, 0.3893, 0.6584,  ..., 0.0382, 0.8338, 0.2904]),
+       col_indices=tensor([  53,   94,  136,  ..., 4853, 4896, 4944]),
+       values=tensor([0.5427, 0.3847, 0.9677,  ..., 0.3094, 0.3612, 0.4453]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5807, 0.7893, 0.2250,  ..., 0.2178, 0.8594, 0.2155])
+tensor([0.3280, 0.3953, 0.4391,  ..., 0.5285, 0.6882, 0.9283])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.472304821014404 seconds
+Time: 10.47754454612732 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     48,     88,  ..., 249902, 249950,
+tensor(crow_indices=tensor([     0,     49,     96,  ..., 249910, 249953,
                             250000]),
-       col_indices=tensor([  75,   83,  302,  ..., 4746, 4941, 4952]),
-       values=tensor([0.9930, 0.3893, 0.6584,  ..., 0.0382, 0.8338, 0.2904]),
+       col_indices=tensor([  53,   94,  136,  ..., 4853, 4896, 4944]),
+       values=tensor([0.5427, 0.3847, 0.9677,  ..., 0.3094, 0.3612, 0.4453]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5807, 0.7893, 0.2250,  ..., 0.2178, 0.8594, 0.2155])
+tensor([0.3280, 0.3953, 0.4391,  ..., 0.5285, 0.6882, 0.9283])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.472304821014404 seconds
+Time: 10.47754454612732 seconds
 
-[39.83, 38.85, 39.09, 38.79, 39.53, 38.8, 39.37, 39.26, 39.29, 38.81]
-[66.47]
-13.169949531555176
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 41575, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.472304821014404, 'TIME_S_1KI': 0.2518894725439424, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 875.4065453624726, 'W': 66.47}
-[39.83, 38.85, 39.09, 38.79, 39.53, 38.8, 39.37, 39.26, 39.29, 38.81, 39.98, 38.99, 38.83, 39.07, 38.9, 38.8, 38.86, 39.29, 47.91, 39.33]
-712.6049999999999
-35.63025
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 41575, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.472304821014404, 'TIME_S_1KI': 0.2518894725439424, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 875.4065453624726, 'W': 66.47, 'J_1KI': 21.056080465723934, 'W_1KI': 1.5987973541791942, 'W_D': 30.839750000000002, 'J_D': 406.1579510657788, 'W_D_1KI': 0.7417859290438966, 'J_D_1KI': 0.017842114949943394}
+[40.24, 38.82, 38.94, 38.82, 40.71, 39.34, 39.06, 40.43, 39.15, 39.41]
+[66.37]
+13.11870288848877
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 41474, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.47754454612732, 'TIME_S_1KI': 0.2526292266510903, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 870.6883107089997, 'W': 66.37}
+[40.24, 38.82, 38.94, 38.82, 40.71, 39.34, 39.06, 40.43, 39.15, 39.41, 39.5, 39.09, 38.93, 39.1, 40.54, 38.88, 39.1, 39.52, 39.14, 39.27]
+708.78
+35.439
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 41474, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.47754454612732, 'TIME_S_1KI': 0.2526292266510903, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 870.6883107089997, 'W': 66.37, 'J_1KI': 20.99359383490861, 'W_1KI': 1.600279693301828, 'W_D': 30.931000000000004, 'J_D': 405.7745990438462, 'W_D_1KI': 0.7457925447268169, 'J_D_1KI': 0.017982170630438755}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.json
index e407283..3843606 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 8178, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.511467218399048, "TIME_S_1KI": 1.2853347051111579, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 880.7755374526977, "W": 66.16, "J_1KI": 107.70060374818999, "W_1KI": 8.089997554414282, "W_D": 30.69249999999999, "J_D": 408.60343384623513, "W_D_1KI": 3.7530569821472226, "J_D_1KI": 0.4589211276775767}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 8146, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.451539278030396, "TIME_S_1KI": 1.283027164010606, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 878.7411267375946, "W": 66.37, "J_1KI": 107.87394141144054, "W_1KI": 8.147557083231035, "W_D": 30.001250000000006, "J_D": 397.2176017558576, "W_D_1KI": 3.682942548490057, "J_D_1KI": 0.45211668898724}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.output
index b9e58d8..5956044 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,54 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.14169001579284668}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.14194607734680176}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     246,     493,  ..., 1249505,
+                            1249740, 1250000]),
+       col_indices=tensor([  15,   19,  107,  ..., 4903, 4917, 4933]),
+       values=tensor([0.8230, 0.2997, 0.5773,  ..., 0.5953, 0.5313, 0.7363]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.8375, 0.6095, 0.7115,  ..., 0.7001, 0.7437, 0.1414])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 0.14194607734680176 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '7397', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.53364109992981}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     264,     530,  ..., 1249523,
+                            1249761, 1250000]),
+       col_indices=tensor([  49,   51,   79,  ..., 4928, 4960, 4997]),
+       values=tensor([0.7644, 0.4849, 0.3773,  ..., 0.1775, 0.5516, 0.1427]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6007, 0.0753, 0.6199,  ..., 0.2126, 0.6661, 0.0074])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 9.53364109992981 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '8146', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.451539278030396}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([      0,     264,     521,  ..., 1249500,
-                            1249758, 1250000]),
-       col_indices=tensor([  22,   35,   54,  ..., 4954, 4963, 4982]),
-       values=tensor([0.3715, 0.6699, 0.1465,  ..., 0.9132, 0.2376, 0.1878]),
+                            1249752, 1250000]),
+       col_indices=tensor([  43,   45,  102,  ..., 4936, 4979, 4982]),
+       values=tensor([0.0537, 0.1465, 0.6339,  ..., 0.3428, 0.4774, 0.7151]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.3853, 0.7833, 0.5244,  ..., 0.5756, 0.3818, 0.8103])
+tensor([0.1318, 0.3066, 0.8294,  ..., 0.3003, 0.7098, 0.9249])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 0.14169001579284668 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '7410', '-ss', '5000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.513462543487549}
+Time: 10.451539278030396 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     245,     477,  ..., 1249530,
-                            1249759, 1250000]),
-       col_indices=tensor([  15,   78,  164,  ..., 4968, 4978, 4992]),
-       values=tensor([0.5947, 0.7215, 0.2123,  ..., 0.7493, 0.6227, 0.0355]),
+tensor(crow_indices=tensor([      0,     264,     521,  ..., 1249500,
+                            1249752, 1250000]),
+       col_indices=tensor([  43,   45,  102,  ..., 4936, 4979, 4982]),
+       values=tensor([0.0537, 0.1465, 0.6339,  ..., 0.3428, 0.4774, 0.7151]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.4245, 0.9857, 0.9704,  ..., 0.1643, 0.0459, 0.0545])
+tensor([0.1318, 0.3066, 0.8294,  ..., 0.3003, 0.7098, 0.9249])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,50 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 9.513462543487549 seconds
+Time: 10.451539278030396 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '8178', '-ss', '5000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.511467218399048}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     243,     531,  ..., 1249480,
-                            1249728, 1250000]),
-       col_indices=tensor([  10,   29,   31,  ..., 4961, 4980, 4981]),
-       values=tensor([0.2878, 0.6436, 0.4714,  ..., 0.0074, 0.1096, 0.3758]),
-       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.4226, 0.4894, 0.7354,  ..., 0.5546, 0.7888, 0.4627])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 1250000
-Density: 0.05
-Time: 10.511467218399048 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     243,     531,  ..., 1249480,
-                            1249728, 1250000]),
-       col_indices=tensor([  10,   29,   31,  ..., 4961, 4980, 4981]),
-       values=tensor([0.2878, 0.6436, 0.4714,  ..., 0.0074, 0.1096, 0.3758]),
-       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.4226, 0.4894, 0.7354,  ..., 0.5546, 0.7888, 0.4627])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 1250000
-Density: 0.05
-Time: 10.511467218399048 seconds
-
-[40.21, 39.14, 39.56, 39.02, 39.43, 41.29, 39.49, 39.07, 39.13, 39.19]
-[66.16]
-13.31281042098999
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 8178, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.511467218399048, 'TIME_S_1KI': 1.2853347051111579, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 880.7755374526977, 'W': 66.16}
-[40.21, 39.14, 39.56, 39.02, 39.43, 41.29, 39.49, 39.07, 39.13, 39.19, 40.14, 39.04, 40.64, 38.87, 39.1, 38.93, 39.37, 39.03, 38.8, 39.34]
-709.3500000000001
-35.46750000000001
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 8178, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.511467218399048, 'TIME_S_1KI': 1.2853347051111579, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 880.7755374526977, 'W': 66.16, 'J_1KI': 107.70060374818999, 'W_1KI': 8.089997554414282, 'W_D': 30.69249999999999, 'J_D': 408.60343384623513, 'W_D_1KI': 3.7530569821472226, 'J_D_1KI': 0.4589211276775767}
+[54.34, 40.8, 39.08, 38.82, 38.81, 39.44, 40.48, 39.19, 39.4, 39.2]
+[66.37]
+13.240035057067871
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 8146, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.451539278030396, 'TIME_S_1KI': 1.283027164010606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 878.7411267375946, 'W': 66.37}
+[54.34, 40.8, 39.08, 38.82, 38.81, 39.44, 40.48, 39.19, 39.4, 39.2, 39.48, 38.91, 38.85, 39.0, 38.87, 39.13, 38.77, 39.03, 39.22, 66.13]
+727.375
+36.36875
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 8146, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.451539278030396, 'TIME_S_1KI': 1.283027164010606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 878.7411267375946, 'W': 66.37, 'J_1KI': 107.87394141144054, 'W_1KI': 8.147557083231035, 'W_D': 30.001250000000006, 'J_D': 397.2176017558576, 'W_D_1KI': 3.682942548490057, 'J_D_1KI': 0.45211668898724}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.json
index cc784c7..d4894d2 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3463, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.092161417007446, "TIME_S_1KI": 2.9142828232767677, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 888.6800115585327, "W": 67.72, "J_1KI": 256.6214298465298, "W_1KI": 19.555298873808837, "W_D": 32.353, "J_D": 424.56385726451873, "W_D_1KI": 9.34247762056021, "J_D_1KI": 2.697798908622642}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3523, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.174333810806274, "TIME_S_1KI": 2.887974399888241, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 994.8108104133606, "W": 75.11, "J_1KI": 282.37604610086873, "W_1KI": 21.319897814362758, "W_D": 26.86425, "J_D": 355.8094303507805, "W_D_1KI": 7.625390292364462, "J_D_1KI": 2.1644593506569576}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.output
index 8f92a77..203e1f8 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.30318737030029297}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.2979764938354492}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     515,    1018,  ..., 2498999,
-                            2499493, 2500000]),
-       col_indices=tensor([   4,   21,   24,  ..., 4955, 4957, 4967]),
-       values=tensor([0.5325, 0.9134, 0.0336,  ..., 0.9679, 0.3618, 0.7033]),
+tensor(crow_indices=tensor([      0,     469,     956,  ..., 2498994,
+                            2499509, 2500000]),
+       col_indices=tensor([   5,   10,   14,  ..., 4977, 4982, 4995]),
+       values=tensor([0.8447, 0.4119, 0.6745,  ..., 0.6408, 0.0559, 0.2121]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8211, 0.4248, 0.8998,  ..., 0.9656, 0.3613, 0.2992])
+tensor([0.8219, 0.5051, 0.6624,  ..., 0.8040, 0.6012, 0.0437])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 0.30318737030029297 seconds
+Time: 0.2979764938354492 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3463', '-ss', '5000', '-sd', '0.1', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.092161417007446}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3523', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.174333810806274}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     498,    1000,  ..., 2499016,
-                            2499523, 2500000]),
-       col_indices=tensor([   2,   43,   50,  ..., 4978, 4986, 4997]),
-       values=tensor([0.9352, 0.1649, 0.8628,  ..., 0.6965, 0.7003, 0.5455]),
+tensor(crow_indices=tensor([      0,     464,     974,  ..., 2499027,
+                            2499519, 2500000]),
+       col_indices=tensor([  10,   16,   20,  ..., 4979, 4983, 4992]),
+       values=tensor([0.3603, 0.6762, 0.3613,  ..., 0.2093, 0.6480, 0.9505]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0499, 0.6757, 0.4614,  ..., 0.0542, 0.7848, 0.0169])
+tensor([0.7173, 0.5165, 0.9096,  ..., 0.8929, 0.9385, 0.9563])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.092161417007446 seconds
+Time: 10.174333810806274 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     498,    1000,  ..., 2499016,
-                            2499523, 2500000]),
-       col_indices=tensor([   2,   43,   50,  ..., 4978, 4986, 4997]),
-       values=tensor([0.9352, 0.1649, 0.8628,  ..., 0.6965, 0.7003, 0.5455]),
+tensor(crow_indices=tensor([      0,     464,     974,  ..., 2499027,
+                            2499519, 2500000]),
+       col_indices=tensor([  10,   16,   20,  ..., 4979, 4983, 4992]),
+       values=tensor([0.3603, 0.6762, 0.3613,  ..., 0.2093, 0.6480, 0.9505]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0499, 0.6757, 0.4614,  ..., 0.0542, 0.7848, 0.0169])
+tensor([0.7173, 0.5165, 0.9096,  ..., 0.8929, 0.9385, 0.9563])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.092161417007446 seconds
+Time: 10.174333810806274 seconds
 
-[40.21, 38.8, 38.93, 38.71, 39.03, 38.9, 38.74, 39.19, 39.25, 38.74]
-[67.72]
-13.122859001159668
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3463, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.092161417007446, 'TIME_S_1KI': 2.9142828232767677, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.6800115585327, 'W': 67.72}
-[40.21, 38.8, 38.93, 38.71, 39.03, 38.9, 38.74, 39.19, 39.25, 38.74, 40.21, 38.79, 39.15, 38.78, 38.85, 38.73, 44.44, 39.11, 38.88, 38.96]
-707.3399999999999
-35.367
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3463, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.092161417007446, 'TIME_S_1KI': 2.9142828232767677, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.6800115585327, 'W': 67.72, 'J_1KI': 256.6214298465298, 'W_1KI': 19.555298873808837, 'W_D': 32.353, 'J_D': 424.56385726451873, 'W_D_1KI': 9.34247762056021, 'J_D_1KI': 2.697798908622642}
+[64.94, 67.66, 66.1, 67.43, 68.81, 68.75, 66.05, 68.95, 70.43, 66.03]
+[75.11]
+13.244718551635742
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3523, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.174333810806274, 'TIME_S_1KI': 2.887974399888241, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 994.8108104133606, 'W': 75.11}
+[64.94, 67.66, 66.1, 67.43, 68.81, 68.75, 66.05, 68.95, 70.43, 66.03, 40.1, 39.77, 40.59, 38.97, 39.63, 39.07, 38.99, 39.02, 39.56, 39.2]
+964.915
+48.24575
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3523, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.174333810806274, 'TIME_S_1KI': 2.887974399888241, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 994.8108104133606, 'W': 75.11, 'J_1KI': 282.37604610086873, 'W_1KI': 21.319897814362758, 'W_D': 26.86425, 'J_D': 355.8094303507805, 'W_D_1KI': 7.625390292364462, 'J_D_1KI': 2.1644593506569576}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.json
index b65b863..33229e8 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1741, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.298704862594604, "TIME_S_1KI": 5.915396245028492, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1027.8759766101837, "W": 74.46, "J_1KI": 590.3940129868946, "W_1KI": 42.76852383687535, "W_D": 39.113499999999995, "J_D": 539.9385846245289, "W_D_1KI": 22.466111430212518, "J_D_1KI": 12.904142119593635}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1742, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.279394626617432, "TIME_S_1KI": 5.900915399895196, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1034.989099354744, "W": 74.74, "J_1KI": 594.1384037627693, "W_1KI": 42.904707233065444, "W_D": 38.945249999999994, "J_D": 539.3083920477031, "W_D_1KI": 22.356630309988518, "J_D_1KI": 12.833886515492836}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.output
index 987a685..57a2980 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.6030943393707275}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.6026511192321777}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1017,    2015,  ..., 4997969,
-                            4998981, 5000000]),
-       col_indices=tensor([   0,   31,   34,  ..., 4984, 4989, 4995]),
-       values=tensor([0.9331, 0.7841, 0.5601,  ..., 0.4749, 0.8668, 0.1575]),
+tensor(crow_indices=tensor([      0,    1041,    1982,  ..., 4998017,
+                            4999003, 5000000]),
+       col_indices=tensor([   5,    7,    9,  ..., 4978, 4988, 4995]),
+       values=tensor([0.5465, 0.8152, 0.9813,  ..., 0.3845, 0.7573, 0.1959]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.1492, 0.3030, 0.4894,  ..., 0.2440, 0.4033, 0.0238])
+tensor([0.4559, 0.3153, 0.5904,  ..., 0.7273, 0.1313, 0.8409])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 0.6030943393707275 seconds
+Time: 0.6026511192321777 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1741', '-ss', '5000', '-sd', '0.2', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.298704862594604}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1742', '-ss', '5000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.279394626617432}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     961,    1935,  ..., 4998003,
-                            4999013, 5000000]),
-       col_indices=tensor([   0,    3,    5,  ..., 4968, 4970, 4989]),
-       values=tensor([0.3710, 0.5603, 0.2176,  ..., 0.5072, 0.2694, 0.8987]),
+tensor(crow_indices=tensor([      0,    1007,    2030,  ..., 4998041,
+                            4999049, 5000000]),
+       col_indices=tensor([   0,    1,    4,  ..., 4982, 4988, 4994]),
+       values=tensor([0.5108, 0.8042, 0.8113,  ..., 0.2803, 0.6803, 0.4820]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.6029, 0.2962, 0.8345,  ..., 0.0907, 0.1121, 0.2666])
+tensor([0.3972, 0.0450, 0.2251,  ..., 0.8758, 0.6674, 0.2144])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.298704862594604 seconds
+Time: 10.279394626617432 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     961,    1935,  ..., 4998003,
-                            4999013, 5000000]),
-       col_indices=tensor([   0,    3,    5,  ..., 4968, 4970, 4989]),
-       values=tensor([0.3710, 0.5603, 0.2176,  ..., 0.5072, 0.2694, 0.8987]),
+tensor(crow_indices=tensor([      0,    1007,    2030,  ..., 4998041,
+                            4999049, 5000000]),
+       col_indices=tensor([   0,    1,    4,  ..., 4982, 4988, 4994]),
+       values=tensor([0.5108, 0.8042, 0.8113,  ..., 0.2803, 0.6803, 0.4820]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.6029, 0.2962, 0.8345,  ..., 0.0907, 0.1121, 0.2666])
+tensor([0.3972, 0.0450, 0.2251,  ..., 0.8758, 0.6674, 0.2144])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.298704862594604 seconds
+Time: 10.279394626617432 seconds
 
-[39.56, 39.38, 38.97, 38.88, 38.82, 39.21, 39.23, 39.16, 39.31, 39.25]
-[74.46]
-13.804404735565186
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1741, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.298704862594604, 'TIME_S_1KI': 5.915396245028492, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1027.8759766101837, 'W': 74.46}
-[39.56, 39.38, 38.97, 38.88, 38.82, 39.21, 39.23, 39.16, 39.31, 39.25, 40.48, 40.05, 39.23, 38.85, 39.62, 39.32, 38.87, 39.04, 39.64, 39.41]
-706.93
-35.3465
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1741, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.298704862594604, 'TIME_S_1KI': 5.915396245028492, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1027.8759766101837, 'W': 74.46, 'J_1KI': 590.3940129868946, 'W_1KI': 42.76852383687535, 'W_D': 39.113499999999995, 'J_D': 539.9385846245289, 'W_D_1KI': 22.466111430212518, 'J_D_1KI': 12.904142119593635}
+[39.81, 39.1, 38.99, 40.23, 39.14, 39.0, 39.11, 38.99, 44.61, 38.94]
+[74.74]
+13.84786057472229
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1742, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.279394626617432, 'TIME_S_1KI': 5.900915399895196, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1034.989099354744, 'W': 74.74}
+[39.81, 39.1, 38.99, 40.23, 39.14, 39.0, 39.11, 38.99, 44.61, 38.94, 39.65, 38.98, 38.94, 38.86, 44.2, 39.35, 39.05, 39.58, 38.86, 39.41]
+715.895
+35.79475
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1742, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.279394626617432, 'TIME_S_1KI': 5.900915399895196, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1034.989099354744, 'W': 74.74, 'J_1KI': 594.1384037627693, 'W_1KI': 42.904707233065444, 'W_D': 38.945249999999994, 'J_D': 539.3083920477031, 'W_D_1KI': 22.356630309988518, 'J_D_1KI': 12.833886515492836}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.json
index 20f95b6..f0630a3 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1166, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.334495306015015, "TIME_S_1KI": 8.8632035214537, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1095.1619911956786, "W": 75.64, "J_1KI": 939.2469907338582, "W_1KI": 64.8713550600343, "W_D": 40.43425, "J_D": 585.4316993985176, "W_D_1KI": 34.67774442538593, "J_D_1KI": 29.74077566499651}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1170, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.355679035186768, "TIME_S_1KI": 8.851007722381853, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1094.0326872682572, "W": 75.42, "J_1KI": 935.0706728788523, "W_1KI": 64.46153846153845, "W_D": 39.963750000000005, "J_D": 579.7089473059774, "W_D_1KI": 34.157051282051285, "J_D_1KI": 29.194060924830158}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.output
index be3ca1f..eff6595 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.9000704288482666}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.8971807956695557}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1542,    2985,  ..., 7497037,
-                            7498525, 7500000]),
-       col_indices=tensor([   0,    1,    6,  ..., 4988, 4994, 4997]),
-       values=tensor([0.4057, 0.2930, 0.9549,  ..., 0.4574, 0.5414, 0.6416]),
+tensor(crow_indices=tensor([      0,    1483,    2970,  ..., 7497043,
+                            7498532, 7500000]),
+       col_indices=tensor([   0,    4,    8,  ..., 4996, 4998, 4999]),
+       values=tensor([0.5744, 0.9844, 0.0965,  ..., 0.2441, 0.2287, 0.5506]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.4314, 0.0098, 0.8060,  ..., 0.6655, 0.0522, 0.0757])
+tensor([0.1028, 0.0300, 0.5873,  ..., 0.0934, 0.0185, 0.3679])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 0.9000704288482666 seconds
+Time: 0.8971807956695557 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1166', '-ss', '5000', '-sd', '0.3', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.334495306015015}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1170', '-ss', '5000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.355679035186768}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1525,    3027,  ..., 7497015,
-                            7498498, 7500000]),
-       col_indices=tensor([  12,   15,   18,  ..., 4991, 4994, 4995]),
-       values=tensor([0.8648, 0.2387, 0.0206,  ..., 0.7504, 0.0755, 0.9898]),
+tensor(crow_indices=tensor([      0,    1495,    2989,  ..., 7497030,
+                            7498472, 7500000]),
+       col_indices=tensor([   0,    9,   11,  ..., 4987, 4988, 4996]),
+       values=tensor([0.5267, 0.7493, 0.0080,  ..., 0.6762, 0.4849, 0.8981]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.0126, 0.2581, 0.5840,  ..., 0.5862, 0.5778, 0.3525])
+tensor([0.2074, 0.3614, 0.1652,  ..., 0.2488, 0.9353, 0.1679])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.334495306015015 seconds
+Time: 10.355679035186768 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1525,    3027,  ..., 7497015,
-                            7498498, 7500000]),
-       col_indices=tensor([  12,   15,   18,  ..., 4991, 4994, 4995]),
-       values=tensor([0.8648, 0.2387, 0.0206,  ..., 0.7504, 0.0755, 0.9898]),
+tensor(crow_indices=tensor([      0,    1495,    2989,  ..., 7497030,
+                            7498472, 7500000]),
+       col_indices=tensor([   0,    9,   11,  ..., 4987, 4988, 4996]),
+       values=tensor([0.5267, 0.7493, 0.0080,  ..., 0.6762, 0.4849, 0.8981]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.0126, 0.2581, 0.5840,  ..., 0.5862, 0.5778, 0.3525])
+tensor([0.2074, 0.3614, 0.1652,  ..., 0.2488, 0.9353, 0.1679])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.334495306015015 seconds
+Time: 10.355679035186768 seconds
 
-[41.12, 38.83, 38.85, 38.96, 39.38, 38.75, 39.14, 38.88, 38.85, 38.77]
-[75.64]
-14.478609085083008
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1166, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.334495306015015, 'TIME_S_1KI': 8.8632035214537, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1095.1619911956786, 'W': 75.64}
-[41.12, 38.83, 38.85, 38.96, 39.38, 38.75, 39.14, 38.88, 38.85, 38.77, 40.18, 38.79, 39.32, 38.91, 39.22, 38.92, 39.3, 39.28, 39.3, 38.8]
-704.115
-35.20575
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1166, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.334495306015015, 'TIME_S_1KI': 8.8632035214537, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1095.1619911956786, 'W': 75.64, 'J_1KI': 939.2469907338582, 'W_1KI': 64.8713550600343, 'W_D': 40.43425, 'J_D': 585.4316993985176, 'W_D_1KI': 34.67774442538593, 'J_D_1KI': 29.74077566499651}
+[41.13, 39.47, 39.12, 39.58, 39.41, 39.34, 39.14, 39.18, 39.03, 38.88]
+[75.42]
+14.505869626998901
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.355679035186768, 'TIME_S_1KI': 8.851007722381853, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1094.0326872682572, 'W': 75.42}
+[41.13, 39.47, 39.12, 39.58, 39.41, 39.34, 39.14, 39.18, 39.03, 38.88, 39.75, 39.09, 39.44, 39.56, 39.53, 39.42, 39.86, 39.14, 39.21, 39.45]
+709.125
+35.45625
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.355679035186768, 'TIME_S_1KI': 8.851007722381853, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1094.0326872682572, 'W': 75.42, 'J_1KI': 935.0706728788523, 'W_1KI': 64.46153846153845, 'W_D': 39.963750000000005, 'J_D': 579.7089473059774, 'W_D_1KI': 34.157051282051285, 'J_D_1KI': 29.194060924830158}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.json
index 973144c..10a3a18 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 704, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.429930925369263, "TIME_S_1KI": 14.815242791717703, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1157.2951932907106, "W": 76.7, "J_1KI": 1643.8852177424865, "W_1KI": 108.94886363636364, "W_D": 41.2785, "J_D": 622.8345454530717, "W_D_1KI": 58.63423295454546, "J_D_1KI": 83.2872627195248}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 701, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.335088968276978, "TIME_S_1KI": 14.743350881992836, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1160.8654913902283, "W": 77.0, "J_1KI": 1656.0135397863455, "W_1KI": 109.84308131241085, "W_D": 40.2995, "J_D": 607.562322990656, "W_D_1KI": 57.488587731811705, "J_D_1KI": 82.00939762027348}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.output
index 0f653eb..f7bd8e4 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 1.4912726879119873}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 1.496537685394287}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1951,     3944,  ...,  9995965,
-                             9997988, 10000000]),
-       col_indices=tensor([   6,   14,   15,  ..., 4996, 4997, 4998]),
-       values=tensor([0.7071, 0.0905, 0.6037,  ..., 0.4689, 0.3914, 0.3516]),
+tensor(crow_indices=tensor([       0,     2015,     4077,  ...,  9996098,
+                             9998057, 10000000]),
+       col_indices=tensor([   1,   12,   18,  ..., 4995, 4997, 4998]),
+       values=tensor([0.7308, 0.3114, 0.6641,  ..., 0.6125, 0.8831, 0.5870]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.7487, 0.9804, 0.4485,  ..., 0.3781, 0.3818, 0.0598])
+tensor([0.5034, 0.4347, 0.2307,  ..., 0.8205, 0.5767, 0.4764])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 1.4912726879119873 seconds
+Time: 1.496537685394287 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '704', '-ss', '5000', '-sd', '0.4', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.429930925369263}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '701', '-ss', '5000', '-sd', '0.4', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.335088968276978}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1962,     3997,  ...,  9995983,
-                             9998031, 10000000]),
-       col_indices=tensor([   2,    3,    4,  ..., 4989, 4991, 4999]),
-       values=tensor([0.3156, 0.5704, 0.4238,  ..., 0.9413, 0.7746, 0.6098]),
+tensor(crow_indices=tensor([       0,     2022,     3993,  ...,  9996030,
+                             9998001, 10000000]),
+       col_indices=tensor([   3,    4,    6,  ..., 4993, 4995, 4999]),
+       values=tensor([0.7874, 0.2302, 0.6902,  ..., 0.9237, 0.4350, 0.1889]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3215, 0.3275, 0.6323,  ..., 0.0024, 0.8893, 0.0654])
+tensor([0.0065, 0.1220, 0.5563,  ..., 0.5954, 0.2879, 0.4390])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 10.429930925369263 seconds
+Time: 10.335088968276978 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1962,     3997,  ...,  9995983,
-                             9998031, 10000000]),
-       col_indices=tensor([   2,    3,    4,  ..., 4989, 4991, 4999]),
-       values=tensor([0.3156, 0.5704, 0.4238,  ..., 0.9413, 0.7746, 0.6098]),
+tensor(crow_indices=tensor([       0,     2022,     3993,  ...,  9996030,
+                             9998001, 10000000]),
+       col_indices=tensor([   3,    4,    6,  ..., 4993, 4995, 4999]),
+       values=tensor([0.7874, 0.2302, 0.6902,  ..., 0.9237, 0.4350, 0.1889]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3215, 0.3275, 0.6323,  ..., 0.0024, 0.8893, 0.0654])
+tensor([0.0065, 0.1220, 0.5563,  ..., 0.5954, 0.2879, 0.4390])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 10.429930925369263 seconds
+Time: 10.335088968276978 seconds
 
-[40.15, 39.37, 39.68, 38.87, 39.22, 39.13, 41.47, 38.91, 39.0, 39.01]
-[76.7]
-15.088594436645508
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 704, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.429930925369263, 'TIME_S_1KI': 14.815242791717703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1157.2951932907106, 'W': 76.7}
-[40.15, 39.37, 39.68, 38.87, 39.22, 39.13, 41.47, 38.91, 39.0, 39.01, 40.04, 39.56, 38.96, 39.33, 38.96, 38.95, 39.21, 38.91, 38.9, 40.8]
-708.4300000000001
-35.4215
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 704, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.429930925369263, 'TIME_S_1KI': 14.815242791717703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1157.2951932907106, 'W': 76.7, 'J_1KI': 1643.8852177424865, 'W_1KI': 108.94886363636364, 'W_D': 41.2785, 'J_D': 622.8345454530717, 'W_D_1KI': 58.63423295454546, 'J_D_1KI': 83.2872627195248}
+[47.96, 39.51, 39.1, 38.93, 39.13, 39.04, 39.5, 38.9, 39.29, 44.42]
+[77.0]
+15.076175212860107
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 701, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.335088968276978, 'TIME_S_1KI': 14.743350881992836, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1160.8654913902283, 'W': 77.0}
+[47.96, 39.51, 39.1, 38.93, 39.13, 39.04, 39.5, 38.9, 39.29, 44.42, 40.01, 39.61, 39.18, 38.98, 52.5, 47.1, 39.47, 39.0, 39.03, 39.09]
+734.01
+36.7005
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 701, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.335088968276978, 'TIME_S_1KI': 14.743350881992836, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1160.8654913902283, 'W': 77.0, 'J_1KI': 1656.0135397863455, 'W_1KI': 109.84308131241085, 'W_D': 40.2995, 'J_D': 607.562322990656, 'W_D_1KI': 57.488587731811705, 'J_D_1KI': 82.00939762027348}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.json
index 70ebf3a..bce4b14 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 569, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.483191013336182, "TIME_S_1KI": 18.42388578793705, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1197.8375442028046, "W": 76.6, "J_1KI": 2105.162643590166, "W_1KI": 134.62214411247803, "W_D": 41.0435, "J_D": 641.8204340142012, "W_D_1KI": 72.13268892794376, "J_D_1KI": 126.77098229867093}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 566, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.359947919845581, "TIME_S_1KI": 18.303794911387953, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1202.7482558536528, "W": 76.69, "J_1KI": 2124.996918469351, "W_1KI": 135.49469964664308, "W_D": 41.312749999999994, "J_D": 647.9180858914851, "W_D_1KI": 72.99072438162543, "J_D_1KI": 128.9588769993382}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.output
index d4faa36..36b5688 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 1.8439099788665771}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 1.8520567417144775}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2531,     5020,  ..., 12494917,
-                            12497450, 12500000]),
-       col_indices=tensor([   0,    1,    9,  ..., 4992, 4994, 4999]),
-       values=tensor([0.6676, 0.2754, 0.2712,  ..., 0.4447, 0.2547, 0.8500]),
+tensor(crow_indices=tensor([       0,     2533,     5033,  ..., 12494920,
+                            12497517, 12500000]),
+       col_indices=tensor([   3,   11,   12,  ..., 4993, 4996, 4999]),
+       values=tensor([0.5117, 0.6458, 0.5264,  ..., 0.9665, 0.6027, 0.6828]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.9338, 0.0413, 0.5968,  ..., 0.6366, 0.2029, 0.7249])
+tensor([0.3100, 0.6010, 0.6518,  ..., 0.6095, 0.4947, 0.0178])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,20 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 1.8439099788665771 seconds
+Time: 1.8520567417144775 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '569', '-ss', '5000', '-sd', '0.5', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.483191013336182}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '566', '-ss', '5000', '-sd', '0.5', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.359947919845581}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2491,     4957,  ..., 12495037,
-                            12497545, 12500000]),
-       col_indices=tensor([   0,    2,    5,  ..., 4995, 4998, 4999]),
-       values=tensor([0.5758, 0.7291, 0.3910,  ..., 0.8483, 0.9816, 0.9388]),
+tensor(crow_indices=tensor([       0,     2504,     4980,  ..., 12495045,
+                            12497491, 12500000]),
+       col_indices=tensor([   1,    4,    6,  ..., 4991, 4992, 4994]),
+       values=tensor([0.0596, 0.1686, 0.0698,  ..., 0.4257, 0.0613, 0.7539]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.0835, 0.8623, 0.0534,  ..., 0.1116, 0.3605, 0.8512])
+tensor([5.7186e-01, 1.5043e-01, 1.3815e-01,  ..., 6.7949e-01, 6.0593e-01,
+        4.5055e-04])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +37,17 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 10.483191013336182 seconds
+Time: 10.359947919845581 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2491,     4957,  ..., 12495037,
-                            12497545, 12500000]),
-       col_indices=tensor([   0,    2,    5,  ..., 4995, 4998, 4999]),
-       values=tensor([0.5758, 0.7291, 0.3910,  ..., 0.8483, 0.9816, 0.9388]),
+tensor(crow_indices=tensor([       0,     2504,     4980,  ..., 12495045,
+                            12497491, 12500000]),
+       col_indices=tensor([   1,    4,    6,  ..., 4991, 4992, 4994]),
+       values=tensor([0.0596, 0.1686, 0.0698,  ..., 0.4257, 0.0613, 0.7539]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.0835, 0.8623, 0.0534,  ..., 0.1116, 0.3605, 0.8512])
+tensor([5.7186e-01, 1.5043e-01, 1.3815e-01,  ..., 6.7949e-01, 6.0593e-01,
+        4.5055e-04])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +55,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 10.483191013336182 seconds
+Time: 10.359947919845581 seconds
 
-[39.92, 38.82, 39.41, 39.19, 39.02, 39.24, 39.27, 38.82, 38.86, 38.89]
-[76.6]
-15.637565851211548
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 569, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.483191013336182, 'TIME_S_1KI': 18.42388578793705, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1197.8375442028046, 'W': 76.6}
-[39.92, 38.82, 39.41, 39.19, 39.02, 39.24, 39.27, 38.82, 38.86, 38.89, 40.74, 39.81, 39.32, 39.32, 39.28, 44.23, 39.36, 38.92, 39.11, 38.75]
-711.1299999999999
-35.55649999999999
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 569, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.483191013336182, 'TIME_S_1KI': 18.42388578793705, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1197.8375442028046, 'W': 76.6, 'J_1KI': 2105.162643590166, 'W_1KI': 134.62214411247803, 'W_D': 41.0435, 'J_D': 641.8204340142012, 'W_D_1KI': 72.13268892794376, 'J_D_1KI': 126.77098229867093}
+[39.65, 38.86, 39.36, 38.85, 40.08, 38.82, 39.08, 40.17, 39.07, 38.83]
+[76.69]
+15.683247566223145
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.359947919845581, 'TIME_S_1KI': 18.303794911387953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1202.7482558536528, 'W': 76.69}
+[39.65, 38.86, 39.36, 38.85, 40.08, 38.82, 39.08, 40.17, 39.07, 38.83, 39.68, 39.84, 39.36, 38.95, 38.9, 39.34, 39.9, 38.81, 39.44, 39.27]
+707.5450000000001
+35.377250000000004
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.359947919845581, 'TIME_S_1KI': 18.303794911387953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1202.7482558536528, 'W': 76.69, 'J_1KI': 2124.996918469351, 'W_1KI': 135.49469964664308, 'W_D': 41.312749999999994, 'J_D': 647.9180858914851, 'W_D_1KI': 72.99072438162543, 'J_D_1KI': 128.9588769993382}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.json
index c3ed634..d7cbf34 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 569391, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.146198511123657, "TIME_S_1KI": 0.017819386873209546, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 837.5037681818008, "W": 65.05, "J_1KI": 1.4708763717406856, "W_1KI": 0.11424486864035434, "W_D": 29.76475, "J_D": 383.2143010605574, "W_D_1KI": 0.05227471105092985, "J_D_1KI": 9.180810910416543e-05}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 557047, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.404626369476318, "TIME_S_1KI": 0.018678184012258063, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 821.7697546434403, "W": 64.83, "J_1KI": 1.4752251688698446, "W_1KI": 0.11638156205849774, "W_D": 29.701, "J_D": 376.4828548922539, "W_D_1KI": 0.0533186607234219, "J_D_1KI": 9.571662844144552e-05}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.output
index 6482c19..23ac588 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,75 +1,75 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.025912761688232422}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.05718708038330078}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([2955, 4682, 3449, 1969, 3854,  251, 2319,  668,  498,
-                           3572, 3854, 2322, 4794,  541, 1204, 3498,  557, 2286,
-                           3599, 1633,  263,  572,  401, 2359, 4315,  911, 3580,
-                            647, 4452, 4876, 2180, 2875, 4244, 3344, 2209,  559,
-                           1303, 3655, 2944, 2633, 4973, 1628, 4101,  143, 4999,
-                           4517, 2519, 3173, 1189, 2853, 1263, 1640, 2218, 2187,
-                            500,  639, 1141, 4234,  755,  459,  531, 3206, 4041,
-                           4859, 2211, 4404, 1761, 2224, 3743, 1719, 4799, 2055,
-                           4619, 4316, 4729, 1778, 2613, 3964, 2169,  739, 4154,
-                           1973, 1793, 4308,  801, 3310,  342, 3095,  520, 3764,
-                           4097, 3032, 1897, 3411, 2128, 1565, 3684, 2810, 2598,
-                           1157, 2540, 3590, 4555,  145, 3631, 3914,  308,   84,
-                           3223, 2928, 1083, 1157, 2416, 2092, 1789, 4234, 2339,
-                            866, 1332,  582,  480,  407, 2460,  594, 1184, 1302,
-                           4554, 3210,  212, 2368, 3876, 3742, 2272, 1393,  406,
-                             80, 3371, 4169, 2118,  452, 2986,  932,   49, 3160,
-                           2327,  713, 2877, 3484, 2257,  182, 3066, 1789,  610,
-                           3257, 2119, 4757,  577,  310, 2782,  116, 2689, 2876,
-                           2666,  144, 4643, 3955,  517, 1439, 3012, 4289, 3484,
-                           1701, 3447, 1824, 1951, 3681, 4272, 2740, 4790, 1804,
-                           3833, 1146,  672, 4424, 3920, 2173, 2155, 2090, 4174,
-                           4084,  560, 3425, 3143, 3470, 1712, 3489, 1811, 1766,
-                           4872, 3288,  771, 4485, 3269, 2695, 4584,  535,  200,
-                           2606, 3547, 3737,  102,  696,  271, 2048, 2470, 1740,
-                           2219,  829, 4785, 1264, 2283, 4517, 1344, 1726,  844,
-                           2384, 4310,   42, 3459, 2017, 2199, 3093, 4339,  313,
-                           1332,  303, 3733, 2074, 4094, 4006,  429, 1830, 3447,
-                           3735, 3446, 1158, 2156, 2171, 2458,  989]),
-       values=tensor([0.1546, 0.0338, 0.3832, 0.6845, 0.5359, 0.2115, 0.5134,
-                      0.5236, 0.8368, 0.0286, 0.8691, 0.3848, 0.7412, 0.7376,
-                      0.0492, 0.2561, 0.4285, 0.6403, 0.4264, 0.9823, 0.2432,
-                      0.9496, 0.9648, 0.1994, 0.5806, 0.9987, 0.8946, 0.3796,
-                      0.9742, 0.5421, 0.7745, 0.0707, 0.4271, 0.7771, 0.6813,
-                      0.4916, 0.5119, 0.7062, 0.2752, 0.9391, 0.5947, 0.6220,
-                      0.1073, 0.5082, 0.8956, 0.6658, 0.1040, 0.0454, 0.1560,
-                      0.7150, 0.4739, 0.5494, 0.1706, 0.6723, 0.3127, 0.2460,
-                      0.1585, 0.6938, 0.6937, 0.2200, 0.6734, 0.5021, 0.7544,
-                      0.1720, 0.3861, 0.8179, 0.3810, 0.0801, 0.6567, 0.1205,
-                      0.2925, 0.1255, 0.7882, 0.3753, 0.2290, 0.4877, 0.9183,
-                      0.7718, 0.3047, 0.6194, 0.2851, 0.3419, 0.7256, 0.2356,
-                      0.5102, 0.4050, 0.2487, 0.5983, 0.2252, 0.4330, 0.1968,
-                      0.1306, 0.4006, 0.3092, 0.2645, 0.4635, 0.3587, 0.7536,
-                      0.7019, 0.0792, 0.1678, 0.7485, 0.9817, 0.9065, 0.9994,
-                      0.3911, 0.8833, 0.5411, 0.3679, 0.9711, 0.9853, 0.6437,
-                      0.7861, 0.5048, 0.2591, 0.7726, 0.6174, 0.1004, 0.1489,
-                      0.6017, 0.8836, 0.5571, 0.7423, 0.4796, 0.5887, 0.7010,
-                      0.6198, 0.7601, 0.9790, 0.0717, 0.8254, 0.1983, 0.6497,
-                      0.9092, 0.9977, 0.7279, 0.3767, 0.5564, 0.8901, 0.2896,
-                      0.4914, 0.7288, 0.7282, 0.1569, 0.7491, 0.2024, 0.5103,
-                      0.2006, 0.2616, 0.3680, 0.8784, 0.5095, 0.3100, 0.5468,
-                      0.7473, 0.8067, 0.1566, 0.7922, 0.7836, 0.0734, 0.2895,
-                      0.0658, 0.3948, 0.1442, 0.9166, 0.4276, 0.4709, 0.0305,
-                      0.5373, 0.3415, 0.0906, 0.7116, 0.3303, 0.4381, 0.5263,
-                      0.3654, 0.9325, 0.0370, 0.6385, 0.6709, 0.3766, 0.7549,
-                      0.4298, 0.9883, 0.6034, 0.5195, 0.8067, 0.8173, 0.5194,
-                      0.5329, 0.4999, 0.5357, 0.5843, 0.4652, 0.6589, 0.6127,
-                      0.9625, 0.8533, 0.0618, 0.2250, 0.1341, 0.3851, 0.9176,
-                      0.6106, 0.0281, 0.2538, 0.5580, 0.0137, 0.4927, 0.5743,
-                      0.6268, 0.5818, 0.7719, 0.1711, 0.1084, 0.6064, 0.1367,
-                      0.6312, 0.8778, 0.2960, 0.3372, 0.8224, 0.9699, 0.6070,
-                      0.2907, 0.4693, 0.5694, 0.7710, 0.6091, 0.5452, 0.3569,
-                      0.0226, 0.4986, 0.6727, 0.5738, 0.8629, 0.9155, 0.9081,
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-                      0.4797, 0.1466, 0.6411, 0.6861, 0.6601]),
+       col_indices=tensor([3449, 1955, 2868, 1468, 3827, 2362, 3401, 1933, 1944,
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+                            371, 3054,  702, 2583,  922, 1484,  109, 1559, 3828,
+                            314, 4650, 2338, 4026, 2066,  363, 1244]),
+       values=tensor([0.9425, 0.9521, 0.0556, 0.6017, 0.2927, 0.6616, 0.3585,
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+                      0.6201, 0.5008, 0.5434, 0.9333, 0.5556, 0.6923, 0.7482,
+                      0.0125, 0.7765, 0.5129, 0.1430, 0.6425, 0.8783, 0.0143,
+                      0.1840, 0.4954, 0.0539, 0.9183, 0.6777, 0.0801, 0.8137,
+                      0.5435, 0.3644, 0.7674, 0.7580, 0.1924, 0.1251, 0.5532,
+                      0.1834, 0.9137, 0.8028, 0.3376, 0.6738, 0.5666, 0.0953,
+                      0.8386, 0.3636, 0.0669, 0.7853, 0.9872, 0.9951, 0.6260,
+                      0.7005, 0.5128, 0.8470, 0.9874, 0.9785]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.1758, 0.5990, 0.4260,  ..., 0.6457, 0.1523, 0.4408])
+tensor([0.6607, 0.9449, 0.8972,  ..., 0.8025, 0.1142, 0.5030])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -77,80 +77,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.025912761688232422 seconds
+Time: 0.05718708038330078 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '40520', '-ss', '5000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.7472190856933594}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18360', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.34607481956481934}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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-                            660, 3425, 4720, 1730, 3977,  302, 2922, 4097, 2384,
-                           2222, 3777, 3527,  253, 2444, 2627,  880]),
-       values=tensor([0.6428, 0.9934, 0.2143, 0.2775, 0.8801, 0.9209, 0.7937,
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-                      0.4605, 0.7585, 0.3262, 0.3531, 0.6379]),
+       col_indices=tensor([ 154, 2999,  825, 1636, 3815, 3438, 2253, 1331, 3004,
+                           2336, 1340, 4444, 3755,   23, 1857, 4181,  841, 2960,
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+                           2442, 4412,  429, 4681, 4197,  549, 2947, 1379, 3800,
+                           3844, 3545,  214, 2263, 3398, 2668,  505, 1351,  813,
+                           4155, 2188, 4068, 1098, 3436,  213, 4345]),
+       values=tensor([0.0441, 0.2021, 0.4197, 0.3787, 0.4994, 0.0669, 0.2390,
+                      0.2704, 0.9990, 0.1473, 0.2147, 0.3802, 0.2154, 0.6864,
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+                      0.6026, 0.7878, 0.7836, 0.9094, 0.0831, 0.5794, 0.8258,
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+                      0.6474, 0.7781, 0.9736, 0.0288, 0.5905, 0.0099, 0.0815,
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+                      0.8768, 0.0033, 0.4004, 0.4728, 0.8507, 0.7044, 0.3384,
+                      0.2356, 0.3620, 0.9012, 0.7812, 0.6493, 0.2764, 0.9638,
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+                      0.3842, 0.7644, 0.3749, 0.3884, 0.4720, 0.1943, 0.6080,
+                      0.9153, 0.2979, 0.6694, 0.3162, 0.5341, 0.5710, 0.6084,
+                      0.2810, 0.5920, 0.9053, 0.6371, 0.5180, 0.5371, 0.5702,
+                      0.1114, 0.7775, 0.2537, 0.6227, 0.7769, 0.9996, 0.7352,
+                      0.7778, 0.2235, 0.2246, 0.4832, 0.1362, 0.1938, 0.0802,
+                      0.7137, 0.9233, 0.8754, 0.8385, 0.5993, 0.0697, 0.3377,
+                      0.8404, 0.6026, 0.1994, 0.8080, 0.6060, 0.6730, 0.9376,
+                      0.2653, 0.4796, 0.9320, 0.1391, 0.3535, 0.4087, 0.4501,
+                      0.2984, 0.9140, 0.2307, 0.8809, 0.1999, 0.7800, 0.6899,
+                      0.2576, 0.6360, 0.9717, 0.9757, 0.5583, 0.8318, 0.2902,
+                      0.9368, 0.9864, 0.9211, 0.2216, 0.9786]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.7653, 0.7067, 0.4116,  ..., 0.4300, 0.1070, 0.3611])
+tensor([0.5639, 0.0624, 0.1703,  ..., 0.2915, 0.6003, 0.2642])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -158,80 +158,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.7472190856933594 seconds
+Time: 0.34607481956481934 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '569391', '-ss', '5000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.146198511123657}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '557047', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.404626369476318}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([  0,   1,   1,  ..., 250, 250, 250]),
-       col_indices=tensor([ 989, 4634,  172,  296,  437,  988, 4701,   40, 3459,
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-                            950, 3157,  372, 4392,  249, 3355, 4991,   61, 3311,
-                            365, 3749, 2426, 4689,  420, 1130, 2303, 3276, 2058,
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-                           1043, 3314,  572, 4511, 1843, 4636, 1964, 2523,  457,
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-                           2982, 4418, 4646, 1091,  541, 4902, 4735, 4604, 3735,
-                           3670,  955,  687, 2373, 4360, 1850, 1893]),
-       values=tensor([0.2345, 0.0289, 0.8583, 0.9123, 0.0874, 0.7501, 0.2033,
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-                      0.6766, 0.7905, 0.8999, 0.3805, 0.8437]),
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([3535,  752, 4871,  285,  193, 3991,  181, 4103, 4994,
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+                           4392,  938, 4212, 3392, 4029, 4006, 3789]),
+       values=tensor([0.2023, 0.8187, 0.5198, 0.7670, 0.2627, 0.6008, 0.5311,
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+                      0.2424, 0.0013, 0.4796, 0.4396, 0.3595, 0.0630, 0.3254,
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+                      0.4346, 0.4795, 0.2241, 0.5218, 0.9445, 0.0909, 0.0892,
+                      0.2052, 0.9134, 0.2381, 0.2170, 0.9650, 0.9711, 0.4432,
+                      0.0629, 0.4315, 0.8955, 0.2044, 0.2668, 0.3191, 0.8287,
+                      0.2249, 0.5210, 0.6771, 0.7730, 0.2266, 0.3403, 0.4586,
+                      0.1469, 0.2324, 0.6734, 0.0843, 0.5033, 0.0074, 0.1433,
+                      0.7814, 0.0379, 0.8469, 0.8898, 0.1937, 0.5462, 0.0482,
+                      0.2123, 0.5237, 0.1975, 0.8515, 0.2026, 0.2206, 0.7011,
+                      0.4381, 0.0204, 0.6609, 0.3691, 0.3074, 0.6258, 0.3408,
+                      0.8391, 0.4305, 0.9402, 0.0381, 0.6451, 0.9800, 0.4677,
+                      0.3257, 0.3529, 0.6511, 0.2780, 0.8455]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.6631, 0.6256, 0.9086,  ..., 0.3830, 0.1647, 0.1472])
+tensor([0.3989, 0.3808, 0.9151,  ..., 0.1528, 0.1783, 0.6798])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -239,77 +239,77 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.146198511123657 seconds
+Time: 10.404626369476318 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([  0,   1,   1,  ..., 250, 250, 250]),
-       col_indices=tensor([ 989, 4634,  172,  296,  437,  988, 4701,   40, 3459,
-                           2902,  284, 2223, 3489,  379, 2336, 3854, 3801, 4913,
-                           1784,  189, 1121, 2886, 4344, 1044, 1507, 1629, 4228,
-                            950, 3157,  372, 4392,  249, 3355, 4991,   61, 3311,
-                            365, 3749, 2426, 4689,  420, 1130, 2303, 3276, 2058,
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-                           2982, 4418, 4646, 1091,  541, 4902, 4735, 4604, 3735,
-                           3670,  955,  687, 2373, 4360, 1850, 1893]),
-       values=tensor([0.2345, 0.0289, 0.8583, 0.9123, 0.0874, 0.7501, 0.2033,
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+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([3535,  752, 4871,  285,  193, 3991,  181, 4103, 4994,
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+                           3836, 3665,  551, 1008, 1253, 3694, 2443, 3292, 3911,
+                           2085, 2516, 1186, 3005, 3743,  302, 4892, 3208, 2118,
+                           3857, 3023,  441, 4190, 2955, 1688, 4134, 4280, 1065,
+                           2373, 2513, 2898, 1630, 4739, 4377,  735, 1709,    8,
+                           4149, 3261, 1198, 4464,  524, 1523, 3524, 4361, 2389,
+                           3440, 2909, 3425, 3957, 3833,  142, 3466, 4004,  363,
+                           2584, 1779, 4216, 3099, 4171, 1410, 1560, 4925,  598,
+                            600, 1688, 2981, 4335, 3575, 3990,   70,  651, 2774,
+                           2274, 2334,  500, 2649, 3505, 1527, 3170, 2669,  939,
+                           4369, 3117, 4409, 3921, 2913, 2672,   90,  189, 2416,
+                           4648, 2158, 4498, 2787, 2777,  427, 1647,  596,   32,
+                           3758, 3798, 1490, 2780,  536, 2521,  511, 3978, 4192,
+                           4339, 3544,  936, 2045, 1778, 4789, 1856,  262, 1089,
+                            642, 2366, 2554, 3581,   71, 2262, 2483, 4988,  827,
+                           4853, 4171, 1818, 4822, 2101, 1941, 2179, 2348, 1002,
+                           1769, 4513, 2538, 2796,  827, 3722, 2583, 3562, 2718,
+                           2215, 2863, 4777,  291, 3837, 1110, 4297, 2738,  986,
+                           4392,  938, 4212, 3392, 4029, 4006, 3789]),
+       values=tensor([0.2023, 0.8187, 0.5198, 0.7670, 0.2627, 0.6008, 0.5311,
+                      0.8539, 0.3635, 0.1994, 0.9519, 0.6280, 0.7439, 0.4916,
+                      0.4110, 0.6007, 0.0466, 0.2207, 0.4184, 0.4846, 0.2712,
+                      0.1303, 0.1180, 0.5130, 0.1621, 0.5385, 0.8464, 0.2856,
+                      0.7650, 0.3522, 0.2748, 0.6971, 0.5615, 0.4695, 0.8702,
+                      0.8957, 0.1425, 0.4572, 0.8699, 0.9647, 0.1193, 0.9814,
+                      0.0413, 0.8860, 0.2349, 0.6689, 0.4064, 0.8116, 0.1071,
+                      0.2195, 0.2890, 0.2579, 0.4438, 0.6245, 0.4398, 0.0869,
+                      0.9152, 0.4439, 0.6949, 0.2975, 0.8353, 0.1252, 0.8116,
+                      0.6012, 0.7192, 0.6124, 0.4150, 0.4552, 0.6692, 0.9519,
+                      0.5595, 0.9511, 0.4391, 0.3332, 0.2767, 0.5331, 0.4974,
+                      0.9181, 0.1964, 0.0743, 0.7761, 0.8892, 0.1605, 0.8005,
+                      0.1158, 0.7113, 0.0699, 0.9667, 0.0886, 0.6206, 0.4614,
+                      0.4981, 0.8561, 0.4178, 0.0614, 0.1711, 0.4511, 0.4612,
+                      0.6347, 0.7605, 0.6549, 0.8747, 0.7453, 0.5427, 0.0521,
+                      0.0769, 0.8320, 0.0535, 0.0986, 0.1164, 0.5041, 0.8307,
+                      0.5913, 0.3421, 0.3672, 0.0839, 0.4439, 0.3274, 0.8462,
+                      0.0753, 0.5743, 0.4336, 0.2315, 0.7502, 0.6125, 0.2294,
+                      0.7800, 0.5684, 0.9272, 0.6866, 0.0274, 0.6553, 0.6854,
+                      0.3119, 0.1113, 0.3305, 0.4940, 0.8530, 0.1185, 0.3421,
+                      0.1736, 0.3136, 0.5608, 0.3923, 0.7039, 0.8823, 0.9229,
+                      0.8681, 0.9040, 0.9731, 0.4768, 0.5091, 0.9307, 0.5945,
+                      0.1103, 0.4819, 0.8047, 0.4694, 0.2887, 0.2838, 0.3189,
+                      0.2238, 0.9533, 0.9758, 0.4073, 0.2681, 0.6063, 0.4492,
+                      0.2424, 0.0013, 0.4796, 0.4396, 0.3595, 0.0630, 0.3254,
+                      0.1162, 0.8933, 0.5119, 0.0665, 0.1748, 0.2380, 0.6503,
+                      0.4346, 0.4795, 0.2241, 0.5218, 0.9445, 0.0909, 0.0892,
+                      0.2052, 0.9134, 0.2381, 0.2170, 0.9650, 0.9711, 0.4432,
+                      0.0629, 0.4315, 0.8955, 0.2044, 0.2668, 0.3191, 0.8287,
+                      0.2249, 0.5210, 0.6771, 0.7730, 0.2266, 0.3403, 0.4586,
+                      0.1469, 0.2324, 0.6734, 0.0843, 0.5033, 0.0074, 0.1433,
+                      0.7814, 0.0379, 0.8469, 0.8898, 0.1937, 0.5462, 0.0482,
+                      0.2123, 0.5237, 0.1975, 0.8515, 0.2026, 0.2206, 0.7011,
+                      0.4381, 0.0204, 0.6609, 0.3691, 0.3074, 0.6258, 0.3408,
+                      0.8391, 0.4305, 0.9402, 0.0381, 0.6451, 0.9800, 0.4677,
+                      0.3257, 0.3529, 0.6511, 0.2780, 0.8455]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.6631, 0.6256, 0.9086,  ..., 0.3830, 0.1647, 0.1472])
+tensor([0.3989, 0.3808, 0.9151,  ..., 0.1528, 0.1783, 0.6798])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -317,13 +317,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.146198511123657 seconds
+Time: 10.404626369476318 seconds
 
-[39.27, 38.69, 38.63, 39.39, 38.61, 38.66, 38.59, 38.96, 38.73, 38.93]
-[65.05]
-12.874769687652588
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 569391, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.146198511123657, 'TIME_S_1KI': 0.017819386873209546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 837.5037681818008, 'W': 65.05}
-[39.27, 38.69, 38.63, 39.39, 38.61, 38.66, 38.59, 38.96, 38.73, 38.93, 39.57, 38.76, 39.0, 38.65, 39.12, 39.14, 39.15, 44.31, 38.75, 39.36]
-705.7049999999999
-35.28525
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 569391, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.146198511123657, 'TIME_S_1KI': 0.017819386873209546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 837.5037681818008, 'W': 65.05, 'J_1KI': 1.4708763717406856, 'W_1KI': 0.11424486864035434, 'W_D': 29.76475, 'J_D': 383.2143010605574, 'W_D_1KI': 0.05227471105092985, 'J_D_1KI': 9.180810910416543e-05}
+[39.16, 38.54, 38.85, 38.84, 38.69, 38.52, 39.94, 38.95, 39.04, 38.48]
+[64.83]
+12.675763607025146
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 557047, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.404626369476318, 'TIME_S_1KI': 0.018678184012258063, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 821.7697546434403, 'W': 64.83}
+[39.16, 38.54, 38.85, 38.84, 38.69, 38.52, 39.94, 38.95, 39.04, 38.48, 40.52, 39.13, 38.73, 38.89, 39.81, 38.78, 39.14, 39.03, 39.08, 39.08]
+702.5799999999999
+35.129
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 557047, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.404626369476318, 'TIME_S_1KI': 0.018678184012258063, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 821.7697546434403, 'W': 64.83, 'J_1KI': 1.4752251688698446, 'W_1KI': 0.11638156205849774, 'W_D': 29.701, 'J_D': 376.4828548922539, 'W_D_1KI': 0.0533186607234219, 'J_D_1KI': 9.571662844144552e-05}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.json
index 1f26acc..a830ef2 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.json
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 520646, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.21049165725708, "TIME_S_1KI": 0.01961119773753583, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 853.8924551653862, "W": 65.17, "J_1KI": 1.6400634119255428, "W_1KI": 0.1251714216569416, "W_D": 29.995000000000005, "J_D": 393.0106520283223, "W_D_1KI": 0.05761112156820566, "J_D_1KI": 0.0001106531531370752}
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 519426, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.050918102264404, "TIME_S_1KI": 0.019350048134410686, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 851.1004844999312, "W": 65.07, "J_1KI": 1.638540397477083, "W_1KI": 0.12527289739058112, "W_D": 29.469499999999996, "J_D": 385.4542143533229, "W_D_1KI": 0.056734741811153074, "J_D_1KI": 0.00010922584123850764}
diff --git a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.output
index 65d6ab4..5930d2c 100644
--- a/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.output
+++ b/pytorch/output_synthetic_1core/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.output
@@ -1,32 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.012744903564453125}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1249, 1250]),
-       col_indices=tensor([1621, 4974, 1997,  ..., 3786, 4849,  461]),
-       values=tensor([0.2109, 0.3256, 0.0266,  ..., 0.3581, 0.6264, 0.0778]),
-       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.8357, 0.0383, 0.1188,  ..., 0.4462, 0.9461, 0.1099])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 1250
-Density: 5e-05
-Time: 0.012744903564453125 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '82385', '-ss', '5000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.6614789962768555}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.013159751892089844}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([1213, 1571, 1960,  ..., 2843, 4867, 4843]),
-       values=tensor([0.3029, 0.3061, 0.3000,  ..., 0.6016, 0.9759, 0.4960]),
+       col_indices=tensor([4644, 3332, 1667,  ..., 4027, 3585, 4089]),
+       values=tensor([0.3040, 0.1880, 0.0650,  ..., 0.6030, 0.8433, 0.6282]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.1072, 0.2899, 0.3055,  ..., 0.7146, 0.5978, 0.9959])
+tensor([0.9378, 0.4555, 0.5641,  ..., 0.1661, 0.3942, 0.0187])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 1.6614789962768555 seconds
+Time: 0.013159751892089844 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '520646', '-ss', '5000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.21049165725708}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '79788', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.612882375717163}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([ 472, 3691, 4268,  ..., 1601, 3041,  533]),
-       values=tensor([0.9317, 0.8516, 0.8376,  ..., 0.6191, 0.8435, 0.3776]),
+tensor(crow_indices=tensor([   0,    2,    2,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([2035, 3979,  625,  ..., 1946, 1967, 3787]),
+       values=tensor([0.7408, 0.7466, 0.0824,  ..., 0.9968, 0.3510, 0.5639]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.6520, 0.6755, 0.7512,  ..., 0.2262, 0.3599, 0.0025])
+tensor([0.7993, 0.7242, 0.8180,  ..., 0.0616, 0.2082, 0.9168])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.21049165725708 seconds
+Time: 1.612882375717163 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '519426', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.050918102264404}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([ 472, 3691, 4268,  ..., 1601, 3041,  533]),
-       values=tensor([0.9317, 0.8516, 0.8376,  ..., 0.6191, 0.8435, 0.3776]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([3746,  319,  215,  ..., 2829, 1777, 1134]),
+       values=tensor([0.2010, 0.1278, 0.2720,  ..., 0.9161, 0.9746, 0.1029]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.6520, 0.6755, 0.7512,  ..., 0.2262, 0.3599, 0.0025])
+tensor([0.7359, 0.6956, 0.4535,  ..., 0.4670, 0.7765, 0.5592])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +53,29 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.21049165725708 seconds
+Time: 10.050918102264404 seconds
 
-[39.48, 39.4, 39.17, 39.09, 40.79, 38.97, 38.82, 38.72, 38.81, 38.71]
-[65.17]
-13.102538824081421
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 520646, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.21049165725708, 'TIME_S_1KI': 0.01961119773753583, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 853.8924551653862, 'W': 65.17}
-[39.48, 39.4, 39.17, 39.09, 40.79, 38.97, 38.82, 38.72, 38.81, 38.71, 39.49, 39.17, 38.98, 38.86, 39.13, 38.88, 38.85, 38.81, 38.77, 38.88]
-703.5
-35.175
-{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 520646, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.21049165725708, 'TIME_S_1KI': 0.01961119773753583, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 853.8924551653862, 'W': 65.17, 'J_1KI': 1.6400634119255428, 'W_1KI': 0.1251714216569416, 'W_D': 29.995000000000005, 'J_D': 393.0106520283223, 'W_D_1KI': 0.05761112156820566, 'J_D_1KI': 0.0001106531531370752}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([3746,  319,  215,  ..., 2829, 1777, 1134]),
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..1415bfa
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 286, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.389951467514038, "TIME_S_1KI": 36.32850163466446, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 766.2996877527237, "W": 52.98, "J_1KI": 2679.369537596936, "W_1KI": 185.24475524475523, "W_D": 35.61999999999999, "J_D": 515.2056413316725, "W_D_1KI": 124.54545454545452, "J_D_1KI": 435.47361729179903}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..261069e
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 3.6701581478118896}
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+                       [43072, 23887, 57626,  ...,  2599, 92945, 52721]]),
+       values=tensor([0.5284, 0.2277, 0.6442,  ..., 0.1523, 0.8543, 0.1278]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.2916, 0.8737, 0.3685,  ..., 0.6723, 0.4085, 0.8827])
+Matrix Type: synthetic
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+Shape: torch.Size([100000, 100000])
+Rows: 100000
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '286', '-ss', '100000', '-sd', '0.0001', '-c', '1']
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..3012046
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 41.292503356933594, "TIME_S_1KI": 412.92503356933594, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2580.4754705643654, "W": 53.19, "J_1KI": 25804.754705643652, "W_1KI": 531.9, "W_D": 35.57125, "J_D": 1725.7141959449648, "W_D_1KI": 355.7125, "J_D_1KI": 3557.1249999999995}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..bac9654
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '0.001', '-c', '1']
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+                       [ 8217, 46972, 15744,  ..., 94107, 44064, 30572]]),
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..71646db
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 429.7097659111023, "TIME_S_1KI": 4297.097659111023, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 24245.604853191377, "W": 53.22, "J_1KI": 242456.04853191378, "W_1KI": 532.2, "W_D": 35.86475, "J_D": 16339.018351343393, "W_D_1KI": 358.64750000000004, "J_D_1KI": 3586.4750000000004}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..3b8854f
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_0.01.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '0.01', '-c', '1']
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..58b91a0
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 2724, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.093451261520386, "TIME_S_1KI": 3.7053785835243707, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 734.1387234449387, "W": 52.86000000000001, "J_1KI": 269.50760772574847, "W_1KI": 19.40528634361234, "W_D": 35.55900000000001, "J_D": 493.85620255351085, "W_D_1KI": 13.053964757709256, "J_D_1KI": 4.792204389761108}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..10f259d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.3854491710662842}
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+tensor(indices=tensor([[55102, 24162, 47477,  ...,  1641, 27945, 96455],
+                       [48361,  3693, 59360,  ..., 86601, 84390, 91492]]),
+       values=tensor([0.4770, 0.5969, 0.7353,  ..., 0.7880, 0.3261, 0.0262]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_coo)
+tensor([0.5704, 0.6401, 0.7937,  ..., 0.6238, 0.9560, 0.6940])
+Matrix Type: synthetic
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+Shape: torch.Size([100000, 100000])
+Rows: 100000
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+NNZ: 100000
+Density: 1e-05
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..c15d0a1
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..a4ac413
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_100000_5e-05.output
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..c6530b5
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.0001.json
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..e3e1a69
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.0001.output
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..96c3f6b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.001.json
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..a6776c2
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.001.output
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..8c291a4
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 298, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.47829008102417, "TIME_S_1KI": 35.16204725175896, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 767.5406039357185, "W": 53.01, "J_1KI": 2575.639610522546, "W_1KI": 177.88590604026842, "W_D": 36.254999999999995, "J_D": 524.9421730935572, "W_D_1KI": 121.66107382550334, "J_D_1KI": 408.2586369983334}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..e784027
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.5194344520568848}
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+tensor(indices=tensor([[3654, 6452,  587,  ...,  850, 5136,  577],
+                       [6053, 7007, 5699,  ..., 4834, 9822, 1975]]),
+       values=tensor([0.0110, 0.0233, 0.3725,  ..., 0.9397, 0.6782, 0.1946]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.4410, 0.5527, 0.9521,  ..., 0.4811, 0.5857, 0.0363])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
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+tensor(indices=tensor([[5487, 6843, 6460,  ..., 6149, 3051, 4869],
+                       [4765, 2140, 6995,  ..., 6813, 1753, 4926]]),
+       values=tensor([0.3736, 0.6438, 0.8428,  ..., 0.4015, 0.6317, 0.4357]),
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+tensor([0.8014, 0.7170, 0.4660,  ..., 0.2951, 0.6544, 0.0082])
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..dcff7e0
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 17.49826955795288, "TIME_S_1KI": 174.9826955795288, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1223.0167962312698, "W": 53.25, "J_1KI": 12230.167962312698, "W_1KI": 532.5, "W_D": 36.153, "J_D": 830.3422766976356, "W_D_1KI": 361.53, "J_D_1KI": 3615.2999999999997}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..fbdfcf6
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.05', '-c', '1']
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+tensor([0.9120, 0.7513, 0.5896,  ..., 0.2518, 0.7399, 0.6609])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
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+Time: 17.49826955795288 seconds
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+tensor(indices=tensor([[7796, 2939, 8332,  ..., 3525, 3120, 8313],
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+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_coo)
+tensor([0.9120, 0.7513, 0.5896,  ..., 0.2518, 0.7399, 0.6609])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
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+Time: 17.49826955795288 seconds
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..e5cb697
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 35.12812423706055, "TIME_S_1KI": 351.28124237060547, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2250.224948012829, "W": 53.39, "J_1KI": 22502.24948012829, "W_1KI": 533.9000000000001, "W_D": 35.64075, "J_D": 1502.148432588279, "W_D_1KI": 356.40749999999997, "J_D_1KI": 3564.075}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..7a72e0d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 35.12812423706055}
+
+tensor(indices=tensor([[6698, 9307, 6437,  ..., 2289, 5848, 3791],
+                       [7340, 1765, 7872,  ..., 2927,  307, 9302]]),
+       values=tensor([0.0196, 0.8715, 0.3597,  ..., 0.8985, 0.2718, 0.2751]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.8883, 0.3132, 0.4078,  ..., 0.3205, 0.4829, 0.4579])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 35.12812423706055 seconds
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+tensor(indices=tensor([[6698, 9307, 6437,  ..., 2289, 5848, 3791],
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+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.8883, 0.3132, 0.4078,  ..., 0.3205, 0.4829, 0.4579])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
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+Size: 100000000
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+Time: 35.12812423706055 seconds
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..2762ec0
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 70.18838477134705, "TIME_S_1KI": 701.8838477134705, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4299.211010894775, "W": 53.36, "J_1KI": 42992.11010894775, "W_1KI": 533.5999999999999, "W_D": 36.20225, "J_D": 2916.812440389156, "W_D_1KI": 362.0225, "J_D_1KI": 3620.2249999999995}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..8885133
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 70.18838477134705}
+
+tensor(indices=tensor([[5797, 5482,  210,  ..., 7841, 5780, 4358],
+                       [4414, 5324, 9857,  ..., 6704, 5929, 3437]]),
+       values=tensor([0.9887, 0.5554, 0.6963,  ..., 0.8302, 0.2539, 0.4394]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_coo)
+tensor([0.2946, 0.8628, 0.5214,  ..., 0.5536, 0.8504, 0.0648])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
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+NNZ: 20000000
+Density: 0.2
+Time: 70.18838477134705 seconds
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+tensor(indices=tensor([[5797, 5482,  210,  ..., 7841, 5780, 4358],
+                       [4414, 5324, 9857,  ..., 6704, 5929, 3437]]),
+       values=tensor([0.9887, 0.5554, 0.6963,  ..., 0.8302, 0.2539, 0.4394]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_coo)
+tensor([0.2946, 0.8628, 0.5214,  ..., 0.5536, 0.8504, 0.0648])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
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+Size: 100000000
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+Time: 70.18838477134705 seconds
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..1a46f1f
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 105.28500413894653, "TIME_S_1KI": 1052.8500413894653, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 6421.246587808132, "W": 54.01, "J_1KI": 64212.46587808132, "W_1KI": 540.1, "W_D": 36.906499999999994, "J_D": 4387.812205016493, "W_D_1KI": 369.06499999999994, "J_D_1KI": 3690.649999999999}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..79b7be4
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.3', '-c', '1']
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+                       [9140, 8066, 1918,  ..., 8537, 9814, 4363]]),
+       values=tensor([0.8932, 0.2978, 0.2898,  ..., 0.4756, 0.9985, 0.1813]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_coo)
+tensor([0.3467, 0.3617, 0.9528,  ..., 0.4984, 0.6391, 0.6014])
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
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+Time: 105.28500413894653 seconds
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+tensor(indices=tensor([[1309, 2263, 4256,  ..., 6284, 7791, 3791],
+                       [9140, 8066, 1918,  ..., 8537, 9814, 4363]]),
+       values=tensor([0.8932, 0.2978, 0.2898,  ..., 0.4756, 0.9985, 0.1813]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_coo)
+tensor([0.3467, 0.3617, 0.9528,  ..., 0.4984, 0.6391, 0.6014])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
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+Time: 105.28500413894653 seconds
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..baaf72b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 140.32227563858032, "TIME_S_1KI": 1403.2227563858032, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 8384.47321164131, "W": 53.53999999999999, "J_1KI": 83844.73211641311, "W_1KI": 535.3999999999999, "W_D": 36.693749999999994, "J_D": 5746.316098424792, "W_D_1KI": 366.93749999999994, "J_D_1KI": 3669.3749999999995}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..6cec52c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.4.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.4', '-c', '1']
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+                       [8014, 1927, 8978,  ..., 1113, 9015,  414]]),
+       values=tensor([0.0613, 0.8201, 0.3391,  ..., 0.1226, 0.6645, 0.0568]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_coo)
+tensor([0.7600, 0.1703, 0.4154,  ..., 0.4075, 0.3991, 0.5788])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 140.32227563858032 seconds
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+tensor(indices=tensor([[ 595, 7978, 3279,  ..., 8901, 5345, 7774],
+                       [8014, 1927, 8978,  ..., 1113, 9015,  414]]),
+       values=tensor([0.0613, 0.8201, 0.3391,  ..., 0.1226, 0.6645, 0.0568]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_coo)
+tensor([0.7600, 0.1703, 0.4154,  ..., 0.4075, 0.3991, 0.5788])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
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+Density: 0.4
+Time: 140.32227563858032 seconds
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..01a8a26
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 175.3649799823761, "TIME_S_1KI": 1753.649799823761, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 10459.217702465057, "W": 53.56, "J_1KI": 104592.17702465056, "W_1KI": 535.6, "W_D": 36.71175, "J_D": 7169.084867223144, "W_D_1KI": 367.11750000000006, "J_D_1KI": 3671.1750000000006}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..78e8d09
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_0.5.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.5', '-c', '1']
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+                       [7159, 9986, 7146,  ..., 7832, 6698, 5324]]),
+       values=tensor([0.6988, 0.5829, 0.8912,  ..., 0.6255, 0.2496, 0.7717]),
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 175.3649799823761 seconds
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+tensor(indices=tensor([[3328, 5614, 1786,  ..., 2116, 9994, 4657],
+                       [7159, 9986, 7146,  ..., 7832, 6698, 5324]]),
+       values=tensor([0.6988, 0.5829, 0.8912,  ..., 0.6255, 0.2496, 0.7717]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_coo)
+tensor([0.8806, 0.8140, 0.3380,  ..., 0.3737, 0.7757, 0.9081])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
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+Time: 175.3649799823761 seconds
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..9d3f07b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 243279, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.448915243148804, "TIME_S_1KI": 0.04295033785550255, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 749.9146813058853, "W": 52.97, "J_1KI": 3.082529446873283, "W_1KI": 0.21773354872389314, "W_D": 35.7745, "J_D": 506.47201748871805, "W_D_1KI": 0.14705132789924327, "J_D_1KI": 0.0006044554930727407}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..25fe7f1
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1069 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.016295909881591797}
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+                       [1973, 1738, 2637,  ...,  444, 6562, 2862]]),
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '64433', '-ss', '10000', '-sd', '1e-05', '-c', '1']
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..2dd7277
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 57254, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.50862431526184, "TIME_S_1KI": 0.183543932568237, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 754.4204360270501, "W": 52.99, "J_1KI": 13.176728892776925, "W_1KI": 0.9255248541586614, "W_D": 35.93475, "J_D": 511.6042604929805, "W_D_1KI": 0.6276373703147379, "J_D_1KI": 0.010962332244292763}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..cb3cde8
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_10000_5e-05.output
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..bdade1d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 121.61708164215088, "TIME_S_1KI": 1216.1708164215088, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 7122.070309627056, "W": 53.15, "J_1KI": 71220.70309627058, "W_1KI": 531.5, "W_D": 36.320499999999996, "J_D": 4866.926710833668, "W_D_1KI": 363.2049999999999, "J_D_1KI": 3632.0499999999993}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..cb8582c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_0.0001.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '500000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 121.61708164215088}
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+tensor([0.1295, 0.8590, 0.1296,  ..., 0.2625, 0.0779, 0.1541])
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..4d75520
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_1e-05.json
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..4be700b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_1e-05.output
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..b7140cf
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_5e-05.json
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..036a203
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_500000_5e-05.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '500000', '-sd', '5e-05', '-c', '1']
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..3c6c56c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.0001.json
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..313a610
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.0001.output
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..a420966
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 117, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.380040407180786, "TIME_S_1KI": 88.71829407846826, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 793.54994743824, "W": 52.98, "J_1KI": 6782.47818323282, "W_1KI": 452.82051282051276, "W_D": 35.899, "J_D": 537.705729767561, "W_D_1KI": 306.8290598290598, "J_D_1KI": 2622.470596829571}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..652c07a
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.001.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '50000', '-sd', '0.001', '-c', '1']
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+                       [10881, 42612, 48486,  ..., 31508, 28624, 40369]]),
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..7c5e4ff
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.01.json
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..d24d27f
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.05.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.05.json
new file mode 100644
index 0000000..16e5953
--- /dev/null
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.05.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_0.05.output
new file mode 100644
index 0000000..626eb3c
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_1e-05.json
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index 0000000..2a471a6
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_1e-05.output
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index 0000000..b197b5c
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..cecd5e3
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_5e-05.json
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..9e2cae8
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_50000_5e-05.output
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..498b7bf
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.0001.json
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..cc96991
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.0001.output
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new file mode 100644
index 0000000..4e3bd6f
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..65bf887
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.001.output
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..eba8d65
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1175, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.336398839950562, "TIME_S_1KI": 8.796935182936647, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 746.7368591165542, "W": 52.89, "J_1KI": 635.5207311630248, "W_1KI": 45.0127659574468, "W_D": 35.812250000000006, "J_D": 505.6216124578715, "W_D_1KI": 30.478510638297877, "J_D_1KI": 25.939157990040744}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..01b79f5
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.01.output
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..8b173ff
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.05.json
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+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 239, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.492762088775635, "TIME_S_1KI": 43.90277024592316, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 778.1596210622787, "W": 53.23, "J_1KI": 3255.89799607648, "W_1KI": 222.7196652719665, "W_D": 36.39574999999999, "J_D": 532.0628034619092, "W_D_1KI": 152.28347280334725, "J_D_1KI": 637.1693422734195}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..4fef78d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.05.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '0.05', '-c', '1']
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+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_coo)
+tensor([0.0804, 0.3830, 0.8558,  ..., 0.8435, 0.2496, 0.0277])
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..0ee178d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 119, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.445932626724243, "TIME_S_1KI": 87.78094644306087, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 794.5498427605629, "W": 53.09, "J_1KI": 6676.8894349627135, "W_1KI": 446.1344537815126, "W_D": 36.087500000000006, "J_D": 540.088857612014, "W_D_1KI": 303.2563025210084, "J_D_1KI": 2548.3722900925077}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..ab7fe98
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.1.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '0.1', '-c', '1']
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+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_coo)
+tensor([0.4996, 0.7088, 0.6416,  ..., 0.4672, 0.1887, 0.4295])
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..a86bb5b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 17.579725742340088, "TIME_S_1KI": 175.79725742340088, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1215.9591540527344, "W": 53.330000000000005, "J_1KI": 12159.591540527344, "W_1KI": 533.3000000000001, "W_D": 36.331500000000005, "J_D": 828.3821489868166, "W_D_1KI": 363.31500000000005, "J_D_1KI": 3633.1500000000005}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..cb11879
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '0.2', '-c', '1']
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+                       [2124,  387, 2742,  ..., 2850, 4147, 1644]]),
+       values=tensor([0.1642, 0.9459, 0.9100,  ..., 0.2504, 0.1529, 0.8530]),
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+tensor([0.1158, 0.0778, 0.1338,  ..., 0.2986, 0.0086, 0.8295])
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..9b914b0
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 26.379098653793335, "TIME_S_1KI": 263.79098653793335, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1731.2462457871436, "W": 53.41, "J_1KI": 17312.462457871436, "W_1KI": 534.1, "W_D": 36.3955, "J_D": 1179.7336217664479, "W_D_1KI": 363.955, "J_D_1KI": 3639.5499999999997}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..f902365
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '0.3', '-c', '1']
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+
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+                       [  29, 3220, 1335,  ..., 3608, 2941, 1585]]),
+       values=tensor([0.6096, 0.3396, 0.2859,  ..., 0.4952, 0.0380, 0.1024]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_coo)
+tensor([0.1918, 0.0876, 0.5397,  ..., 0.7140, 0.9679, 0.7231])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 26.379098653793335 seconds
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+tensor(indices=tensor([[ 386, 1957, 1559,  ..., 2478, 1085, 4412],
+                       [  29, 3220, 1335,  ..., 3608, 2941, 1585]]),
+       values=tensor([0.6096, 0.3396, 0.2859,  ..., 0.4952, 0.0380, 0.1024]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_coo)
+tensor([0.1918, 0.0876, 0.5397,  ..., 0.7140, 0.9679, 0.7231])
+Matrix Type: synthetic
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+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 26.379098653793335 seconds
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..6432ccd
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 34.94771075248718, "TIME_S_1KI": 349.4771075248718, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2247.251918399334, "W": 53.550000000000004, "J_1KI": 22472.519183993343, "W_1KI": 535.5000000000001, "W_D": 36.6275, "J_D": 1537.0909363430737, "W_D_1KI": 366.275, "J_D_1KI": 3662.75}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..15fbf2c
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.4.output
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '0.4', '-c', '1']
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+tensor(indices=tensor([[ 341, 2125, 2941,  ..., 2481, 3481,  943],
+                       [2128,  980, 2280,  ..., 2746, 1908, 4985]]),
+       values=tensor([0.2713, 0.6415, 0.0835,  ..., 0.2173, 0.4665, 0.7810]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.8918, 0.0864, 0.8458,  ..., 0.9888, 0.3713, 0.4277])
+Matrix Type: synthetic
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+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 34.94771075248718 seconds
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+tensor(indices=tensor([[ 341, 2125, 2941,  ..., 2481, 3481,  943],
+                       [2128,  980, 2280,  ..., 2746, 1908, 4985]]),
+       values=tensor([0.2713, 0.6415, 0.0835,  ..., 0.2173, 0.4665, 0.7810]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.8918, 0.0864, 0.8458,  ..., 0.9888, 0.3713, 0.4277])
+Matrix Type: synthetic
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+Shape: torch.Size([5000, 5000])
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+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 34.94771075248718 seconds
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..18c5dd3
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.5.json
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+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 43.97061109542847, "TIME_S_1KI": 439.70611095428467, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2751.18099268198, "W": 53.43, "J_1KI": 27511.809926819802, "W_1KI": 534.3, "W_D": 36.369249999999994, "J_D": 1872.7005300037263, "W_D_1KI": 363.69249999999994, "J_D_1KI": 3636.9249999999993}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..c312c82
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '0.5', '-c', '1']
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+                       [4962, 4520, 3297,  ...,  791, 3059, 3340]]),
+       values=tensor([0.7023, 0.5600, 0.8014,  ..., 0.2262, 0.5513, 0.0605]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_coo)
+tensor([0.3141, 0.0727, 0.1677,  ..., 0.2736, 0.9208, 0.9057])
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+Shape: torch.Size([5000, 5000])
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+NNZ: 12500000
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+tensor(indices=tensor([[3145, 1307, 4347,  ..., 4045, 3429, 3199],
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+tensor([0.3141, 0.0727, 0.1677,  ..., 0.2736, 0.9208, 0.9057])
+Matrix Type: synthetic
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+Time: 43.97061109542847 seconds
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..52214c0
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 657339, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.507478475570679, "TIME_S_1KI": 0.015984870022272644, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 738.8582760810853, "W": 52.6, "J_1KI": 1.1240140567973074, "W_1KI": 0.08001959415157171, "W_D": 35.739000000000004, "J_D": 502.0162724118233, "W_D_1KI": 0.054369206756331216, "J_D_1KI": 8.27110619578805e-05}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..1addc0a
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,429 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.014267444610595703}
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diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..ab5f5e4
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 205269, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.446171522140503, "TIME_S_1KI": 0.05089015643930892, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 746.4549215507507, "W": 52.72, "J_1KI": 3.63647175925615, "W_1KI": 0.25683371575834635, "W_D": 35.52775, "J_D": 503.03231864804025, "W_D_1KI": 0.17307898416224562, "J_D_1KI": 0.0008431813092198316}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..cb7eecb
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_coo_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,73 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.01698613166809082}
+
+tensor(indices=tensor([[2254,  318, 1383,  ..., 1891, 4522, 2908],
+                       [2593, 4670, 4638,  ..., 2668, 2035,  426]]),
+       values=tensor([0.0456, 0.8923, 0.6404,  ..., 0.6341, 0.6032, 0.2804]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.8987, 0.0761, 0.5515,  ..., 0.7891, 0.3447, 0.7080])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.01698613166809082 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '61815', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.1619837284088135}
+
+tensor(indices=tensor([[ 457, 1002, 2225,  ...,  796, 1709, 4999],
+                       [ 974, 1684, 2121,  ..., 1078,   81, 2950]]),
+       values=tensor([0.9488, 0.6975, 0.2006,  ..., 0.9426, 0.0186, 0.4857]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.9601, 0.3703, 0.7281,  ..., 0.7581, 0.0514, 0.7748])
+Matrix Type: synthetic
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+Shape: torch.Size([5000, 5000])
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+Time: 3.1619837284088135 seconds
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+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '205269', '-ss', '5000', '-sd', '5e-05', '-c', '1']
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+Time: 10.446171522140503 seconds
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+tensor(indices=tensor([[4898, 4877, 1527,  ..., 3334, 4519, 4465],
+                       [1066, 3154, 3911,  ..., 4483, 4042, 3763]]),
+       values=tensor([0.8722, 0.9425, 0.0691,  ..., 0.8751, 0.4805, 0.0876]),
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+343.845
+17.19225
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 205269, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.446171522140503, 'TIME_S_1KI': 0.05089015643930892, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 746.4549215507507, 'W': 52.72, 'J_1KI': 3.63647175925615, 'W_1KI': 0.25683371575834635, 'W_D': 35.52775, 'J_D': 503.03231864804025, 'W_D_1KI': 0.17307898416224562, 'J_D_1KI': 0.0008431813092198316}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json
index 3a94ff3..1ca2fad 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3659, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.513276815414429, "TIME_S_1KI": 2.8732650493070317, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 699.5738222312928, "W": 48.46, "J_1KI": 191.19262701046534, "W_1KI": 13.244055752937962, "W_D": 31.944000000000003, "J_D": 461.147052772522, "W_D_1KI": 8.73025416780541, "J_D_1KI": 2.3859672500151436}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3625, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.394656419754028, "TIME_S_1KI": 2.867491426139042, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 779.2213072299957, "W": 54.15, "J_1KI": 214.95760199448156, "W_1KI": 14.937931034482759, "W_D": 37.07275, "J_D": 533.4787944157124, "W_D_1KI": 10.226965517241378, "J_D_1KI": 2.821231866825208}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output
index 3030a8d..83ec13e 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,14 +1,34 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3051443099975586}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3142099380493164}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       9,      20,  ...,  999978,
+tensor(crow_indices=tensor([      0,      10,      21,  ...,  999982,
+                             999993, 1000000]),
+       col_indices=tensor([ 9595, 12371, 15341,  ..., 68774, 88209, 89359]),
+       values=tensor([0.5689, 0.1836, 0.5609,  ..., 0.8694, 0.5209, 0.6444]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0233, 0.5023, 0.1367,  ..., 0.1519, 0.1947, 0.3720])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.3142099380493164 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '3341', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.675478458404541}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      19,  ...,  999976,
                              999988, 1000000]),
-       col_indices=tensor([10874, 16180, 25759,  ..., 85120, 90595, 97571]),
-       values=tensor([0.6980, 0.1450, 0.2222,  ..., 0.0442, 0.2876, 0.2305]),
+       col_indices=tensor([ 4263,  6426, 34678,  ..., 65159, 69813, 89844]),
+       values=tensor([0.9067, 0.8913, 0.2582,  ..., 0.8629, 0.5634, 0.7349]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9393, 0.9771, 0.4381,  ..., 0.2097, 0.8256, 0.0395])
+tensor([0.6979, 0.0510, 0.2361,  ..., 0.7900, 0.5351, 0.0148])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 0.3051443099975586 seconds
+Time: 9.675478458404541 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '3440', '-ss', '100000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.870911598205566}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '3625', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.394656419754028}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      16,      29,  ...,  999980,
-                             999991, 1000000]),
-       col_indices=tensor([ 5523, 13716, 16446,  ..., 89337, 96388, 97674]),
-       values=tensor([0.9883, 0.0360, 0.0063,  ..., 0.9506, 0.8956, 0.5971]),
+tensor(crow_indices=tensor([      0,      12,      23,  ...,  999985,
+                             999995, 1000000]),
+       col_indices=tensor([ 2685, 12181, 30698,  ..., 53529, 78569, 87140]),
+       values=tensor([0.4991, 0.6096, 0.8526,  ..., 0.6405, 0.0983, 0.2024]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2735, 0.3671, 0.2374,  ..., 0.9952, 0.6404, 0.3809])
+tensor([0.2731, 0.2403, 0.0212,  ..., 0.3306, 0.6694, 0.0606])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 9.870911598205566 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '3659', '-ss', '100000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.513276815414429}
+Time: 10.394656419754028 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      15,      23,  ...,  999980,
-                             999992, 1000000]),
-       col_indices=tensor([ 3420, 12508, 17596,  ..., 74140, 75972, 84324]),
-       values=tensor([0.2457, 0.5369, 0.4041,  ..., 0.2835, 0.7746, 0.0854]),
+tensor(crow_indices=tensor([      0,      12,      23,  ...,  999985,
+                             999995, 1000000]),
+       col_indices=tensor([ 2685, 12181, 30698,  ..., 53529, 78569, 87140]),
+       values=tensor([0.4991, 0.6096, 0.8526,  ..., 0.6405, 0.0983, 0.2024]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1485, 0.5045, 0.7097,  ..., 0.5250, 0.4505, 0.4467])
+tensor([0.2731, 0.2403, 0.0212,  ..., 0.3306, 0.6694, 0.0606])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,30 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.513276815414429 seconds
+Time: 10.394656419754028 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      15,      23,  ...,  999980,
-                             999992, 1000000]),
-       col_indices=tensor([ 3420, 12508, 17596,  ..., 74140, 75972, 84324]),
-       values=tensor([0.2457, 0.5369, 0.4041,  ..., 0.2835, 0.7746, 0.0854]),
-       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.1485, 0.5045, 0.7097,  ..., 0.5250, 0.4505, 0.4467])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 1000000
-Density: 0.0001
-Time: 10.513276815414429 seconds
-
-[19.03, 17.89, 18.11, 18.19, 17.92, 19.96, 20.76, 18.13, 18.16, 18.25]
-[48.46]
-14.436108589172363
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3659, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.513276815414429, 'TIME_S_1KI': 2.8732650493070317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 699.5738222312928, 'W': 48.46}
-[19.03, 17.89, 18.11, 18.19, 17.92, 19.96, 20.76, 18.13, 18.16, 18.25, 18.14, 18.17, 18.05, 17.87, 18.21, 18.32, 17.88, 17.89, 18.02, 18.16]
-330.31999999999994
-16.516
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3659, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.513276815414429, 'TIME_S_1KI': 2.8732650493070317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 699.5738222312928, 'W': 48.46, 'J_1KI': 191.19262701046534, 'W_1KI': 13.244055752937962, 'W_D': 31.944000000000003, 'J_D': 461.147052772522, 'W_D_1KI': 8.73025416780541, 'J_D_1KI': 2.3859672500151436}
+[19.72, 18.58, 18.34, 22.32, 19.76, 18.72, 19.22, 18.5, 18.89, 18.49]
+[54.15]
+14.39005184173584
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3625, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.394656419754028, 'TIME_S_1KI': 2.867491426139042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 779.2213072299957, 'W': 54.15}
+[19.72, 18.58, 18.34, 22.32, 19.76, 18.72, 19.22, 18.5, 18.89, 18.49, 18.86, 18.52, 19.54, 18.62, 18.52, 18.39, 18.6, 18.45, 18.66, 18.76]
+341.545
+17.07725
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3625, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.394656419754028, 'TIME_S_1KI': 2.867491426139042, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 779.2213072299957, 'W': 54.15, 'J_1KI': 214.95760199448156, 'W_1KI': 14.937931034482759, 'W_D': 37.07275, 'J_D': 533.4787944157124, 'W_D_1KI': 10.226965517241378, 'J_D_1KI': 2.821231866825208}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.json
index 253d722..933d87d 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 376, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.408750772476196, "TIME_S_1KI": 27.68284779913882, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 864.391910111904, "W": 48.41, "J_1KI": 2298.914654552936, "W_1KI": 128.75, "W_D": 32.1215, "J_D": 573.5501908832788, "W_D_1KI": 85.42952127659574, "J_D_1KI": 227.20617360796737}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 372, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.448328256607056, "TIME_S_1KI": 28.086903915610364, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 951.2793003344535, "W": 54.37, "J_1KI": 2557.2024202539073, "W_1KI": 146.15591397849462, "W_D": 37.12375, "J_D": 649.5320015779138, "W_D_1KI": 99.79502688172043, "J_D_1KI": 268.2662012949474}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.output
index 8c93db0..0739078 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,34 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.7904272079467773}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.818298578262329}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       83,      173,  ...,  9999788,
+tensor(crow_indices=tensor([       0,       94,      190,  ...,  9999809,
+                             9999901, 10000000]),
+       col_indices=tensor([  679,   794,  1284,  ..., 96944, 98164, 99000]),
+       values=tensor([0.4981, 0.4934, 0.8562,  ..., 0.8870, 0.8354, 0.5851]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2786, 0.4976, 0.3615,  ..., 0.4502, 0.8268, 0.0967])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 2.818298578262329 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '372', '-ss', '100000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.448328256607056}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      105,      209,  ...,  9999801,
                              9999895, 10000000]),
-       col_indices=tensor([  687,  1990,  2832,  ..., 93491, 98909, 99713]),
-       values=tensor([0.2182, 0.4312, 0.1873,  ..., 0.5994, 0.5663, 0.3895]),
+       col_indices=tensor([  873,  1265,  1381,  ..., 96409, 96627, 98514]),
+       values=tensor([0.9757, 0.4166, 0.2628,  ..., 0.5352, 0.4288, 0.9605]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.6218, 0.2935, 0.0099,  ..., 0.4944, 0.2399, 0.4191])
+tensor([0.3038, 0.5505, 0.5965,  ..., 0.9015, 0.0486, 0.8079])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 2.7904272079467773 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '376', '-ss', '100000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.408750772476196}
+Time: 10.448328256607056 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      101,      179,  ...,  9999785,
-                             9999885, 10000000]),
-       col_indices=tensor([  974,  1017,  1175,  ..., 97865, 98322, 99037]),
-       values=tensor([0.8598, 0.2680, 0.8943,  ..., 0.1763, 0.5676, 0.4916]),
+tensor(crow_indices=tensor([       0,      105,      209,  ...,  9999801,
+                             9999895, 10000000]),
+       col_indices=tensor([  873,  1265,  1381,  ..., 96409, 96627, 98514]),
+       values=tensor([0.9757, 0.4166, 0.2628,  ..., 0.5352, 0.4288, 0.9605]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4049, 0.5573, 0.2557,  ..., 0.0615, 0.7671, 0.4849])
+tensor([0.3038, 0.5505, 0.5965,  ..., 0.9015, 0.0486, 0.8079])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,30 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 10.408750772476196 seconds
+Time: 10.448328256607056 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      101,      179,  ...,  9999785,
-                             9999885, 10000000]),
-       col_indices=tensor([  974,  1017,  1175,  ..., 97865, 98322, 99037]),
-       values=tensor([0.8598, 0.2680, 0.8943,  ..., 0.1763, 0.5676, 0.4916]),
-       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4049, 0.5573, 0.2557,  ..., 0.0615, 0.7671, 0.4849])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 10000000
-Density: 0.001
-Time: 10.408750772476196 seconds
-
-[18.19, 17.86, 18.15, 17.76, 18.57, 17.97, 17.88, 17.91, 18.5, 18.33]
-[48.41]
-17.855647802352905
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 376, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.408750772476196, 'TIME_S_1KI': 27.68284779913882, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 864.391910111904, 'W': 48.41}
-[18.19, 17.86, 18.15, 17.76, 18.57, 17.97, 17.88, 17.91, 18.5, 18.33, 18.68, 17.76, 17.89, 18.01, 18.9, 17.73, 18.37, 17.97, 18.0, 17.88]
-325.77
-16.2885
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 376, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.408750772476196, 'TIME_S_1KI': 27.68284779913882, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 864.391910111904, 'W': 48.41, 'J_1KI': 2298.914654552936, 'W_1KI': 128.75, 'W_D': 32.1215, 'J_D': 573.5501908832788, 'W_D_1KI': 85.42952127659574, 'J_D_1KI': 227.20617360796737}
+[18.85, 18.56, 19.07, 19.17, 20.19, 18.59, 18.9, 18.53, 18.6, 18.44]
+[54.37]
+17.496400594711304
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 372, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.448328256607056, 'TIME_S_1KI': 28.086903915610364, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 951.2793003344535, 'W': 54.37}
+[18.85, 18.56, 19.07, 19.17, 20.19, 18.59, 18.9, 18.53, 18.6, 18.44, 19.0, 18.67, 18.6, 23.11, 19.22, 19.08, 19.57, 18.58, 18.99, 18.7]
+344.925
+17.24625
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 372, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.448328256607056, 'TIME_S_1KI': 28.086903915610364, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 951.2793003344535, 'W': 54.37, 'J_1KI': 2557.2024202539073, 'W_1KI': 146.15591397849462, 'W_D': 37.12375, 'J_D': 649.5320015779138, 'W_D_1KI': 99.79502688172043, 'J_D_1KI': 268.2662012949474}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..267ac01
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 28.13305163383484, "TIME_S_1KI": 281.3305163383484, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3732.906339125633, "W": 53.41, "J_1KI": 37329.063391256335, "W_1KI": 534.1, "W_D": 36.108999999999995, "J_D": 2523.713068704128, "W_D_1KI": 361.09, "J_D_1KI": 3610.8999999999996}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..900f98d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 28.13305163383484}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       978,      1920,  ...,
+                             99998042,  99999027, 100000000]),
+       col_indices=tensor([   99,   121,   146,  ..., 99677, 99702, 99983]),
+       values=tensor([0.9922, 0.1937, 0.7553,  ..., 0.0650, 0.9551, 0.6219]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.3364, 0.0616, 0.7992,  ..., 0.4393, 0.1655, 0.2840])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 28.13305163383484 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       978,      1920,  ...,
+                             99998042,  99999027, 100000000]),
+       col_indices=tensor([   99,   121,   146,  ..., 99677, 99702, 99983]),
+       values=tensor([0.9922, 0.1937, 0.7553,  ..., 0.0650, 0.9551, 0.6219]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.3364, 0.0616, 0.7992,  ..., 0.4393, 0.1655, 0.2840])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 28.13305163383484 seconds
+
+[18.88, 18.44, 18.83, 23.29, 18.84, 18.42, 19.21, 18.51, 18.57, 18.64]
+[53.41]
+69.89152479171753
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 28.13305163383484, 'TIME_S_1KI': 281.3305163383484, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3732.906339125633, 'W': 53.41}
+[18.88, 18.44, 18.83, 23.29, 18.84, 18.42, 19.21, 18.51, 18.57, 18.64, 19.21, 18.5, 18.61, 22.68, 19.39, 18.55, 19.14, 18.61, 18.77, 18.59]
+346.02
+17.301
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 28.13305163383484, 'TIME_S_1KI': 281.3305163383484, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3732.906339125633, 'W': 53.41, 'J_1KI': 37329.063391256335, 'W_1KI': 534.1, 'W_D': 36.108999999999995, 'J_D': 2523.713068704128, 'W_D_1KI': 361.09, 'J_D_1KI': 3610.8999999999996}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json
index 9c4b03d..2289536 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 8087, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.519959688186646, "TIME_S_1KI": 1.300848236451916, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 670.7503997087479, "W": 46.91, "J_1KI": 82.94180780372794, "W_1KI": 5.800667738345492, "W_D": 30.164499999999997, "J_D": 431.3120961844921, "W_D_1KI": 3.729998763447508, "J_D_1KI": 0.46123392648046346}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 8021, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.439693927764893, "TIME_S_1KI": 1.3015451848603532, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 753.044820907116, "W": 52.77, "J_1KI": 93.88415670204662, "W_1KI": 6.578980177035283, "W_D": 35.9735, "J_D": 513.3533800436259, "W_D_1KI": 4.484914599177159, "J_D_1KI": 0.5591465651635905}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output
index cc342ed..c0a1649 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.14809489250183105}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.14688372611999512}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      3,  ...,  99993,  99994,
+tensor(crow_indices=tensor([     0,      0,      1,  ..., 100000, 100000,
                             100000]),
-       col_indices=tensor([11566,  2001, 14819,  ..., 49184, 52555, 95716]),
-       values=tensor([0.6903, 0.1382, 0.4591,  ..., 0.3067, 0.8088, 0.6364]),
+       col_indices=tensor([91487, 18138, 33632,  ..., 69542,  2807, 61553]),
+       values=tensor([0.3172, 0.5107, 0.8105,  ..., 0.7415, 0.8638, 0.9887]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.9563, 0.6034, 0.0890,  ..., 0.8548, 0.6115, 0.7911])
+tensor([0.9817, 0.8005, 0.9250,  ..., 0.8478, 0.4886, 0.4801])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 0.14809489250183105 seconds
+Time: 0.14688372611999512 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7090', '-ss', '100000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.205029487609863}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7148', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.356493711471558}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99999, 100000,
                             100000]),
-       col_indices=tensor([ 7711, 16815, 22150,  ..., 77554, 50594, 27282]),
-       values=tensor([0.3735, 0.5582, 0.2278,  ..., 0.2317, 0.9623, 0.5188]),
+       col_indices=tensor([72680, 33748, 44813,  ..., 44201,  1700, 10017]),
+       values=tensor([0.7425, 0.3395, 0.2846,  ..., 0.1862, 0.0564, 0.9994]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8847, 0.2063, 0.0570,  ..., 0.4149, 0.1346, 0.4208])
+tensor([0.9156, 0.9400, 0.0601,  ..., 0.4846, 0.4224, 0.5521])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 9.205029487609863 seconds
+Time: 9.356493711471558 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8087', '-ss', '100000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.519959688186646}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8021', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.439693927764893}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      3,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99999, 100000,
                             100000]),
-       col_indices=tensor([95443, 50058, 77222,  ..., 43317,  3451, 10339]),
-       values=tensor([0.1078, 0.8522, 0.7935,  ..., 0.8133, 0.3945, 0.6126]),
+       col_indices=tensor([44228, 41904, 44214,  ..., 74875, 97256, 58231]),
+       values=tensor([0.4903, 0.3681, 0.9972,  ..., 0.7317, 0.3326, 0.0360]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8231, 0.3876, 0.1205,  ..., 0.1479, 0.8608, 0.1605])
+tensor([0.0537, 0.8160, 0.1280,  ..., 0.5848, 0.5575, 0.6796])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.519959688186646 seconds
+Time: 10.439693927764893 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      3,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99999, 100000,
                             100000]),
-       col_indices=tensor([95443, 50058, 77222,  ..., 43317,  3451, 10339]),
-       values=tensor([0.1078, 0.8522, 0.7935,  ..., 0.8133, 0.3945, 0.6126]),
+       col_indices=tensor([44228, 41904, 44214,  ..., 74875, 97256, 58231]),
+       values=tensor([0.4903, 0.3681, 0.9972,  ..., 0.7317, 0.3326, 0.0360]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8231, 0.3876, 0.1205,  ..., 0.1479, 0.8608, 0.1605])
+tensor([0.0537, 0.8160, 0.1280,  ..., 0.5848, 0.5575, 0.6796])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.519959688186646 seconds
+Time: 10.439693927764893 seconds
 
-[18.45, 18.35, 18.06, 17.99, 17.93, 18.06, 18.08, 17.93, 22.44, 18.22]
-[46.91]
-14.298665523529053
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8087, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.519959688186646, 'TIME_S_1KI': 1.300848236451916, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 670.7503997087479, 'W': 46.91}
-[18.45, 18.35, 18.06, 17.99, 17.93, 18.06, 18.08, 17.93, 22.44, 18.22, 19.6, 17.81, 18.45, 17.9, 22.14, 18.02, 18.39, 17.96, 18.34, 17.85]
-334.90999999999997
-16.7455
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8087, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.519959688186646, 'TIME_S_1KI': 1.300848236451916, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 670.7503997087479, 'W': 46.91, 'J_1KI': 82.94180780372794, 'W_1KI': 5.800667738345492, 'W_D': 30.164499999999997, 'J_D': 431.3120961844921, 'W_D_1KI': 3.729998763447508, 'J_D_1KI': 0.46123392648046346}
+[18.87, 19.11, 18.57, 18.45, 18.65, 18.59, 18.65, 18.89, 18.53, 18.76]
+[52.77]
+14.270320653915405
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8021, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.439693927764893, 'TIME_S_1KI': 1.3015451848603532, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 753.044820907116, 'W': 52.77}
+[18.87, 19.11, 18.57, 18.45, 18.65, 18.59, 18.65, 18.89, 18.53, 18.76, 18.94, 18.41, 18.64, 18.76, 18.81, 18.52, 18.51, 18.74, 18.6, 18.43]
+335.93000000000006
+16.7965
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8021, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.439693927764893, 'TIME_S_1KI': 1.3015451848603532, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 753.044820907116, 'W': 52.77, 'J_1KI': 93.88415670204662, 'W_1KI': 6.578980177035283, 'W_D': 35.9735, 'J_D': 513.3533800436259, 'W_D_1KI': 4.484914599177159, 'J_D_1KI': 0.5591465651635905}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.json
index 0e52b1a..d4df35b 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 4760, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.509625434875488, "TIME_S_1KI": 2.2079045031251026, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 685.1951848602295, "W": 48.04, "J_1KI": 143.94856824794735, "W_1KI": 10.092436974789916, "W_D": 31.590249999999997, "J_D": 450.57217295026777, "W_D_1KI": 6.636607142857142, "J_D_1KI": 1.3942451980792314}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 4750, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.478902816772461, "TIME_S_1KI": 2.206084803531044, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 770.5235755848885, "W": 53.69, "J_1KI": 162.2154895968186, "W_1KI": 11.303157894736842, "W_D": 36.774, "J_D": 527.7562668757439, "W_D_1KI": 7.741894736842106, "J_D_1KI": 1.6298725761772854}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.output
index e5c487f..d80ad0e 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.2381742000579834}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.23902273178100586}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,     10,  ..., 499988, 499996,
+tensor(crow_indices=tensor([     0,      4,     10,  ..., 499990, 499996,
                             500000]),
-       col_indices=tensor([ 7829, 21471, 22951,  ..., 29509, 41224, 66852]),
-       values=tensor([0.9739, 0.6225, 0.8607,  ..., 0.0619, 0.3093, 0.0510]),
+       col_indices=tensor([46215, 48676, 63655,  ..., 18613, 29803, 54196]),
+       values=tensor([0.6348, 0.4657, 0.3860,  ..., 0.9465, 0.8143, 0.3811]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.6694, 0.1094, 0.7903,  ..., 0.4860, 0.7386, 0.2172])
+tensor([0.1130, 0.6589, 0.4685,  ..., 0.3406, 0.2852, 0.5797])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 0.2381742000579834 seconds
+Time: 0.23902273178100586 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4408', '-ss', '100000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.722268104553223}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4392', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.70858883857727}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,      8,  ..., 499991, 499997,
+tensor(crow_indices=tensor([     0,      4,     10,  ..., 499991, 499995,
                             500000]),
-       col_indices=tensor([ 8468, 11831, 46487,  ..., 65418, 70471, 71020]),
-       values=tensor([0.5611, 0.2625, 0.0139,  ..., 0.7643, 0.0263, 0.5630]),
+       col_indices=tensor([13857, 24838, 53079,  ..., 51175, 58095, 86985]),
+       values=tensor([0.7254, 0.5307, 0.5131,  ..., 0.5524, 0.8711, 0.6845]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.6021, 0.3259, 0.4454,  ..., 0.1291, 0.3066, 0.3093])
+tensor([0.3462, 0.9722, 0.1255,  ..., 0.6830, 0.5801, 0.1671])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 9.722268104553223 seconds
+Time: 9.70858883857727 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4760', '-ss', '100000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.509625434875488}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4750', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.478902816772461}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      7,  ..., 499986, 499991,
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 499991, 499993,
                             500000]),
-       col_indices=tensor([37454, 51813, 86506,  ..., 62954, 73906, 92773]),
-       values=tensor([0.8256, 0.7091, 0.8154,  ..., 0.4160, 0.7952, 0.1689]),
+       col_indices=tensor([19509, 54538, 74720,  ..., 64544, 67166, 82018]),
+       values=tensor([0.3550, 0.7838, 0.6148,  ..., 0.8985, 0.6480, 0.7596]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.1880, 0.2475, 0.0895,  ..., 0.2917, 0.5906, 0.9519])
+tensor([0.5561, 0.3606, 0.7254,  ..., 0.6601, 0.9264, 0.9766])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.509625434875488 seconds
+Time: 10.478902816772461 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      7,  ..., 499986, 499991,
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 499991, 499993,
                             500000]),
-       col_indices=tensor([37454, 51813, 86506,  ..., 62954, 73906, 92773]),
-       values=tensor([0.8256, 0.7091, 0.8154,  ..., 0.4160, 0.7952, 0.1689]),
+       col_indices=tensor([19509, 54538, 74720,  ..., 64544, 67166, 82018]),
+       values=tensor([0.3550, 0.7838, 0.6148,  ..., 0.8985, 0.6480, 0.7596]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.1880, 0.2475, 0.0895,  ..., 0.2917, 0.5906, 0.9519])
+tensor([0.5561, 0.3606, 0.7254,  ..., 0.6601, 0.9264, 0.9766])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.509625434875488 seconds
+Time: 10.478902816772461 seconds
 
-[18.07, 18.03, 18.14, 17.98, 17.8, 18.31, 18.47, 18.23, 18.16, 17.92]
-[48.04]
-14.26301383972168
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4760, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.509625434875488, 'TIME_S_1KI': 2.2079045031251026, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 685.1951848602295, 'W': 48.04}
-[18.07, 18.03, 18.14, 17.98, 17.8, 18.31, 18.47, 18.23, 18.16, 17.92, 18.33, 17.81, 18.24, 18.05, 18.09, 17.75, 17.78, 21.35, 18.67, 17.95]
-328.995
-16.44975
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4760, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.509625434875488, 'TIME_S_1KI': 2.2079045031251026, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 685.1951848602295, 'W': 48.04, 'J_1KI': 143.94856824794735, 'W_1KI': 10.092436974789916, 'W_D': 31.590249999999997, 'J_D': 450.57217295026777, 'W_D_1KI': 6.636607142857142, 'J_D_1KI': 1.3942451980792314}
+[23.96, 18.57, 18.91, 18.97, 18.6, 18.54, 18.61, 18.77, 18.53, 18.41]
+[53.69]
+14.35134243965149
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4750, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.478902816772461, 'TIME_S_1KI': 2.206084803531044, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 770.5235755848885, 'W': 53.69}
+[23.96, 18.57, 18.91, 18.97, 18.6, 18.54, 18.61, 18.77, 18.53, 18.41, 18.78, 18.69, 18.58, 18.42, 18.72, 18.51, 18.59, 18.62, 18.9, 18.43]
+338.32
+16.916
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4750, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.478902816772461, 'TIME_S_1KI': 2.206084803531044, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 770.5235755848885, 'W': 53.69, 'J_1KI': 162.2154895968186, 'W_1KI': 11.303157894736842, 'W_D': 36.774, 'J_D': 527.7562668757439, 'W_D_1KI': 7.741894736842106, 'J_D_1KI': 1.6298725761772854}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json
index 20b7d93..232248f 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 84718, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.541555881500244, "TIME_S_1KI": 0.12443112303761, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 661.3758532905579, "W": 46.44, "J_1KI": 7.80679257407585, "W_1KI": 0.5481715810099388, "W_D": 30.123999999999995, "J_D": 429.01133084678645, "W_D_1KI": 0.3555796879057579, "J_D_1KI": 0.004197215325028422}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 84535, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.447430849075317, "TIME_S_1KI": 0.12358704500000375, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 746.7200598120689, "W": 52.35, "J_1KI": 8.833265035926763, "W_1KI": 0.6192701248003786, "W_D": 35.134750000000004, "J_D": 501.1618456825615, "W_D_1KI": 0.41562370615721306, "J_D_1KI": 0.00491658728523349}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output
index 79fd885..1cb8566 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.028100967407226562}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.028455734252929688}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ...,  9999, 10000, 10000]),
-       col_indices=tensor([1696, 2591, 3015,  ..., 1730, 8585, 3790]),
-       values=tensor([0.6837, 0.7697, 0.7550,  ..., 0.1323, 0.4514, 0.4553]),
+tensor(crow_indices=tensor([    0,     0,     3,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([2863, 3203, 5585,  ..., 4025, 1664, 4563]),
+       values=tensor([0.8169, 0.1123, 0.9845,  ..., 0.3088, 0.1015, 0.1122]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1557, 0.9230, 0.6401,  ..., 0.3725, 0.8926, 0.6402])
+tensor([0.5166, 0.1713, 0.7752,  ..., 0.5891, 0.3496, 0.5710])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 0.028100967407226562 seconds
+Time: 0.028455734252929688 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '37365', '-ss', '10000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 4.631014823913574}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '36899', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 4.583176374435425}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     4,  ...,  9998,  9998, 10000]),
-       col_indices=tensor([2386, 8388, 9261,  ..., 1344, 2569, 4425]),
-       values=tensor([0.6000, 0.2415, 0.7139,  ..., 0.1197, 0.4001, 0.0791]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([  38, 7868, 9573,  ..., 9594, 8912, 2909]),
+       values=tensor([0.9633, 0.5599, 0.7433,  ..., 0.5575, 0.2197, 0.0804]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.9194, 0.6903, 0.0708,  ..., 0.1917, 0.6424, 0.6800])
+tensor([0.9040, 0.9660, 0.9215,  ..., 0.1976, 0.2947, 0.7033])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 4.631014823913574 seconds
+Time: 4.583176374435425 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '84718', '-ss', '10000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.541555881500244}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '84535', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.447430849075317}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9999, 10000]),
-       col_indices=tensor([6630, 3375,  966,  ..., 5451,   76,  624]),
-       values=tensor([0.0314, 0.6841, 0.2123,  ..., 0.3011, 0.8872, 0.9156]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([2318, 5199, 4784,  ...,  521, 6780, 7890]),
+       values=tensor([0.6235, 0.7346, 0.8772,  ..., 0.1411, 0.9530, 0.2973]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.4846, 0.1841, 0.4323,  ..., 0.0718, 0.1957, 0.5902])
+tensor([0.9172, 0.1812, 0.4417,  ..., 0.6631, 0.2007, 0.7007])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.541555881500244 seconds
+Time: 10.447430849075317 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9999, 10000]),
-       col_indices=tensor([6630, 3375,  966,  ..., 5451,   76,  624]),
-       values=tensor([0.0314, 0.6841, 0.2123,  ..., 0.3011, 0.8872, 0.9156]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([2318, 5199, 4784,  ...,  521, 6780, 7890]),
+       values=tensor([0.6235, 0.7346, 0.8772,  ..., 0.1411, 0.9530, 0.2973]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.4846, 0.1841, 0.4323,  ..., 0.0718, 0.1957, 0.5902])
+tensor([0.9172, 0.1812, 0.4417,  ..., 0.6631, 0.2007, 0.7007])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.541555881500244 seconds
+Time: 10.447430849075317 seconds
 
-[18.42, 18.02, 18.15, 18.67, 18.24, 18.06, 18.05, 17.99, 18.11, 18.07]
-[46.44]
-14.241512775421143
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 84718, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.541555881500244, 'TIME_S_1KI': 0.12443112303761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.3758532905579, 'W': 46.44}
-[18.42, 18.02, 18.15, 18.67, 18.24, 18.06, 18.05, 17.99, 18.11, 18.07, 18.36, 18.1, 17.99, 18.16, 18.02, 18.13, 18.12, 18.03, 18.02, 18.07]
-326.32000000000005
-16.316000000000003
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 84718, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.541555881500244, 'TIME_S_1KI': 0.12443112303761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.3758532905579, 'W': 46.44, 'J_1KI': 7.80679257407585, 'W_1KI': 0.5481715810099388, 'W_D': 30.123999999999995, 'J_D': 429.01133084678645, 'W_D_1KI': 0.3555796879057579, 'J_D_1KI': 0.004197215325028422}
+[19.01, 18.45, 19.05, 18.83, 20.38, 18.78, 18.93, 18.59, 19.06, 18.59]
+[52.35]
+14.263993501663208
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 84535, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.447430849075317, 'TIME_S_1KI': 0.12358704500000375, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 746.7200598120689, 'W': 52.35}
+[19.01, 18.45, 19.05, 18.83, 20.38, 18.78, 18.93, 18.59, 19.06, 18.59, 19.13, 18.92, 18.71, 18.62, 18.73, 18.82, 18.69, 23.04, 19.11, 18.46]
+344.30499999999995
+17.215249999999997
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 84535, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.447430849075317, 'TIME_S_1KI': 0.12358704500000375, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 746.7200598120689, 'W': 52.35, 'J_1KI': 8.833265035926763, 'W_1KI': 0.6192701248003786, 'W_D': 35.134750000000004, 'J_D': 501.1618456825615, 'W_D_1KI': 0.41562370615721306, 'J_D_1KI': 0.00491658728523349}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json
index e07fffc..616dd52 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 34651, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.471668004989624, "TIME_S_1KI": 0.30220391922281103, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 677.0323536157608, "W": 47.45, "J_1KI": 19.53860937969354, "W_1KI": 1.369368849383856, "W_D": 30.828750000000007, "J_D": 439.8748402851821, "W_D_1KI": 0.8896929381547433, "J_D_1KI": 0.025675822866720825}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 34381, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.354862451553345, "TIME_S_1KI": 0.3011797926631961, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 758.8139896988869, "W": 53.45, "J_1KI": 22.070736444515482, "W_1KI": 1.5546377359588146, "W_D": 36.52275, "J_D": 518.5027809593082, "W_D_1KI": 1.0622945813094442, "J_D_1KI": 0.030897722035701233}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output
index ff06711..59d193d 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.04611039161682129}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.04586505889892578}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      9,     22,  ...,  99983,  99992,
+tensor(crow_indices=tensor([     0,      7,     16,  ...,  99984,  99991,
                             100000]),
-       col_indices=tensor([ 845, 1153, 1508,  ..., 8313, 9367, 9854]),
-       values=tensor([0.8746, 0.4039, 0.9243,  ..., 0.5657, 0.2713, 0.6449]),
+       col_indices=tensor([1286, 1569, 6335,  ..., 5067, 8495, 9347]),
+       values=tensor([0.2454, 0.5356, 0.5719,  ..., 0.1387, 0.3718, 0.5544]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.3540, 0.2628, 0.4314,  ..., 0.0912, 0.3507, 0.8651])
+tensor([0.4425, 0.1808, 0.7035,  ..., 0.1236, 0.9167, 0.8594])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 0.04611039161682129 seconds
+Time: 0.04586505889892578 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22771', '-ss', '10000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 6.900022268295288}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22893', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 6.991376161575317}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     19,  ...,  99978,  99991,
+tensor(crow_indices=tensor([     0,      7,     22,  ...,  99980,  99993,
                             100000]),
-       col_indices=tensor([ 917, 1959, 2965,  ..., 8075, 8263, 9058]),
-       values=tensor([0.2271, 0.8712, 0.9636,  ..., 0.2167, 0.1262, 0.7253]),
+       col_indices=tensor([1030, 1120, 3717,  ..., 6893, 8161, 8605]),
+       values=tensor([0.5471, 0.9720, 0.9415,  ..., 0.5145, 0.9989, 0.2057]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8029, 0.1325, 0.2655,  ..., 0.5832, 0.4718, 0.3144])
+tensor([0.3134, 0.0807, 0.4892,  ..., 0.6457, 0.4386, 0.1718])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 6.900022268295288 seconds
+Time: 6.991376161575317 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '34651', '-ss', '10000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.471668004989624}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '34381', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.354862451553345}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,     14,  ...,  99994,  99997,
+tensor(crow_indices=tensor([     0,     13,     24,  ...,  99984,  99990,
                             100000]),
-       col_indices=tensor([  24, 1396, 2236,  ..., 5590, 6310, 9874]),
-       values=tensor([0.4982, 0.6812, 0.1465,  ..., 0.3747, 0.0311, 0.9162]),
+       col_indices=tensor([  47,  400, 2720,  ..., 8715, 9139, 9752]),
+       values=tensor([0.3311, 0.8378, 0.2116,  ..., 0.1501, 0.6097, 0.8050]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7533, 0.0703, 0.6276,  ..., 0.6008, 0.2603, 0.3256])
+tensor([0.8329, 0.0693, 0.6537,  ..., 0.9223, 0.1997, 0.5278])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.471668004989624 seconds
+Time: 10.354862451553345 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,     14,  ...,  99994,  99997,
+tensor(crow_indices=tensor([     0,     13,     24,  ...,  99984,  99990,
                             100000]),
-       col_indices=tensor([  24, 1396, 2236,  ..., 5590, 6310, 9874]),
-       values=tensor([0.4982, 0.6812, 0.1465,  ..., 0.3747, 0.0311, 0.9162]),
+       col_indices=tensor([  47,  400, 2720,  ..., 8715, 9139, 9752]),
+       values=tensor([0.3311, 0.8378, 0.2116,  ..., 0.1501, 0.6097, 0.8050]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7533, 0.0703, 0.6276,  ..., 0.6008, 0.2603, 0.3256])
+tensor([0.8329, 0.0693, 0.6537,  ..., 0.9223, 0.1997, 0.5278])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.471668004989624 seconds
+Time: 10.354862451553345 seconds
 
-[18.34, 18.19, 18.22, 18.02, 22.16, 18.64, 18.12, 17.87, 17.95, 18.81]
-[47.45]
-14.26833200454712
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 34651, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.471668004989624, 'TIME_S_1KI': 0.30220391922281103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 677.0323536157608, 'W': 47.45}
-[18.34, 18.19, 18.22, 18.02, 22.16, 18.64, 18.12, 17.87, 17.95, 18.81, 22.38, 17.93, 18.41, 17.77, 18.07, 17.88, 18.52, 17.88, 18.1, 17.86]
-332.42499999999995
-16.621249999999996
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 34651, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.471668004989624, 'TIME_S_1KI': 0.30220391922281103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 677.0323536157608, 'W': 47.45, 'J_1KI': 19.53860937969354, 'W_1KI': 1.369368849383856, 'W_D': 30.828750000000007, 'J_D': 439.8748402851821, 'W_D_1KI': 0.8896929381547433, 'J_D_1KI': 0.025675822866720825}
+[18.93, 18.77, 18.52, 18.65, 18.56, 18.49, 18.5, 18.59, 18.48, 22.8]
+[53.45]
+14.196707010269165
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 34381, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.354862451553345, 'TIME_S_1KI': 0.3011797926631961, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 758.8139896988869, 'W': 53.45}
+[18.93, 18.77, 18.52, 18.65, 18.56, 18.49, 18.5, 18.59, 18.48, 22.8, 18.87, 18.53, 18.61, 18.49, 18.52, 18.82, 18.88, 19.97, 18.54, 18.65]
+338.54499999999996
+16.927249999999997
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 34381, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.354862451553345, 'TIME_S_1KI': 0.3011797926631961, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 758.8139896988869, 'W': 53.45, 'J_1KI': 22.070736444515482, 'W_1KI': 1.5546377359588146, 'W_D': 36.52275, 'J_D': 518.5027809593082, 'W_D_1KI': 1.0622945813094442, 'J_D_1KI': 0.030897722035701233}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json
index 89c422b..6b71db3 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 5583, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.513006925582886, "TIME_S_1KI": 1.8830390337780558, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 692.6418607521057, "W": 47.919999999999995, "J_1KI": 124.06266536845884, "W_1KI": 8.58319899695504, "W_D": 31.175249999999995, "J_D": 450.611084503591, "W_D_1KI": 5.583960236432024, "J_D_1KI": 1.0001719929127753}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 5568, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.491016387939453, "TIME_S_1KI": 1.884162425994873, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 784.1237088561058, "W": 54.21, "J_1KI": 140.82681552731785, "W_1KI": 9.735991379310345, "W_D": 37.368, "J_D": 540.5116169071198, "W_D_1KI": 6.711206896551724, "J_D_1KI": 1.2053173305588585}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output
index d152642..3d068e7 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.2040116786956787}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.20917105674743652}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     106,     193,  ...,  999811,
-                             999905, 1000000]),
-       col_indices=tensor([ 107,  139,  344,  ..., 9485, 9560, 9767]),
-       values=tensor([0.2657, 0.7219, 0.2773,  ..., 0.6022, 0.5377, 0.2291]),
+tensor(crow_indices=tensor([      0,     108,     217,  ...,  999801,
+                             999904, 1000000]),
+       col_indices=tensor([ 201,  293,  363,  ..., 9740, 9784, 9831]),
+       values=tensor([0.0755, 0.8455, 0.1421,  ..., 0.4938, 0.3767, 0.3652]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8604, 0.1859, 0.3719,  ..., 0.6286, 0.9460, 0.3185])
+tensor([0.5155, 0.3962, 0.4386,  ..., 0.0611, 0.7322, 0.1065])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 0.2040116786956787 seconds
+Time: 0.20917105674743652 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5146', '-ss', '10000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.677676439285278}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5019', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.464475870132446}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      90,     186,  ...,  999811,
-                             999907, 1000000]),
-       col_indices=tensor([  74,  208,  311,  ..., 9654, 9863, 9976]),
-       values=tensor([0.0395, 0.4059, 0.0831,  ..., 0.6188, 0.9591, 0.8953]),
+tensor(crow_indices=tensor([      0,      94,     204,  ...,  999823,
+                             999910, 1000000]),
+       col_indices=tensor([   9,  178,  235,  ..., 9823, 9912, 9951]),
+       values=tensor([0.9644, 0.3014, 0.0048,  ..., 0.8699, 0.7725, 0.2107]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6360, 0.9265, 0.4313,  ..., 0.6926, 0.7242, 0.0651])
+tensor([0.4099, 0.5837, 0.3039,  ..., 0.5378, 0.1585, 0.3682])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 9.677676439285278 seconds
+Time: 9.464475870132446 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5583', '-ss', '10000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.513006925582886}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5568', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.491016387939453}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     104,     207,  ...,  999792,
-                             999898, 1000000]),
-       col_indices=tensor([ 168,  206,  240,  ..., 9827, 9842, 9996]),
-       values=tensor([0.8276, 0.5768, 0.6424,  ..., 0.0752, 0.7475, 0.3129]),
+tensor(crow_indices=tensor([      0,      90,     187,  ...,  999816,
+                             999910, 1000000]),
+       col_indices=tensor([ 130,  331,  358,  ..., 9312, 9659, 9864]),
+       values=tensor([0.2328, 0.9346, 0.2422,  ..., 0.1433, 0.4918, 0.2397]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.7571, 0.4178, 0.1860,  ..., 0.0563, 0.6255, 0.7203])
+tensor([0.3909, 0.7739, 0.3092,  ..., 0.2381, 0.8305, 0.8369])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.513006925582886 seconds
+Time: 10.491016387939453 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     104,     207,  ...,  999792,
-                             999898, 1000000]),
-       col_indices=tensor([ 168,  206,  240,  ..., 9827, 9842, 9996]),
-       values=tensor([0.8276, 0.5768, 0.6424,  ..., 0.0752, 0.7475, 0.3129]),
+tensor(crow_indices=tensor([      0,      90,     187,  ...,  999816,
+                             999910, 1000000]),
+       col_indices=tensor([ 130,  331,  358,  ..., 9312, 9659, 9864]),
+       values=tensor([0.2328, 0.9346, 0.2422,  ..., 0.1433, 0.4918, 0.2397]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.7571, 0.4178, 0.1860,  ..., 0.0563, 0.6255, 0.7203])
+tensor([0.3909, 0.7739, 0.3092,  ..., 0.2381, 0.8305, 0.8369])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.513006925582886 seconds
+Time: 10.491016387939453 seconds
 
-[18.38, 17.86, 18.24, 17.87, 19.06, 17.88, 18.29, 21.74, 18.54, 18.08]
-[47.92]
-14.454128980636597
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5583, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.513006925582886, 'TIME_S_1KI': 1.8830390337780558, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.6418607521057, 'W': 47.919999999999995}
-[18.38, 17.86, 18.24, 17.87, 19.06, 17.88, 18.29, 21.74, 18.54, 18.08, 18.58, 17.99, 17.96, 22.09, 18.22, 17.97, 17.93, 18.49, 18.13, 18.23]
-334.895
-16.74475
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5583, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.513006925582886, 'TIME_S_1KI': 1.8830390337780558, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.6418607521057, 'W': 47.919999999999995, 'J_1KI': 124.06266536845884, 'W_1KI': 8.58319899695504, 'W_D': 31.175249999999995, 'J_D': 450.611084503591, 'W_D_1KI': 5.583960236432024, 'J_D_1KI': 1.0001719929127753}
+[18.94, 18.64, 18.65, 18.57, 18.36, 18.8, 18.73, 18.52, 18.5, 18.61]
+[54.21]
+14.464558362960815
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5568, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.491016387939453, 'TIME_S_1KI': 1.884162425994873, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 784.1237088561058, 'W': 54.21}
+[18.94, 18.64, 18.65, 18.57, 18.36, 18.8, 18.73, 18.52, 18.5, 18.61, 18.94, 18.45, 18.95, 18.62, 18.72, 18.63, 19.37, 18.88, 18.82, 18.77]
+336.84
+16.842
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5568, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.491016387939453, 'TIME_S_1KI': 1.884162425994873, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 784.1237088561058, 'W': 54.21, 'J_1KI': 140.82681552731785, 'W_1KI': 9.735991379310345, 'W_D': 37.368, 'J_D': 540.5116169071198, 'W_D_1KI': 6.711206896551724, 'J_D_1KI': 1.2053173305588585}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json
index 20cc2bc..79d1b9e 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 959, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.294347047805786, "TIME_S_1KI": 10.73445990386422, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 792.9360737204552, "W": 48.35, "J_1KI": 826.8363646720074, "W_1KI": 50.417101147028156, "W_D": 31.9585, "J_D": 524.1168047982454, "W_D_1KI": 33.324817518248175, "J_D_1KI": 34.74954902841311}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 984, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.35936164855957, "TIME_S_1KI": 10.527806553414196, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 874.1194585132598, "W": 54.41, "J_1KI": 888.3327830419307, "W_1KI": 55.294715447154466, "W_D": 37.35825, "J_D": 600.175946719408, "W_D_1KI": 37.96570121951219, "J_D_1KI": 38.583029694626205}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output
index 643494f..8890410 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 1.0941581726074219}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 1.0661232471466064}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     514,    1009,  ..., 4998988,
-                            4999478, 5000000]),
-       col_indices=tensor([   8,   23,   83,  ..., 9969, 9982, 9990]),
-       values=tensor([0.1393, 0.4453, 0.1108,  ..., 0.3215, 0.7885, 0.8444]),
+tensor(crow_indices=tensor([      0,     512,    1006,  ..., 4999050,
+                            4999502, 5000000]),
+       col_indices=tensor([  27,   66,   69,  ..., 9942, 9959, 9960]),
+       values=tensor([0.7928, 0.2549, 0.9608,  ..., 0.7743, 0.6342, 0.3657]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.3317, 0.0622, 0.5595,  ..., 0.2290, 0.2268, 0.9236])
+tensor([0.4712, 0.8826, 0.6721,  ..., 0.6632, 0.2423, 0.7939])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 1.0941581726074219 seconds
+Time: 1.0661232471466064 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '959', '-ss', '10000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.294347047805786}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '984', '-ss', '10000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.35936164855957}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     514,     994,  ..., 4999000,
-                            4999500, 5000000]),
-       col_indices=tensor([  13,   16,   23,  ..., 9955, 9988, 9993]),
-       values=tensor([0.2414, 0.9977, 0.9772,  ..., 0.9200, 0.6029, 0.8714]),
+tensor(crow_indices=tensor([      0,     464,     981,  ..., 4998963,
+                            4999483, 5000000]),
+       col_indices=tensor([  30,   35,   45,  ..., 9959, 9962, 9993]),
+       values=tensor([0.5322, 0.6168, 0.7005,  ..., 0.6069, 0.6645, 0.7150]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.0530, 0.1017, 0.7510,  ..., 0.2543, 0.0728, 0.9686])
+tensor([0.4721, 0.8060, 0.5631,  ..., 0.4372, 0.7905, 0.4509])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.294347047805786 seconds
+Time: 10.35936164855957 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     514,     994,  ..., 4999000,
-                            4999500, 5000000]),
-       col_indices=tensor([  13,   16,   23,  ..., 9955, 9988, 9993]),
-       values=tensor([0.2414, 0.9977, 0.9772,  ..., 0.9200, 0.6029, 0.8714]),
+tensor(crow_indices=tensor([      0,     464,     981,  ..., 4998963,
+                            4999483, 5000000]),
+       col_indices=tensor([  30,   35,   45,  ..., 9959, 9962, 9993]),
+       values=tensor([0.5322, 0.6168, 0.7005,  ..., 0.6069, 0.6645, 0.7150]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.0530, 0.1017, 0.7510,  ..., 0.2543, 0.0728, 0.9686])
+tensor([0.4721, 0.8060, 0.5631,  ..., 0.4372, 0.7905, 0.4509])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.294347047805786 seconds
+Time: 10.35936164855957 seconds
 
-[18.47, 18.05, 17.89, 17.81, 18.54, 18.75, 17.99, 17.87, 18.49, 18.81]
-[48.35]
-16.399918794631958
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 959, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.294347047805786, 'TIME_S_1KI': 10.73445990386422, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 792.9360737204552, 'W': 48.35}
-[18.47, 18.05, 17.89, 17.81, 18.54, 18.75, 17.99, 17.87, 18.49, 18.81, 18.47, 17.8, 17.9, 18.12, 18.16, 18.55, 18.0, 18.63, 18.45, 17.91]
-327.83
-16.3915
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 959, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.294347047805786, 'TIME_S_1KI': 10.73445990386422, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 792.9360737204552, 'W': 48.35, 'J_1KI': 826.8363646720074, 'W_1KI': 50.417101147028156, 'W_D': 31.9585, 'J_D': 524.1168047982454, 'W_D_1KI': 33.324817518248175, 'J_D_1KI': 34.74954902841311}
+[19.12, 18.46, 18.53, 18.47, 19.72, 22.18, 18.6, 18.51, 19.45, 18.49]
+[54.41]
+16.06541919708252
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 984, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.35936164855957, 'TIME_S_1KI': 10.527806553414196, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 874.1194585132598, 'W': 54.41}
+[19.12, 18.46, 18.53, 18.47, 19.72, 22.18, 18.6, 18.51, 19.45, 18.49, 19.15, 18.48, 19.07, 18.4, 18.75, 18.52, 18.64, 18.47, 19.05, 18.71]
+341.03499999999997
+17.05175
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 984, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.35936164855957, 'TIME_S_1KI': 10.527806553414196, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 874.1194585132598, 'W': 54.41, 'J_1KI': 888.3327830419307, 'W_1KI': 55.294715447154466, 'W_D': 37.35825, 'J_D': 600.175946719408, 'W_D_1KI': 37.96570121951219, 'J_D_1KI': 38.583029694626205}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.json
index a3bba53..92a8569 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 389, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.411779165267944, "TIME_S_1KI": 26.765499139506282, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 876.1174001097679, "W": 47.85, "J_1KI": 2252.2298203335936, "W_1KI": 123.00771208226222, "W_D": 31.457250000000002, "J_D": 575.971663210094, "W_D_1KI": 80.86696658097686, "J_D_1KI": 207.88423285598165}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 395, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.455632448196411, "TIME_S_1KI": 26.469955565054203, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 950.4409645557403, "W": 54.12, "J_1KI": 2406.1796571031396, "W_1KI": 137.01265822784808, "W_D": 36.991749999999996, "J_D": 649.6392193385958, "W_D_1KI": 93.64999999999999, "J_D_1KI": 237.0886075949367}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.output
index 086f941..8761a48 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 2.693603992462158}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 2.653668165206909}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1004,     2049,  ...,  9997955,
-                             9998949, 10000000]),
-       col_indices=tensor([   1,    3,   35,  ..., 9984, 9987, 9993]),
-       values=tensor([0.3631, 0.8073, 0.7190,  ..., 0.1286, 0.7057, 0.1104]),
+tensor(crow_indices=tensor([       0,      976,     1914,  ...,  9997996,
+                             9998980, 10000000]),
+       col_indices=tensor([  22,   23,   24,  ..., 9979, 9981, 9994]),
+       values=tensor([0.6028, 0.2174, 0.1558,  ..., 0.2391, 0.7709, 0.5491]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.7885, 0.1169, 0.1101,  ..., 0.4416, 0.5822, 0.3212])
+tensor([0.9646, 0.0721, 0.9565,  ..., 0.6113, 0.6933, 0.7146])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 2.693603992462158 seconds
+Time: 2.653668165206909 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '389', '-ss', '10000', '-sd', '0.1', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.411779165267944}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '395', '-ss', '10000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.455632448196411}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      966,     1941,  ...,  9997989,
-                             9998973, 10000000]),
-       col_indices=tensor([   2,   11,   26,  ..., 9956, 9965, 9978]),
-       values=tensor([0.0342, 0.9218, 0.6993,  ..., 0.4506, 0.1146, 0.2093]),
+tensor(crow_indices=tensor([       0,     1007,     2030,  ...,  9998046,
+                             9999015, 10000000]),
+       col_indices=tensor([   7,   39,   40,  ..., 9952, 9976, 9988]),
+       values=tensor([0.2137, 0.2372, 0.5461,  ..., 0.5874, 0.2267, 0.3233]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.6037, 0.3505, 0.1319,  ..., 0.1315, 0.2126, 0.8791])
+tensor([0.3576, 0.7682, 0.3831,  ..., 0.3551, 0.0880, 0.6819])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 10.411779165267944 seconds
+Time: 10.455632448196411 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      966,     1941,  ...,  9997989,
-                             9998973, 10000000]),
-       col_indices=tensor([   2,   11,   26,  ..., 9956, 9965, 9978]),
-       values=tensor([0.0342, 0.9218, 0.6993,  ..., 0.4506, 0.1146, 0.2093]),
+tensor(crow_indices=tensor([       0,     1007,     2030,  ...,  9998046,
+                             9999015, 10000000]),
+       col_indices=tensor([   7,   39,   40,  ..., 9952, 9976, 9988]),
+       values=tensor([0.2137, 0.2372, 0.5461,  ..., 0.5874, 0.2267, 0.3233]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.6037, 0.3505, 0.1319,  ..., 0.1315, 0.2126, 0.8791])
+tensor([0.3576, 0.7682, 0.3831,  ..., 0.3551, 0.0880, 0.6819])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 10.411779165267944 seconds
+Time: 10.455632448196411 seconds
 
-[18.87, 18.17, 18.37, 17.93, 17.97, 18.29, 18.55, 17.92, 18.13, 17.84]
-[47.85]
-18.30966353416443
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 389, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.411779165267944, 'TIME_S_1KI': 26.765499139506282, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 876.1174001097679, 'W': 47.85}
-[18.87, 18.17, 18.37, 17.93, 17.97, 18.29, 18.55, 17.92, 18.13, 17.84, 19.36, 18.07, 17.99, 17.91, 18.82, 18.49, 18.1, 18.04, 18.09, 17.96]
-327.855
-16.39275
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 389, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.411779165267944, 'TIME_S_1KI': 26.765499139506282, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 876.1174001097679, 'W': 47.85, 'J_1KI': 2252.2298203335936, 'W_1KI': 123.00771208226222, 'W_D': 31.457250000000002, 'J_D': 575.971663210094, 'W_D_1KI': 80.86696658097686, 'J_D_1KI': 207.88423285598165}
+[19.36, 18.76, 18.66, 18.48, 19.47, 18.73, 18.73, 18.42, 18.63, 18.7]
+[54.12]
+17.561732530593872
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.455632448196411, 'TIME_S_1KI': 26.469955565054203, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 950.4409645557403, 'W': 54.12}
+[19.36, 18.76, 18.66, 18.48, 19.47, 18.73, 18.73, 18.42, 18.63, 18.7, 18.69, 18.51, 18.48, 19.67, 23.07, 18.55, 18.65, 19.34, 18.71, 18.66]
+342.56500000000005
+17.12825
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 395, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.455632448196411, 'TIME_S_1KI': 26.469955565054203, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 950.4409645557403, 'W': 54.12, 'J_1KI': 2406.1796571031396, 'W_1KI': 137.01265822784808, 'W_D': 36.991749999999996, 'J_D': 649.6392193385958, 'W_D_1KI': 93.64999999999999, 'J_D_1KI': 237.0886075949367}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.json
index 9e5a000..d8afc97 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 193, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.419133424758911, "TIME_S_1KI": 53.98514727854358, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1480.8600453448294, "W": 45.41, "J_1KI": 7672.849975879945, "W_1KI": 235.28497409326422, "W_D": 29.142749999999996, "J_D": 950.3707132013438, "W_D_1KI": 150.9987046632124, "J_D_1KI": 782.3767080995461}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 196, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.46868371963501, "TIME_S_1KI": 53.41165163079087, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1113.3714706707, "W": 53.72, "J_1KI": 5680.466687095409, "W_1KI": 274.08163265306126, "W_D": 36.585499999999996, "J_D": 758.2511530197858, "W_D_1KI": 186.66071428571425, "J_D_1KI": 952.3505830903788}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.output
index f051b42..d57c721 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 5.42013144493103}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 5.35328221321106}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2043,     4027,  ..., 19995921,
-                            19998006, 20000000]),
-       col_indices=tensor([   9,   15,   16,  ..., 9989, 9991, 9993]),
-       values=tensor([0.8685, 0.2737, 0.0800,  ..., 0.3440, 0.3550, 0.7008]),
+tensor(crow_indices=tensor([       0,     1985,     4014,  ..., 19996011,
+                            19998003, 20000000]),
+       col_indices=tensor([   1,    2,    4,  ..., 9982, 9984, 9986]),
+       values=tensor([0.5600, 0.3798, 0.2809,  ..., 0.4925, 0.5579, 0.2402]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.2841, 0.6439, 0.8852,  ..., 0.0124, 0.9656, 0.3759])
+tensor([0.1902, 0.0411, 0.2807,  ..., 0.8123, 0.1809, 0.3115])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 5.42013144493103 seconds
+Time: 5.35328221321106 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '193', '-ss', '10000', '-sd', '0.2', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.419133424758911}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '196', '-ss', '10000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.46868371963501}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1925,     3946,  ..., 19996050,
-                            19998038, 20000000]),
-       col_indices=tensor([   4,    9,   22,  ..., 9994, 9995, 9997]),
-       values=tensor([0.4143, 0.0158, 0.3991,  ..., 0.4975, 0.2189, 0.5132]),
+tensor(crow_indices=tensor([       0,     2016,     4136,  ..., 19996065,
+                            19998058, 20000000]),
+       col_indices=tensor([   0,    1,    6,  ..., 9981, 9991, 9993]),
+       values=tensor([0.1996, 0.0892, 0.4643,  ..., 0.6653, 0.0272, 0.6524]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.4618, 0.1550, 0.6479,  ..., 0.8342, 0.8619, 0.7737])
+tensor([0.9437, 0.4502, 0.6936,  ..., 0.4828, 0.5406, 0.8208])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 10.419133424758911 seconds
+Time: 10.46868371963501 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1925,     3946,  ..., 19996050,
-                            19998038, 20000000]),
-       col_indices=tensor([   4,    9,   22,  ..., 9994, 9995, 9997]),
-       values=tensor([0.4143, 0.0158, 0.3991,  ..., 0.4975, 0.2189, 0.5132]),
+tensor(crow_indices=tensor([       0,     2016,     4136,  ..., 19996065,
+                            19998058, 20000000]),
+       col_indices=tensor([   0,    1,    6,  ..., 9981, 9991, 9993]),
+       values=tensor([0.1996, 0.0892, 0.4643,  ..., 0.6653, 0.0272, 0.6524]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.4618, 0.1550, 0.6479,  ..., 0.8342, 0.8619, 0.7737])
+tensor([0.9437, 0.4502, 0.6936,  ..., 0.4828, 0.5406, 0.8208])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 10.419133424758911 seconds
+Time: 10.46868371963501 seconds
 
-[18.6, 18.13, 18.25, 17.88, 17.94, 17.94, 18.23, 18.23, 18.37, 17.79]
-[45.41]
-32.61087965965271
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.419133424758911, 'TIME_S_1KI': 53.98514727854358, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1480.8600453448294, 'W': 45.41}
-[18.6, 18.13, 18.25, 17.88, 17.94, 17.94, 18.23, 18.23, 18.37, 17.79, 18.29, 17.94, 17.98, 17.82, 18.04, 18.23, 17.96, 17.98, 18.11, 17.95]
-325.345
-16.26725
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.419133424758911, 'TIME_S_1KI': 53.98514727854358, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1480.8600453448294, 'W': 45.41, 'J_1KI': 7672.849975879945, 'W_1KI': 235.28497409326422, 'W_D': 29.142749999999996, 'J_D': 950.3707132013438, 'W_D_1KI': 150.9987046632124, 'J_D_1KI': 782.3767080995461}
+[18.93, 19.98, 18.49, 18.61, 18.55, 19.11, 18.84, 19.4, 22.73, 18.68]
+[53.72]
+20.72545552253723
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 196, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.46868371963501, 'TIME_S_1KI': 53.41165163079087, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1113.3714706707, 'W': 53.72}
+[18.93, 19.98, 18.49, 18.61, 18.55, 19.11, 18.84, 19.4, 22.73, 18.68, 19.44, 18.47, 18.8, 18.41, 18.55, 18.58, 18.94, 18.89, 18.64, 18.35]
+342.69
+17.1345
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 196, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.46868371963501, 'TIME_S_1KI': 53.41165163079087, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1113.3714706707, 'W': 53.72, 'J_1KI': 5680.466687095409, 'W_1KI': 274.08163265306126, 'W_D': 36.585499999999996, 'J_D': 758.2511530197858, 'W_D_1KI': 186.66071428571425, 'J_D_1KI': 952.3505830903788}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.json
index 28c2d57..5f0eef4 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.80802845954895, "TIME_S_1KI": 108.0802845954895, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3840.7128683352466, "W": 37.19, "J_1KI": 38407.128683352465, "W_1KI": 371.9, "W_D": 20.811749999999996, "J_D": 2149.286260757625, "W_D_1KI": 208.11749999999995, "J_D_1KI": 2081.1749999999997}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 130, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.446887969970703, "TIME_S_1KI": 80.36067669208232, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1269.5882425427437, "W": 53.45, "J_1KI": 9766.063404174953, "W_1KI": 411.1538461538462, "W_D": 36.43225, "J_D": 865.3686856759191, "W_D_1KI": 280.24807692307695, "J_D_1KI": 2155.7544378698226}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.output
index 5b24363..31c14f3 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.80802845954895}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 8.05606722831726}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2964,     5988,  ..., 29994017,
-                            29996992, 30000000]),
-       col_indices=tensor([   4,    9,   14,  ..., 9990, 9993, 9995]),
-       values=tensor([0.5510, 0.0884, 0.7125,  ..., 0.7844, 0.3492, 0.1801]),
+tensor(crow_indices=tensor([       0,     3029,     6010,  ..., 29993971,
+                            29997008, 30000000]),
+       col_indices=tensor([   6,    8,   11,  ..., 9995, 9996, 9999]),
+       values=tensor([0.6372, 0.1660, 0.5359,  ..., 0.8195, 0.0231, 0.1758]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.7648, 0.2589, 0.8570,  ..., 0.0438, 0.7014, 0.5513])
+tensor([0.7326, 0.9528, 0.8980,  ..., 0.4303, 0.6858, 0.9257])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 10.80802845954895 seconds
+Time: 8.05606722831726 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '130', '-ss', '10000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.446887969970703}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2964,     5988,  ..., 29994017,
-                            29996992, 30000000]),
-       col_indices=tensor([   4,    9,   14,  ..., 9990, 9993, 9995]),
-       values=tensor([0.5510, 0.0884, 0.7125,  ..., 0.7844, 0.3492, 0.1801]),
+tensor(crow_indices=tensor([       0,     2978,     6072,  ..., 29993838,
+                            29996899, 30000000]),
+       col_indices=tensor([   1,    2,    3,  ..., 9981, 9983, 9987]),
+       values=tensor([0.6017, 0.3434, 0.3105,  ..., 0.2774, 0.3106, 0.3686]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.7648, 0.2589, 0.8570,  ..., 0.0438, 0.7014, 0.5513])
+tensor([0.7100, 0.2738, 0.5674,  ..., 0.9189, 0.4155, 0.7852])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +36,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 10.80802845954895 seconds
+Time: 10.446887969970703 seconds
 
-[18.36, 18.76, 18.08, 17.8, 18.22, 18.13, 18.92, 17.85, 18.59, 18.05]
-[37.19]
-103.27273106575012
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.80802845954895, 'TIME_S_1KI': 108.0802845954895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3840.7128683352466, 'W': 37.19}
-[18.36, 18.76, 18.08, 17.8, 18.22, 18.13, 18.92, 17.85, 18.59, 18.05, 18.39, 18.01, 18.68, 17.8, 18.23, 17.94, 17.9, 17.92, 18.33, 18.01]
-327.56500000000005
-16.37825
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.80802845954895, 'TIME_S_1KI': 108.0802845954895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3840.7128683352466, 'W': 37.19, 'J_1KI': 38407.128683352465, 'W_1KI': 371.9, 'W_D': 20.811749999999996, 'J_D': 2149.286260757625, 'W_D_1KI': 208.11749999999995, 'J_D_1KI': 2081.1749999999997}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2978,     6072,  ..., 29993838,
+                            29996899, 30000000]),
+       col_indices=tensor([   1,    2,    3,  ..., 9981, 9983, 9987]),
+       values=tensor([0.6017, 0.3434, 0.3105,  ..., 0.2774, 0.3106, 0.3686]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.7100, 0.2738, 0.5674,  ..., 0.9189, 0.4155, 0.7852])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.446887969970703 seconds
+
+[19.09, 18.44, 19.0, 18.75, 18.74, 18.86, 18.82, 18.47, 18.69, 18.9]
+[53.45]
+23.752820253372192
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 130, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.446887969970703, 'TIME_S_1KI': 80.36067669208232, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1269.5882425427437, 'W': 53.45}
+[19.09, 18.44, 19.0, 18.75, 18.74, 18.86, 18.82, 18.47, 18.69, 18.9, 19.05, 19.15, 18.83, 18.69, 18.63, 18.47, 18.97, 18.71, 20.03, 21.17]
+340.355
+17.01775
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 130, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.446887969970703, 'TIME_S_1KI': 80.36067669208232, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1269.5882425427437, 'W': 53.45, 'J_1KI': 9766.063404174953, 'W_1KI': 411.1538461538462, 'W_D': 36.43225, 'J_D': 865.3686856759191, 'W_D_1KI': 280.24807692307695, 'J_D_1KI': 2155.7544378698226}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..f90ee1b
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.716163635253906, "TIME_S_1KI": 107.16163635253906, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1446.293502857685, "W": 53.410000000000004, "J_1KI": 14462.93502857685, "W_1KI": 534.1, "W_D": 36.370250000000006, "J_D": 984.8728004551532, "W_D_1KI": 363.70250000000004, "J_D_1KI": 3637.0250000000005}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..1c49c52
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.4.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.4', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.716163635253906}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4022,     8006,  ..., 39992008,
+                            39996052, 40000000]),
+       col_indices=tensor([   2,    5,   11,  ..., 9987, 9990, 9994]),
+       values=tensor([0.9710, 0.1504, 0.2354,  ..., 0.2928, 0.0267, 0.1134]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.1346, 0.0124, 0.5368,  ..., 0.7889, 0.7272, 0.1080])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 10.716163635253906 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4022,     8006,  ..., 39992008,
+                            39996052, 40000000]),
+       col_indices=tensor([   2,    5,   11,  ..., 9987, 9990, 9994]),
+       values=tensor([0.9710, 0.1504, 0.2354,  ..., 0.2928, 0.0267, 0.1134]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.1346, 0.0124, 0.5368,  ..., 0.7889, 0.7272, 0.1080])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 10.716163635253906 seconds
+
+[19.2, 18.48, 18.79, 18.84, 18.86, 18.53, 18.57, 18.59, 18.57, 18.36]
+[53.41]
+27.079077005386353
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.716163635253906, 'TIME_S_1KI': 107.16163635253906, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1446.293502857685, 'W': 53.410000000000004}
+[19.2, 18.48, 18.79, 18.84, 18.86, 18.53, 18.57, 18.59, 18.57, 18.36, 19.07, 18.45, 18.49, 18.67, 22.82, 19.3, 18.62, 19.1, 18.61, 18.38]
+340.79499999999996
+17.039749999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.716163635253906, 'TIME_S_1KI': 107.16163635253906, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1446.293502857685, 'W': 53.410000000000004, 'J_1KI': 14462.93502857685, 'W_1KI': 534.1, 'W_D': 36.370250000000006, 'J_D': 984.8728004551532, 'W_D_1KI': 363.70250000000004, 'J_D_1KI': 3637.0250000000005}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..0d979b3
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 13.625776529312134, "TIME_S_1KI": 136.25776529312134, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1769.4445055365563, "W": 53.31, "J_1KI": 17694.445055365562, "W_1KI": 533.1, "W_D": 36.382000000000005, "J_D": 1207.5770024466517, "W_D_1KI": 363.82000000000005, "J_D_1KI": 3638.2000000000003}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..b29bf4d
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_0.5.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.5', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 13.625776529312134}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     5008,    10008,  ..., 49989993,
+                            49994999, 50000000]),
+       col_indices=tensor([   0,    5,    6,  ..., 9997, 9998, 9999]),
+       values=tensor([0.9462, 0.7067, 0.0407,  ..., 0.1529, 0.0867, 0.3215]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.7597, 0.0756, 0.8548,  ..., 0.0318, 0.4702, 0.4476])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 13.625776529312134 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     5008,    10008,  ..., 49989993,
+                            49994999, 50000000]),
+       col_indices=tensor([   0,    5,    6,  ..., 9997, 9998, 9999]),
+       values=tensor([0.9462, 0.7067, 0.0407,  ..., 0.1529, 0.0867, 0.3215]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.7597, 0.0756, 0.8548,  ..., 0.0318, 0.4702, 0.4476])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 13.625776529312134 seconds
+
+[19.52, 18.56, 18.8, 18.93, 20.12, 18.45, 18.74, 18.63, 19.08, 19.12]
+[53.31]
+33.19160580635071
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 13.625776529312134, 'TIME_S_1KI': 136.25776529312134, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1769.4445055365563, 'W': 53.31}
+[19.52, 18.56, 18.8, 18.93, 20.12, 18.45, 18.74, 18.63, 19.08, 19.12, 18.94, 18.79, 18.56, 18.5, 18.64, 18.65, 18.62, 18.43, 18.61, 19.32]
+338.55999999999995
+16.927999999999997
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 13.625776529312134, 'TIME_S_1KI': 136.25776529312134, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1769.4445055365563, 'W': 53.31, 'J_1KI': 17694.445055365562, 'W_1KI': 533.1, 'W_D': 36.382000000000005, 'J_D': 1207.5770024466517, 'W_D_1KI': 363.82000000000005, 'J_D_1KI': 3638.2000000000003}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json
index d3d85cb..4995eb6 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 223318, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.272708654403687, "TIME_S_1KI": 0.046000361163917314, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 661.7633163213729, "W": 46.54, "J_1KI": 2.963322778823798, "W_1KI": 0.2084023679237679, "W_D": 29.777749999999997, "J_D": 423.41690143078563, "W_D_1KI": 0.13334236380408207, "J_D_1KI": 0.0005970963549919042}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 226437, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.510953187942505, "TIME_S_1KI": 0.046418885552902155, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 749.9334471988677, "W": 52.58, "J_1KI": 3.311885633526622, "W_1KI": 0.23220586741566088, "W_D": 35.7245, "J_D": 509.5282889778614, "W_D_1KI": 0.15776794428472377, "J_D_1KI": 0.0006967410108980589}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output
index 404ad45..9fac80e 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output
@@ -1,373 +1,266 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.02031111717224121}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.021244287490844727}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
-       col_indices=tensor([4137, 7377, 7043, 6108, 9323,  897, 9654,  227, 2578,
-                           4698, 9915, 4015, 9949, 2349, 8008, 1966, 4875, 4040,
-                           6979, 2450, 7633, 6619, 9795, 2725, 5746, 5264, 4390,
-                           2139, 8315, 3676, 8258, 2550, 7703, 6861, 8246, 1325,
-                           6548, 4010, 3106, 8161, 3532, 2108, 7993,  386, 7921,
-                           2704, 9957, 7073, 3331, 9744, 4980, 6346, 1498, 1404,
-                           8824, 1275, 9681, 2862, 4082, 1465, 5274, 7840, 5924,
-                           9483,   98, 2679, 7843, 1423, 2659, 4200, 7882, 4396,
-                           8968, 5460, 7766, 9614, 1590, 8686, 7557, 1225, 5913,
-                           5155, 9823, 7515, 4242,  147, 5927, 2787, 5542, 7655,
-                           5944, 2866, 5695, 9408, 3998, 7348, 7456, 5035, 7898,
-                           6129, 2807, 3798, 4059, 6217, 4055, 6985, 8048, 2902,
-                           2337,  461, 3316, 7537,   42, 5645, 9082, 1247, 3998,
-                           9775, 8363, 2735,   93,  476, 9970, 9170, 4926, 3860,
-                           2000, 8258, 8990, 1304, 1955, 2898, 4931, 9660,  749,
-                           7970, 1430,  136, 1728, 3493, 5173,  838, 9924, 9475,
-                           9208, 3918, 2507, 3992, 2000, 7754, 5632,   87, 7861,
-                           1028, 9056, 3445, 6453, 2164, 8030, 8837, 3400, 1991,
-                           5976, 7138, 4278, 6917, 8431, 3928, 8200, 9576,  544,
-                           3683, 3997, 1043, 2590, 5349, 7018,   58, 9350, 6692,
-                           1721, 3408, 5604,  655, 7107, 7982, 4650, 1645, 8377,
-                           5270, 5374, 9226, 6822, 6438, 8163, 2878, 5855, 3643,
-                           4380, 8884, 7438,   20, 8087, 7305, 2720, 1905, 9868,
-                           6635,  715, 5790,  888, 2709, 5700, 3461, 8946, 8488,
-                           9562, 8957, 6895, 1530, 8225, 2482, 9848, 6363,   60,
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+                      0.3701, 0.0428, 0.3821, 0.0376, 0.4264, 0.4894, 0.0264,
+                      0.2934, 0.7715, 0.0475, 0.4972, 0.4763, 0.7381, 0.0581,
+                      0.4297, 0.2473, 0.9622, 0.8031, 0.4876, 0.3660, 0.3001,
+                      0.9864, 0.7705, 0.2207, 0.7099, 0.7682, 0.1747, 0.2131,
+                      0.1442, 0.6566, 0.0198, 0.5217, 0.5235, 0.0168]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.3846, 0.0429, 0.8390,  ..., 0.4444, 0.4671, 0.5693])
+tensor([0.4657, 0.6211, 0.7331,  ..., 0.5211, 0.3283, 0.1551])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -375,378 +268,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.02031111717224121 seconds
+Time: 0.021244287490844727 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '51695', '-ss', '10000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.430596351623535}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '49425', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.291856288909912}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      8.4844e-01, 6.4685e-01, 8.1450e-03, 1.7539e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.8755, 0.4171, 0.4007,  ..., 0.3638, 0.0663, 0.0983])
+tensor([0.6861, 0.5695, 0.5465,  ..., 0.1082, 0.7995, 0.1510])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -754,271 +647,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 2.430596351623535 seconds
+Time: 2.291856288909912 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '223318', '-ss', '10000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.272708654403687}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '226437', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.510953187942505}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      1.1248e-01, 7.1314e-01, 9.2116e-01, 4.1416e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.5980, 0.9421, 0.9493,  ..., 0.6518, 0.5202, 0.4457])
+tensor([0.0845, 0.2230, 0.9209,  ..., 0.5627, 0.3340, 0.7945])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1026,268 +1026,375 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.272708654403687 seconds
+Time: 10.510953187942505 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      2.1437e-02, 8.8828e-01, 4.8335e-01, 7.4366e-01,
+                      9.8953e-01, 5.8257e-01, 2.9039e-04, 1.1123e-01,
+                      5.1846e-02, 1.3108e-01, 4.2323e-01, 6.5304e-01,
+                      1.8625e-01, 3.7199e-02, 7.0216e-01, 6.0287e-01,
+                      2.3789e-01, 9.2863e-01, 1.2348e-01, 8.6839e-01,
+                      4.9520e-01, 2.7288e-01, 4.4592e-01, 4.2396e-01,
+                      6.0645e-01, 6.2528e-01, 6.5293e-01, 9.2566e-01,
+                      9.4540e-01, 5.8610e-02, 8.7155e-01, 2.2119e-01,
+                      8.7605e-01, 8.8102e-01, 8.2781e-01, 3.8576e-01,
+                      6.3776e-01, 7.8203e-01, 1.0462e-02, 9.1986e-01,
+                      6.5027e-01, 2.7230e-01, 2.1864e-01, 2.5327e-01,
+                      9.8263e-01, 4.8835e-01, 8.8863e-01, 5.6728e-01,
+                      8.0559e-01, 1.2815e-01, 4.6205e-01, 8.5618e-01,
+                      1.8311e-01, 4.8907e-01, 6.4311e-01, 3.8609e-01,
+                      9.3749e-01, 8.9364e-01, 7.1836e-01, 3.0713e-01,
+                      5.5364e-01, 7.7259e-01, 1.0362e-01, 2.7281e-01,
+                      2.1067e-01, 8.0247e-01, 1.0462e-01, 6.2383e-03,
+                      4.1906e-01, 4.6448e-02, 8.4964e-01, 1.8122e-01,
+                      6.4569e-01, 6.6590e-01, 9.6978e-01, 5.3446e-01,
+                      9.2632e-02, 8.2326e-01, 2.3167e-02, 7.1511e-01,
+                      7.4454e-01, 8.3709e-01, 2.7395e-01, 3.9752e-01,
+                      2.3413e-01, 4.1262e-02, 8.1277e-01, 8.4946e-01,
+                      5.3420e-01, 2.8694e-01, 6.3996e-01, 8.6404e-01,
+                      7.7503e-01, 8.7692e-01, 4.2612e-01, 4.9620e-02,
+                      3.3084e-01, 8.5854e-01, 7.7131e-01, 1.9680e-01,
+                      1.6464e-01, 9.8493e-01, 6.7014e-01, 9.6494e-01,
+                      7.2374e-01, 5.8901e-01, 7.9710e-01, 6.1744e-01,
+                      9.8465e-01, 9.2253e-01, 3.8584e-01, 4.0856e-01,
+                      1.8180e-01, 8.8451e-01, 5.0687e-01, 7.6331e-01,
+                      2.9799e-01, 3.8883e-01, 9.8601e-01, 6.4816e-01,
+                      4.7480e-03, 2.3454e-01, 3.3701e-02, 6.5933e-01,
+                      5.6913e-01, 1.7944e-01, 7.1449e-01, 8.1288e-01,
+                      4.0565e-01, 1.1448e-01, 7.6631e-02, 5.9701e-01,
+                      9.9955e-01, 8.5047e-01, 6.2163e-01, 3.4651e-02,
+                      4.7043e-01, 6.7147e-01, 4.1853e-01, 5.6234e-01,
+                      4.9969e-01, 8.8040e-01, 9.8673e-01, 8.3060e-02,
+                      8.6189e-02, 9.1021e-01, 8.1437e-01, 9.1474e-01,
+                      1.0110e-01, 4.2937e-01, 6.7076e-02, 5.5100e-01,
+                      1.4978e-01, 2.3091e-01, 9.2119e-01, 8.8302e-01,
+                      7.1686e-01, 8.5039e-01, 6.3669e-01, 6.9416e-01,
+                      9.9660e-01, 4.4720e-01, 4.8955e-01, 2.9264e-01,
+                      1.2478e-01, 6.6834e-01, 1.5549e-01, 8.6643e-01,
+                      4.8584e-01, 6.2762e-01, 8.9681e-01, 9.9150e-01,
+                      8.9800e-01, 4.9195e-02, 3.4261e-02, 9.9473e-02,
+                      6.5421e-01, 3.1869e-02, 7.3417e-01, 7.2239e-01,
+                      5.2055e-01, 7.2939e-01, 4.0424e-01, 5.3910e-01,
+                      1.4767e-01, 2.2639e-01, 9.4374e-01, 7.8686e-01,
+                      1.2226e-02, 5.6413e-01, 5.1201e-01, 4.0050e-01,
+                      7.2622e-01, 7.4764e-01, 3.7669e-01, 4.7057e-01,
+                      3.6367e-02, 9.6347e-01, 1.0702e-01, 9.4801e-01,
+                      5.9118e-01, 1.3243e-01, 2.9751e-01, 3.7908e-01,
+                      6.3144e-01, 4.1336e-01, 4.4719e-01, 9.2700e-01,
+                      5.3549e-01, 6.8263e-01, 1.0998e-01, 7.2406e-01,
+                      8.0101e-01, 2.8276e-01, 5.0451e-01, 9.4162e-01,
+                      3.8205e-01, 3.9509e-01, 2.6665e-02, 9.7060e-01,
+                      1.1248e-01, 7.1314e-01, 9.2116e-01, 4.1416e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.5980, 0.9421, 0.9493,  ..., 0.6518, 0.5202, 0.4457])
+tensor([0.0845, 0.2230, 0.9209,  ..., 0.5627, 0.3340, 0.7945])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1295,13 +1402,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.272708654403687 seconds
+Time: 10.510953187942505 seconds
 
-[18.39, 17.85, 18.29, 17.9, 18.11, 17.8, 18.16, 22.08, 18.79, 18.14]
-[46.54]
-14.219237565994263
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 223318, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.272708654403687, 'TIME_S_1KI': 0.046000361163917314, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.7633163213729, 'W': 46.54}
-[18.39, 17.85, 18.29, 17.9, 18.11, 17.8, 18.16, 22.08, 18.79, 18.14, 20.06, 18.33, 18.25, 21.91, 18.14, 18.0, 18.29, 18.13, 17.88, 18.08]
-335.245
-16.76225
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 223318, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.272708654403687, 'TIME_S_1KI': 0.046000361163917314, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.7633163213729, 'W': 46.54, 'J_1KI': 2.963322778823798, 'W_1KI': 0.2084023679237679, 'W_D': 29.777749999999997, 'J_D': 423.41690143078563, 'W_D_1KI': 0.13334236380408207, 'J_D_1KI': 0.0005970963549919042}
+[19.17, 18.72, 18.85, 18.68, 18.89, 18.73, 18.67, 18.78, 18.6, 18.53]
+[52.58]
+14.262712955474854
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 226437, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.510953187942505, 'TIME_S_1KI': 0.046418885552902155, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 749.9334471988677, 'W': 52.58}
+[19.17, 18.72, 18.85, 18.68, 18.89, 18.73, 18.67, 18.78, 18.6, 18.53, 19.4, 18.65, 18.9, 18.42, 18.63, 18.51, 18.55, 18.66, 18.95, 18.74]
+337.11
+16.8555
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 226437, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.510953187942505, 'TIME_S_1KI': 0.046418885552902155, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 749.9334471988677, 'W': 52.58, 'J_1KI': 3.311885633526622, 'W_1KI': 0.23220586741566088, 'W_D': 35.7245, 'J_D': 509.5282889778614, 'W_D_1KI': 0.15776794428472377, 'J_D_1KI': 0.0006967410108980589}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.json
index d34e261..b1102d4 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 115566, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.362370014190674, "TIME_S_1KI": 0.08966625144238508, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 657.2445391845703, "W": 46.58, "J_1KI": 5.687179094063741, "W_1KI": 0.40305972344807295, "W_D": 30.2825, "J_D": 427.28655555725095, "W_D_1KI": 0.2620364120935223, "J_D_1KI": 0.002267417857272228}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 113233, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.205250024795532, "TIME_S_1KI": 0.09012611186487625, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 752.6565923953057, "W": 53.59, "J_1KI": 6.646972105263533, "W_1KI": 0.4732719260286313, "W_D": 22.6315, "J_D": 317.853100779891, "W_D_1KI": 0.1998666466489451, "J_D_1KI": 0.0017650918605790283}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.output
index 30badbd..407df54 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.024602413177490234}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.023785829544067383}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    2,    3,  ..., 4997, 4999, 5000]),
-       col_indices=tensor([5115, 9337, 5262,  ..., 1244, 4227, 2124]),
-       values=tensor([0.7036, 0.8839, 0.8989,  ..., 0.3409, 0.8377, 0.4572]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4997, 4997, 5000]),
+       col_indices=tensor([1263, 3281, 2385,  ..., 1647, 8857, 9729]),
+       values=tensor([0.1325, 0.5780, 0.0735,  ..., 0.4975, 0.1005, 0.9200]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.4155, 0.9580, 0.1653,  ..., 0.8843, 0.0512, 0.6581])
+tensor([0.9661, 0.4765, 0.8121,  ..., 0.6083, 0.6316, 0.2082])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 0.024602413177490234 seconds
+Time: 0.023785829544067383 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '42678', '-ss', '10000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.8775720596313477}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '44143', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.093339204788208}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 4998, 4999, 5000]),
-       col_indices=tensor([ 717, 6679, 2344,  ..., 3928, 4219, 6236]),
-       values=tensor([0.9595, 0.5891, 0.3421,  ..., 0.3760, 0.2961, 0.2336]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([4240, 5372, 9073,  ...,  536, 3650,  487]),
+       values=tensor([0.8815, 0.3828, 0.1359,  ..., 0.3328, 0.9511, 0.5438]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.6496, 0.5288, 0.3835,  ..., 0.2038, 0.3313, 0.3083])
+tensor([0.7920, 0.2578, 0.7925,  ..., 0.7391, 0.3319, 0.5267])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 3.8775720596313477 seconds
+Time: 4.093339204788208 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '115566', '-ss', '10000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.362370014190674}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '113233', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.205250024795532}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([6593, 2332, 3653,  ..., 6447, 6839, 4175]),
-       values=tensor([0.4277, 0.1691, 0.2657,  ..., 0.2731, 0.4419, 0.1553]),
+tensor(crow_indices=tensor([   0,    1,    3,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([1678, 6917, 7081,  ..., 5230, 7690, 5828]),
+       values=tensor([0.5732, 0.2259, 0.2631,  ..., 0.8737, 0.8035, 0.2694]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.3155, 0.6085, 0.7514,  ..., 0.0185, 0.1956, 0.3828])
+tensor([0.9981, 0.1612, 0.0478,  ..., 0.0903, 0.5916, 0.6765])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.362370014190674 seconds
+Time: 10.205250024795532 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([6593, 2332, 3653,  ..., 6447, 6839, 4175]),
-       values=tensor([0.4277, 0.1691, 0.2657,  ..., 0.2731, 0.4419, 0.1553]),
+tensor(crow_indices=tensor([   0,    1,    3,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([1678, 6917, 7081,  ..., 5230, 7690, 5828]),
+       values=tensor([0.5732, 0.2259, 0.2631,  ..., 0.8737, 0.8035, 0.2694]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.3155, 0.6085, 0.7514,  ..., 0.0185, 0.1956, 0.3828])
+tensor([0.9981, 0.1612, 0.0478,  ..., 0.0903, 0.5916, 0.6765])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.362370014190674 seconds
+Time: 10.205250024795532 seconds
 
-[18.44, 17.79, 18.13, 18.16, 18.19, 17.86, 18.01, 17.82, 18.05, 18.06]
-[46.58]
-14.110015869140625
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 115566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.362370014190674, 'TIME_S_1KI': 0.08966625144238508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 657.2445391845703, 'W': 46.58}
-[18.44, 17.79, 18.13, 18.16, 18.19, 17.86, 18.01, 17.82, 18.05, 18.06, 18.35, 17.92, 18.08, 18.77, 17.97, 18.23, 18.59, 17.91, 18.14, 17.81]
-325.95
-16.2975
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 115566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.362370014190674, 'TIME_S_1KI': 0.08966625144238508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 657.2445391845703, 'W': 46.58, 'J_1KI': 5.687179094063741, 'W_1KI': 0.40305972344807295, 'W_D': 30.2825, 'J_D': 427.28655555725095, 'W_D_1KI': 0.2620364120935223, 'J_D_1KI': 0.002267417857272228}
+[43.74, 52.65, 53.85, 53.25, 53.48, 53.65, 48.94, 44.74, 45.01, 44.67]
+[53.59]
+14.044720888137817
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 113233, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.205250024795532, 'TIME_S_1KI': 0.09012611186487625, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 752.6565923953057, 'W': 53.59}
+[43.74, 52.65, 53.85, 53.25, 53.48, 53.65, 48.94, 44.74, 45.01, 44.67, 18.93, 19.56, 18.83, 18.45, 18.72, 18.73, 19.01, 18.63, 18.56, 18.88]
+619.1700000000001
+30.958500000000004
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 113233, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.205250024795532, 'TIME_S_1KI': 0.09012611186487625, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 752.6565923953057, 'W': 53.59, 'J_1KI': 6.646972105263533, 'W_1KI': 0.4732719260286313, 'W_D': 22.6315, 'J_D': 317.853100779891, 'W_D_1KI': 0.1998666466489451, 'J_D_1KI': 0.0017650918605790283}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.json
index 5b0e976..bc2885e 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.447505474090576, "TIME_S_1KI": 154.47505474090576, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2621.8117760252953, "W": 44.61, "J_1KI": 26218.117760252953, "W_1KI": 446.1, "W_D": 28.050250000000002, "J_D": 1648.56480095166, "W_D_1KI": 280.50250000000005, "J_D_1KI": 2805.0250000000005}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 14.074536085128784, "TIME_S_1KI": 140.74536085128784, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1450.0492564201354, "W": 53.4, "J_1KI": 14500.492564201353, "W_1KI": 534.0, "W_D": 36.354, "J_D": 987.173982544899, "W_D_1KI": 363.53999999999996, "J_D_1KI": 3635.3999999999996}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.output
index 55f3dfa..09615f1 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.447505474090576}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 14.074536085128784}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       57,      102,  ..., 24999899,
-                            24999940, 25000000]),
-       col_indices=tensor([  5577,  28835,  47310,  ..., 481805, 486701,
-                           494412]),
-       values=tensor([0.4828, 0.8396, 0.7554,  ..., 0.0896, 0.7495, 0.8303]),
+tensor(crow_indices=tensor([       0,       60,      101,  ..., 24999901,
+                            24999948, 25000000]),
+       col_indices=tensor([  2074,   4285,  14752,  ..., 481790, 489574,
+                           493216]),
+       values=tensor([0.5532, 0.1710, 0.9566,  ..., 0.4584, 0.5329, 0.8738]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.9266, 0.8773, 0.4193,  ..., 0.0231, 0.6267, 0.1934])
+tensor([0.0537, 0.3552, 0.4509,  ..., 0.9134, 0.7144, 0.9089])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,17 +17,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 15.447505474090576 seconds
+Time: 14.074536085128784 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       57,      102,  ..., 24999899,
-                            24999940, 25000000]),
-       col_indices=tensor([  5577,  28835,  47310,  ..., 481805, 486701,
-                           494412]),
-       values=tensor([0.4828, 0.8396, 0.7554,  ..., 0.0896, 0.7495, 0.8303]),
+tensor(crow_indices=tensor([       0,       60,      101,  ..., 24999901,
+                            24999948, 25000000]),
+       col_indices=tensor([  2074,   4285,  14752,  ..., 481790, 489574,
+                           493216]),
+       values=tensor([0.5532, 0.1710, 0.9566,  ..., 0.4584, 0.5329, 0.8738]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.9266, 0.8773, 0.4193,  ..., 0.0231, 0.6267, 0.1934])
+tensor([0.0537, 0.3552, 0.4509,  ..., 0.9134, 0.7144, 0.9089])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -35,13 +35,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 15.447505474090576 seconds
+Time: 14.074536085128784 seconds
 
-[18.81, 17.9, 18.4, 17.9, 18.2, 17.84, 18.01, 22.17, 18.54, 18.17]
-[44.61]
-58.77183985710144
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.447505474090576, 'TIME_S_1KI': 154.47505474090576, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2621.8117760252953, 'W': 44.61}
-[18.81, 17.9, 18.4, 17.9, 18.2, 17.84, 18.01, 22.17, 18.54, 18.17, 18.44, 18.17, 17.99, 18.08, 18.12, 18.6, 18.11, 18.22, 18.18, 18.11]
-331.19499999999994
-16.559749999999998
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.447505474090576, 'TIME_S_1KI': 154.47505474090576, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2621.8117760252953, 'W': 44.61, 'J_1KI': 26218.117760252953, 'W_1KI': 446.1, 'W_D': 28.050250000000002, 'J_D': 1648.56480095166, 'W_D_1KI': 280.50250000000005, 'J_D_1KI': 2805.0250000000005}
+[19.02, 18.78, 18.66, 18.75, 18.74, 18.65, 18.64, 18.75, 18.66, 18.51]
+[53.4]
+27.154480457305908
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 14.074536085128784, 'TIME_S_1KI': 140.74536085128784, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1450.0492564201354, 'W': 53.4}
+[19.02, 18.78, 18.66, 18.75, 18.74, 18.65, 18.64, 18.75, 18.66, 18.51, 19.39, 19.96, 18.64, 18.39, 18.75, 18.41, 19.4, 18.74, 18.66, 23.76]
+340.92
+17.046
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 14.074536085128784, 'TIME_S_1KI': 140.74536085128784, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1450.0492564201354, 'W': 53.4, 'J_1KI': 14500.492564201353, 'W_1KI': 534.0, 'W_D': 36.354, 'J_D': 987.173982544899, 'W_D_1KI': 363.53999999999996, 'J_D_1KI': 3635.3999999999996}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json
index 8a43ad7..49fa8c0 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 751, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.154199123382568, "TIME_S_1KI": 13.520904292120598, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 705.406801700592, "W": 48.84, "J_1KI": 939.290015580016, "W_1KI": 65.03328894806924, "W_D": 32.38000000000001, "J_D": 467.67142176628124, "W_D_1KI": 43.115845539280976, "J_D_1KI": 57.41124572474165}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 774, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.392576932907104, "TIME_S_1KI": 13.427101980500135, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 815.1673431968688, "W": 54.76, "J_1KI": 1053.1877819080992, "W_1KI": 70.74935400516794, "W_D": 37.97175, "J_D": 565.2543930612802, "W_D_1KI": 49.05910852713178, "J_D_1KI": 63.38386114616508}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output
index 9480055..dda1d3d 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,36 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.3980276584625244}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.3549816608428955}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       7,      14,  ..., 2499987,
-                            2499996, 2500000]),
-       col_indices=tensor([ 56026, 195485, 327540,  ...,  74351, 467081,
-                           495492]),
-       values=tensor([0.8691, 0.8234, 0.1160,  ..., 0.0380, 0.6115, 0.8262]),
-       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8652, 0.4148, 0.1413,  ..., 0.7873, 0.1950, 0.8001])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 2500000
-Density: 1e-05
-Time: 1.3980276584625244 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '751', '-ss', '500000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.154199123382568}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       3,      11,  ..., 2499987,
+tensor(crow_indices=tensor([      0,       5,       9,  ..., 2499988,
                             2499993, 2500000]),
-       col_indices=tensor([159259, 352180, 455738,  ..., 361655, 368506,
-                           421546]),
-       values=tensor([0.7015, 0.3878, 0.3559,  ..., 0.2417, 0.3895, 0.7278]),
+       col_indices=tensor([ 26442, 175167, 250311,  ..., 373376, 376337,
+                           459483]),
+       values=tensor([0.5969, 0.1012, 0.1691,  ..., 0.9172, 0.2591, 0.6319]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4879, 0.7909, 0.7587,  ..., 0.1983, 0.9582, 0.5253])
+tensor([0.8308, 0.0173, 0.4266,  ..., 0.3319, 0.0201, 0.1301])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.154199123382568 seconds
+Time: 1.3549816608428955 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '774', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.392576932907104}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       3,      11,  ..., 2499987,
-                            2499993, 2500000]),
-       col_indices=tensor([159259, 352180, 455738,  ..., 361655, 368506,
-                           421546]),
-       values=tensor([0.7015, 0.3878, 0.3559,  ..., 0.2417, 0.3895, 0.7278]),
+tensor(crow_indices=tensor([      0,       6,      12,  ..., 2499991,
+                            2499997, 2500000]),
+       col_indices=tensor([ 71123, 409588, 458159,  ...,  75663, 128358,
+                           158973]),
+       values=tensor([0.2081, 0.8364, 0.6856,  ..., 0.2605, 0.4815, 0.1721]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4879, 0.7909, 0.7587,  ..., 0.1983, 0.9582, 0.5253])
+tensor([0.3363, 0.8875, 0.8268,  ..., 0.0761, 0.0201, 0.4445])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +38,31 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.154199123382568 seconds
+Time: 10.392576932907104 seconds
 
-[18.79, 17.69, 18.39, 21.58, 18.07, 18.02, 18.17, 17.87, 17.87, 18.03]
-[48.84]
-14.44321870803833
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 751, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.154199123382568, 'TIME_S_1KI': 13.520904292120598, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 705.406801700592, 'W': 48.84}
-[18.79, 17.69, 18.39, 21.58, 18.07, 18.02, 18.17, 17.87, 17.87, 18.03, 18.42, 17.82, 17.95, 18.61, 17.96, 18.22, 17.97, 18.42, 18.04, 17.86]
-329.19999999999993
-16.459999999999997
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 751, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.154199123382568, 'TIME_S_1KI': 13.520904292120598, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 705.406801700592, 'W': 48.84, 'J_1KI': 939.290015580016, 'W_1KI': 65.03328894806924, 'W_D': 32.38000000000001, 'J_D': 467.67142176628124, 'W_D_1KI': 43.115845539280976, 'J_D_1KI': 57.41124572474165}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      12,  ..., 2499991,
+                            2499997, 2500000]),
+       col_indices=tensor([ 71123, 409588, 458159,  ...,  75663, 128358,
+                           158973]),
+       values=tensor([0.2081, 0.8364, 0.6856,  ..., 0.2605, 0.4815, 0.1721]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3363, 0.8875, 0.8268,  ..., 0.0761, 0.0201, 0.4445])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.392576932907104 seconds
+
+[18.95, 18.4, 18.66, 18.8, 18.65, 18.6, 18.52, 18.81, 18.48, 18.43]
+[54.76]
+14.886182308197021
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 774, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.392576932907104, 'TIME_S_1KI': 13.427101980500135, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 815.1673431968688, 'W': 54.76}
+[18.95, 18.4, 18.66, 18.8, 18.65, 18.6, 18.52, 18.81, 18.48, 18.43, 19.08, 18.64, 18.64, 18.61, 18.83, 18.38, 18.69, 18.54, 18.85, 18.87]
+335.765
+16.788249999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 774, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.392576932907104, 'TIME_S_1KI': 13.427101980500135, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 815.1673431968688, 'W': 54.76, 'J_1KI': 1053.1877819080992, 'W_1KI': 70.74935400516794, 'W_D': 37.97175, 'J_D': 565.2543930612802, 'W_D_1KI': 49.05910852713178, 'J_D_1KI': 63.38386114616508}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.json
index e9ce521..d253e00 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 147, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.563251495361328, "TIME_S_1KI": 71.85885370994102, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 973.9529043245316, "W": 47.49, "J_1KI": 6625.529961391371, "W_1KI": 323.0612244897959, "W_D": 30.996250000000003, "J_D": 635.6893600898982, "W_D_1KI": 210.858843537415, "J_D_1KI": 1434.4139016150682}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 149, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.615598917007446, "TIME_S_1KI": 71.24563031548621, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1009.7270592689514, "W": 53.65, "J_1KI": 6776.691672945982, "W_1KI": 360.0671140939597, "W_D": 36.829, "J_D": 693.1451605930329, "W_D_1KI": 247.17449664429532, "J_D_1KI": 1658.8892392234584}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.output
index b78ba4c..c7eb0e4 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.09878396987915}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.029554843902588}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       30,       58,  ..., 12499941,
-                            12499962, 12500000]),
-       col_indices=tensor([  1470,   2567,   5271,  ..., 471166, 490246,
-                           499700]),
-       values=tensor([0.1668, 0.7788, 0.4321,  ..., 0.7966, 0.9450, 0.5105]),
+tensor(crow_indices=tensor([       0,       24,       43,  ..., 12499950,
+                            12499974, 12500000]),
+       col_indices=tensor([ 44813,  67635,  69778,  ..., 441122, 471109,
+                           483449]),
+       values=tensor([0.5651, 0.1070, 0.2363,  ..., 0.2654, 0.2204, 0.8535]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.9128, 0.0898, 0.7303,  ..., 0.7724, 0.8343, 0.8680])
+tensor([0.0975, 0.7325, 0.1851,  ..., 0.7815, 0.9825, 0.0299])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 7.09878396987915 seconds
+Time: 7.029554843902588 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '147', '-ss', '500000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.563251495361328}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '149', '-ss', '500000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.615598917007446}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       16,       39,  ..., 12499961,
-                            12499982, 12500000]),
-       col_indices=tensor([ 42415,  50722,  59820,  ..., 419133, 436999,
-                           480407]),
-       values=tensor([0.4848, 0.7890, 0.2846,  ..., 0.4428, 0.7066, 0.1150]),
+tensor(crow_indices=tensor([       0,       22,       50,  ..., 12499947,
+                            12499977, 12500000]),
+       col_indices=tensor([121511, 129611, 141383,  ..., 433293, 442071,
+                           465951]),
+       values=tensor([0.3869, 0.3126, 0.6203,  ..., 0.4955, 0.6399, 0.1679]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.7955, 0.8202, 0.0668,  ..., 0.2866, 0.4586, 0.4680])
+tensor([0.8747, 0.6262, 0.4850,  ..., 0.2052, 0.6103, 0.1532])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 10.563251495361328 seconds
+Time: 10.615598917007446 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       16,       39,  ..., 12499961,
-                            12499982, 12500000]),
-       col_indices=tensor([ 42415,  50722,  59820,  ..., 419133, 436999,
-                           480407]),
-       values=tensor([0.4848, 0.7890, 0.2846,  ..., 0.4428, 0.7066, 0.1150]),
+tensor(crow_indices=tensor([       0,       22,       50,  ..., 12499947,
+                            12499977, 12500000]),
+       col_indices=tensor([121511, 129611, 141383,  ..., 433293, 442071,
+                           465951]),
+       values=tensor([0.3869, 0.3126, 0.6203,  ..., 0.4955, 0.6399, 0.1679]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.7955, 0.8202, 0.0668,  ..., 0.2866, 0.4586, 0.4680])
+tensor([0.8747, 0.6262, 0.4850,  ..., 0.2052, 0.6103, 0.1532])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 10.563251495361328 seconds
+Time: 10.615598917007446 seconds
 
-[18.64, 21.68, 17.93, 17.89, 17.96, 18.08, 18.05, 17.94, 17.92, 17.72]
-[47.49]
-20.508589267730713
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.563251495361328, 'TIME_S_1KI': 71.85885370994102, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 973.9529043245316, 'W': 47.49}
-[18.64, 21.68, 17.93, 17.89, 17.96, 18.08, 18.05, 17.94, 17.92, 17.72, 18.36, 17.99, 18.51, 17.9, 18.05, 18.98, 18.56, 18.01, 18.0, 18.13]
-329.875
-16.49375
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.563251495361328, 'TIME_S_1KI': 71.85885370994102, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 973.9529043245316, 'W': 47.49, 'J_1KI': 6625.529961391371, 'W_1KI': 323.0612244897959, 'W_D': 30.996250000000003, 'J_D': 635.6893600898982, 'W_D_1KI': 210.858843537415, 'J_D_1KI': 1434.4139016150682}
+[18.79, 18.52, 19.14, 18.58, 18.65, 18.79, 18.51, 18.49, 18.45, 18.51]
+[53.65]
+18.820634841918945
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 149, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.615598917007446, 'TIME_S_1KI': 71.24563031548621, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1009.7270592689514, 'W': 53.65}
+[18.79, 18.52, 19.14, 18.58, 18.65, 18.79, 18.51, 18.49, 18.45, 18.51, 18.83, 18.43, 18.61, 18.61, 18.97, 18.85, 19.22, 18.59, 18.64, 18.61]
+336.41999999999996
+16.820999999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 149, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.615598917007446, 'TIME_S_1KI': 71.24563031548621, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1009.7270592689514, 'W': 53.65, 'J_1KI': 6776.691672945982, 'W_1KI': 360.0671140939597, 'W_D': 36.829, 'J_D': 693.1451605930329, 'W_D_1KI': 247.17449664429532, 'J_D_1KI': 1658.8892392234584}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json
index 4d243f2..0f29de9 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 9147, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.50059986114502, "TIME_S_1KI": 1.1479829300475588, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 681.5257016324997, "W": 47.63, "J_1KI": 74.50811212774677, "W_1KI": 5.207171750300645, "W_D": 31.135, "J_D": 445.50289146184923, "W_D_1KI": 3.4038482562588825, "J_D_1KI": 0.37212728285327235}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 9113, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.462905645370483, "TIME_S_1KI": 1.14812966590261, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 762.6086788606643, "W": 53.41, "J_1KI": 83.68360351812404, "W_1KI": 5.860858114781081, "W_D": 36.17825, "J_D": 516.5670742555857, "W_D_1KI": 3.9699604959947328, "J_D_1KI": 0.4356370565120962}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output
index 2f48a00..d2635eb 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.13143563270568848}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.13171839714050293}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,     12,  ..., 249990, 249995,
+tensor(crow_indices=tensor([     0,      8,     11,  ..., 249994, 249996,
                             250000]),
-       col_indices=tensor([33764,   781,  3609,  ..., 16676, 21435, 31146]),
-       values=tensor([0.7781, 0.6572, 0.9120,  ..., 0.8330, 0.7571, 0.7121]),
+       col_indices=tensor([  469, 11700, 29368,  ..., 13379, 15725, 40011]),
+       values=tensor([0.4755, 0.4561, 0.0643,  ..., 0.2814, 0.0236, 0.1702]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7934, 0.6307, 0.2590,  ..., 0.3547, 0.1547, 0.8460])
+tensor([0.1153, 0.8349, 0.0283,  ..., 0.5756, 0.1210, 0.6674])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 0.13143563270568848 seconds
+Time: 0.13171839714050293 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7988', '-ss', '50000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.168656826019287}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7971', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.183703422546387}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     10,     18,  ..., 249992, 249995,
+tensor(crow_indices=tensor([     0,      8,     16,  ..., 249992, 249996,
                             250000]),
-       col_indices=tensor([ 6728,  8437,  8523,  ..., 40465, 44043, 46138]),
-       values=tensor([0.4640, 0.7108, 0.7346,  ..., 0.1761, 0.2770, 0.7056]),
+       col_indices=tensor([18157, 19287, 25360,  ...,  8756, 10679, 25164]),
+       values=tensor([0.5500, 0.0351, 0.7744,  ..., 0.4439, 0.1084, 0.1584]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0571, 0.2747, 0.4590,  ..., 0.7273, 0.2570, 0.3128])
+tensor([0.8126, 0.7393, 0.2533,  ..., 0.1690, 0.4952, 0.2081])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 9.168656826019287 seconds
+Time: 9.183703422546387 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9147', '-ss', '50000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.50059986114502}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9113', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.462905645370483}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,     10,  ..., 249987, 249996,
+tensor(crow_indices=tensor([     0,      4,     12,  ..., 249992, 249995,
                             250000]),
-       col_indices=tensor([ 4811,  5188, 33226,  ..., 17568, 20020, 26384]),
-       values=tensor([0.5580, 0.6578, 0.5141,  ..., 0.8482, 0.1339, 0.2046]),
+       col_indices=tensor([  658,  8939, 19459,  ..., 31752, 38380, 43446]),
+       values=tensor([0.6807, 0.2742, 0.9335,  ..., 0.8451, 0.5422, 0.0945]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.2505, 0.9140, 0.1873,  ..., 0.2385, 0.4644, 0.1302])
+tensor([0.4836, 0.3449, 0.7596,  ..., 0.7679, 0.9836, 0.8519])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.50059986114502 seconds
+Time: 10.462905645370483 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,     10,  ..., 249987, 249996,
+tensor(crow_indices=tensor([     0,      4,     12,  ..., 249992, 249995,
                             250000]),
-       col_indices=tensor([ 4811,  5188, 33226,  ..., 17568, 20020, 26384]),
-       values=tensor([0.5580, 0.6578, 0.5141,  ..., 0.8482, 0.1339, 0.2046]),
+       col_indices=tensor([  658,  8939, 19459,  ..., 31752, 38380, 43446]),
+       values=tensor([0.6807, 0.2742, 0.9335,  ..., 0.8451, 0.5422, 0.0945]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.2505, 0.9140, 0.1873,  ..., 0.2385, 0.4644, 0.1302])
+tensor([0.4836, 0.3449, 0.7596,  ..., 0.7679, 0.9836, 0.8519])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.50059986114502 seconds
+Time: 10.462905645370483 seconds
 
-[18.33, 18.14, 21.59, 19.0, 18.11, 18.16, 17.98, 18.16, 18.03, 18.31]
-[47.63]
-14.308748722076416
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 9147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.50059986114502, 'TIME_S_1KI': 1.1479829300475588, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 681.5257016324997, 'W': 47.63}
-[18.33, 18.14, 21.59, 19.0, 18.11, 18.16, 17.98, 18.16, 18.03, 18.31, 18.43, 17.74, 17.82, 18.07, 18.59, 18.18, 17.93, 18.01, 17.93, 17.85]
-329.90000000000003
-16.495
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 9147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.50059986114502, 'TIME_S_1KI': 1.1479829300475588, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 681.5257016324997, 'W': 47.63, 'J_1KI': 74.50811212774677, 'W_1KI': 5.207171750300645, 'W_D': 31.135, 'J_D': 445.50289146184923, 'W_D_1KI': 3.4038482562588825, 'J_D_1KI': 0.37212728285327235}
+[19.09, 18.91, 19.2, 18.56, 18.6, 18.52, 18.84, 18.77, 18.58, 22.57]
+[53.41]
+14.278387546539307
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 9113, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.462905645370483, 'TIME_S_1KI': 1.14812966590261, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 762.6086788606643, 'W': 53.41}
+[19.09, 18.91, 19.2, 18.56, 18.6, 18.52, 18.84, 18.77, 18.58, 22.57, 19.29, 18.45, 19.43, 18.57, 19.96, 22.08, 18.58, 18.73, 19.2, 18.36]
+344.635
+17.231749999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 9113, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.462905645370483, 'TIME_S_1KI': 1.14812966590261, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 762.6086788606643, 'W': 53.41, 'J_1KI': 83.68360351812404, 'W_1KI': 5.860858114781081, 'W_D': 36.17825, 'J_D': 516.5670742555857, 'W_D_1KI': 3.9699604959947328, 'J_D_1KI': 0.4356370565120962}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json
index db8a72e..61f8eec 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1997, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.44909930229187, "TIME_S_1KI": 5.232398248518714, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 733.6282246422767, "W": 48.73, "J_1KI": 367.3651600612302, "W_1KI": 24.40160240360541, "W_D": 32.179249999999996, "J_D": 484.4573373244404, "W_D_1KI": 16.113795693540307, "J_D_1KI": 8.069001348793345}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1979, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.479721784591675, "TIME_S_1KI": 5.295463256488972, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 828.9978633999824, "W": 55.01, "J_1KI": 418.8973539161104, "W_1KI": 27.79686710459828, "W_D": 37.86475, "J_D": 570.6198299977184, "W_D_1KI": 19.133274381000504, "J_D_1KI": 9.668152794846137}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output
index a863f3a..2510e91 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.5546464920043945}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.5715610980987549}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      55,     117,  ..., 2499903,
-                            2499953, 2500000]),
-       col_indices=tensor([  566,  1603,  2858,  ..., 47622, 48780, 49985]),
-       values=tensor([0.9915, 0.7849, 0.7900,  ..., 0.9170, 0.4625, 0.4875]),
+tensor(crow_indices=tensor([      0,      43,      95,  ..., 2499900,
+                            2499947, 2500000]),
+       col_indices=tensor([  440,   592,  3640,  ..., 47603, 48117, 49754]),
+       values=tensor([0.1661, 0.7674, 0.7986,  ..., 0.7717, 0.8953, 0.0926]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6866, 0.0421, 0.1446,  ..., 0.6566, 0.6603, 0.7026])
+tensor([0.4593, 0.0804, 0.6079,  ..., 0.0764, 0.7492, 0.9367])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,39 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 0.5546464920043945 seconds
+Time: 0.5715610980987549 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1893', '-ss', '50000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.948294878005981}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1837', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.74488639831543}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      33,      68,  ..., 2499891,
-                            2499938, 2500000]),
-       col_indices=tensor([ 3534,  3824,  4376,  ..., 49368, 49484, 49571]),
-       values=tensor([0.2824, 0.5783, 0.2215,  ..., 0.8826, 0.1249, 0.2741]),
-       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5004, 0.8496, 0.6985,  ..., 0.2602, 0.0299, 0.5346])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 2500000
-Density: 0.001
-Time: 9.948294878005981 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1997', '-ss', '50000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.44909930229187}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      60,     107,  ..., 2499904,
+tensor(crow_indices=tensor([      0,      51,     106,  ..., 2499900,
                             2499948, 2500000]),
-       col_indices=tensor([ 1519,  4331,  6515,  ..., 42103, 42230, 49135]),
-       values=tensor([0.6099, 0.9393, 0.8647,  ..., 0.8575, 0.6331, 0.4704]),
+       col_indices=tensor([  108,   457,   651,  ..., 47686, 48796, 49691]),
+       values=tensor([0.2349, 0.1510, 0.8409,  ..., 0.7616, 0.6600, 0.1731]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1131, 0.9150, 0.5556,  ..., 0.6033, 0.7715, 0.6124])
+tensor([0.3877, 0.3672, 0.7824,  ..., 0.6258, 0.7734, 0.2600])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.44909930229187 seconds
+Time: 9.74488639831543 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1979', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.479721784591675}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      60,     107,  ..., 2499904,
-                            2499948, 2500000]),
-       col_indices=tensor([ 1519,  4331,  6515,  ..., 42103, 42230, 49135]),
-       values=tensor([0.6099, 0.9393, 0.8647,  ..., 0.8575, 0.6331, 0.4704]),
+tensor(crow_indices=tensor([      0,      59,     111,  ..., 2499895,
+                            2499957, 2500000]),
+       col_indices=tensor([ 1818,  1883,  2319,  ..., 46626, 47772, 49582]),
+       values=tensor([0.6336, 0.5989, 0.4201,  ..., 0.3558, 0.1630, 0.4148]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1131, 0.9150, 0.5556,  ..., 0.6033, 0.7715, 0.6124])
+tensor([0.7936, 0.0330, 0.0768,  ..., 0.8085, 0.2404, 0.8732])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +56,30 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.44909930229187 seconds
+Time: 10.479721784591675 seconds
 
-[18.64, 18.18, 18.44, 22.12, 18.9, 18.07, 17.86, 17.86, 18.53, 18.0]
-[48.73]
-15.054960489273071
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1997, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.44909930229187, 'TIME_S_1KI': 5.232398248518714, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 733.6282246422767, 'W': 48.73}
-[18.64, 18.18, 18.44, 22.12, 18.9, 18.07, 17.86, 17.86, 18.53, 18.0, 18.34, 18.09, 18.08, 17.94, 18.14, 18.44, 18.04, 17.91, 17.9, 18.05]
-331.015
-16.55075
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1997, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.44909930229187, 'TIME_S_1KI': 5.232398248518714, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 733.6282246422767, 'W': 48.73, 'J_1KI': 367.3651600612302, 'W_1KI': 24.40160240360541, 'W_D': 32.179249999999996, 'J_D': 484.4573373244404, 'W_D_1KI': 16.113795693540307, 'J_D_1KI': 8.069001348793345}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      59,     111,  ..., 2499895,
+                            2499957, 2500000]),
+       col_indices=tensor([ 1818,  1883,  2319,  ..., 46626, 47772, 49582]),
+       values=tensor([0.6336, 0.5989, 0.4201,  ..., 0.3558, 0.1630, 0.4148]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7936, 0.0330, 0.0768,  ..., 0.8085, 0.2404, 0.8732])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.479721784591675 seconds
+
+[19.91, 18.84, 18.94, 19.94, 18.54, 18.89, 18.6, 18.5, 18.83, 18.88]
+[55.01]
+15.069948434829712
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1979, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.479721784591675, 'TIME_S_1KI': 5.295463256488972, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 828.9978633999824, 'W': 55.01}
+[19.91, 18.84, 18.94, 19.94, 18.54, 18.89, 18.6, 18.5, 18.83, 18.88, 18.92, 18.57, 18.74, 18.6, 18.52, 22.63, 19.15, 18.43, 19.1, 18.46]
+342.90500000000003
+17.14525
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1979, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.479721784591675, 'TIME_S_1KI': 5.295463256488972, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 828.9978633999824, 'W': 55.01, 'J_1KI': 418.8973539161104, 'W_1KI': 27.79686710459828, 'W_D': 37.86475, 'J_D': 570.6198299977184, 'W_D_1KI': 19.133274381000504, 'J_D_1KI': 9.668152794846137}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.json
index 981b4ab..e6a8a4b 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 134, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.514585256576538, "TIME_S_1KI": 78.46705415355626, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2494.187549471855, "W": 43.5, "J_1KI": 18613.339921431754, "W_1KI": 324.6268656716418, "W_D": 26.962, "J_D": 1545.9375795140265, "W_D_1KI": 201.2089552238806, "J_D_1KI": 1501.5593673423925}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 152, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.39022421836853, "TIME_S_1KI": 68.35673827874034, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1277.9351173400878, "W": 53.72, "J_1KI": 8407.467877237419, "W_1KI": 353.4210526315789, "W_D": 36.683499999999995, "J_D": 872.6569783496856, "W_D_1KI": 241.33881578947367, "J_D_1KI": 1587.755367036011}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.output
index 7744b5c..9df8811 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.827495813369751}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 6.902969121932983}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      509,     1029,  ..., 24999021,
-                            24999525, 25000000]),
-       col_indices=tensor([   77,   168,   174,  ..., 49716, 49743, 49917]),
-       values=tensor([0.0871, 0.3865, 0.3717,  ..., 0.4376, 0.0483, 0.0994]),
+tensor(crow_indices=tensor([       0,      509,     1046,  ..., 24998991,
+                            24999499, 25000000]),
+       col_indices=tensor([  275,   623,   640,  ..., 49748, 49845, 49925]),
+       values=tensor([0.4383, 0.1606, 0.4979,  ..., 0.8937, 0.3916, 0.2299]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.1988, 0.8388, 0.2584,  ..., 0.5965, 0.5005, 0.7795])
+tensor([0.4910, 0.4324, 0.6781,  ..., 0.4435, 0.1381, 0.0974])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 7.827495813369751 seconds
+Time: 6.902969121932983 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '134', '-ss', '50000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.514585256576538}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '152', '-ss', '50000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.39022421836853}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      525,     1042,  ..., 24999041,
-                            24999512, 25000000]),
-       col_indices=tensor([  163,   320,   387,  ..., 49821, 49828, 49920]),
-       values=tensor([0.8765, 0.3303, 0.5777,  ..., 0.2129, 0.9852, 0.0873]),
+tensor(crow_indices=tensor([       0,      516,     1047,  ..., 24999044,
+                            24999539, 25000000]),
+       col_indices=tensor([  752,   778,   926,  ..., 49813, 49898, 49988]),
+       values=tensor([0.8686, 0.1586, 0.7679,  ..., 0.0255, 0.0592, 0.0678]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.2121, 0.5034, 0.7106,  ..., 0.6677, 0.7232, 0.0645])
+tensor([0.1218, 0.2324, 0.7166,  ..., 0.4698, 0.1352, 0.0532])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,16 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 10.514585256576538 seconds
+Time: 10.39022421836853 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      525,     1042,  ..., 24999041,
-                            24999512, 25000000]),
-       col_indices=tensor([  163,   320,   387,  ..., 49821, 49828, 49920]),
-       values=tensor([0.8765, 0.3303, 0.5777,  ..., 0.2129, 0.9852, 0.0873]),
+tensor(crow_indices=tensor([       0,      516,     1047,  ..., 24999044,
+                            24999539, 25000000]),
+       col_indices=tensor([  752,   778,   926,  ..., 49813, 49898, 49988]),
+       values=tensor([0.8686, 0.1586, 0.7679,  ..., 0.0255, 0.0592, 0.0678]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.2121, 0.5034, 0.7106,  ..., 0.6677, 0.7232, 0.0645])
+tensor([0.1218, 0.2324, 0.7166,  ..., 0.4698, 0.1352, 0.0532])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,13 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 10.514585256576538 seconds
+Time: 10.39022421836853 seconds
 
-[18.5, 18.14, 19.17, 17.93, 18.04, 17.95, 18.11, 18.93, 18.07, 18.37]
-[43.5]
-57.337644815444946
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 134, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.514585256576538, 'TIME_S_1KI': 78.46705415355626, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2494.187549471855, 'W': 43.5}
-[18.5, 18.14, 19.17, 17.93, 18.04, 17.95, 18.11, 18.93, 18.07, 18.37, 22.44, 18.33, 18.23, 18.16, 18.38, 17.89, 18.61, 17.96, 18.23, 17.95]
-330.76
-16.538
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 134, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.514585256576538, 'TIME_S_1KI': 78.46705415355626, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2494.187549471855, 'W': 43.5, 'J_1KI': 18613.339921431754, 'W_1KI': 324.6268656716418, 'W_D': 26.962, 'J_D': 1545.9375795140265, 'W_D_1KI': 201.2089552238806, 'J_D_1KI': 1501.5593673423925}
+[18.92, 22.47, 19.1, 18.56, 19.07, 18.37, 19.4, 18.4, 18.48, 18.45]
+[53.72]
+23.788814544677734
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 152, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.39022421836853, 'TIME_S_1KI': 68.35673827874034, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1277.9351173400878, 'W': 53.72}
+[18.92, 22.47, 19.1, 18.56, 19.07, 18.37, 19.4, 18.4, 18.48, 18.45, 18.92, 18.45, 18.96, 18.55, 18.64, 18.41, 18.69, 18.85, 18.72, 18.93]
+340.73
+17.0365
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 152, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.39022421836853, 'TIME_S_1KI': 68.35673827874034, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1277.9351173400878, 'W': 53.72, 'J_1KI': 8407.467877237419, 'W_1KI': 353.4210526315789, 'W_D': 36.683499999999995, 'J_D': 872.6569783496856, 'W_D_1KI': 241.33881578947367, 'J_D_1KI': 1587.755367036011}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.05.json
new file mode 100644
index 0000000..271cf75
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 34.656867027282715, "TIME_S_1KI": 346.56867027282715, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 4525.48052918911, "W": 53.5, "J_1KI": 45254.8052918911, "W_1KI": 535.0, "W_D": 36.41225, "J_D": 3080.0547364292743, "W_D_1KI": 364.1225, "J_D_1KI": 3641.225}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.05.output
new file mode 100644
index 0000000..8952b7e
--- /dev/null
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_0.05.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 34.656867027282715}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2537,      5021,  ...,
+                            124995008, 124997510, 125000000]),
+       col_indices=tensor([    5,    11,    12,  ..., 49956, 49966, 49969]),
+       values=tensor([0.1015, 0.8493, 0.5638,  ..., 0.6405, 0.0202, 0.3838]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.9316, 0.6211, 0.3946,  ..., 0.1857, 0.3082, 0.5923])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 34.656867027282715 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2537,      5021,  ...,
+                            124995008, 124997510, 125000000]),
+       col_indices=tensor([    5,    11,    12,  ..., 49956, 49966, 49969]),
+       values=tensor([0.1015, 0.8493, 0.5638,  ..., 0.6405, 0.0202, 0.3838]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.9316, 0.6211, 0.3946,  ..., 0.1857, 0.3082, 0.5923])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 34.656867027282715 seconds
+
+[19.53, 18.65, 23.68, 18.84, 18.77, 19.19, 18.64, 18.44, 18.65, 18.73]
+[53.5]
+84.5884211063385
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 34.656867027282715, 'TIME_S_1KI': 346.56867027282715, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4525.48052918911, 'W': 53.5}
+[19.53, 18.65, 23.68, 18.84, 18.77, 19.19, 18.64, 18.44, 18.65, 18.73, 19.15, 18.5, 18.6, 18.49, 18.91, 18.59, 18.51, 18.49, 18.82, 18.56]
+341.755
+17.08775
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 34.656867027282715, 'TIME_S_1KI': 346.56867027282715, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 4525.48052918911, 'W': 53.5, 'J_1KI': 45254.8052918911, 'W_1KI': 535.0, 'W_D': 36.41225, 'J_D': 3080.0547364292743, 'W_D_1KI': 364.1225, 'J_D_1KI': 3641.225}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json
index 01c9d2b..d393b6d 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 22242, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.517975091934204, "TIME_S_1KI": 0.472888008809199, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 668.1220734119415, "W": 47.0, "J_1KI": 30.03875880819807, "W_1KI": 2.113119323801816, "W_D": 30.704, "J_D": 436.4685136604309, "W_D_1KI": 1.3804513982555526, "J_D_1KI": 0.062065075004745646}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 22192, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.510575294494629, "TIME_S_1KI": 0.47362001146785454, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 756.9577831959724, "W": 52.91, "J_1KI": 34.10948914906149, "W_1KI": 2.384192501802451, "W_D": 36.012249999999995, "J_D": 515.2098455471396, "W_D_1KI": 1.6227582011535686, "J_D_1KI": 0.0731235671031709}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output
index f23093c..a80afda 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,32 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.06578350067138672}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.07807016372680664}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 24997, 24997, 25000]),
+       col_indices=tensor([33411, 44594, 10023,  ..., 13012, 22802, 46143]),
+       values=tensor([0.8310, 0.0198, 0.3050,  ..., 0.9692, 0.0759, 0.4781]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.4739, 0.7206, 0.8157,  ..., 0.6111, 0.0255, 0.2691])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.07807016372680664 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '13449', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.363297462463379}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([16918,  1143,  1724,  ..., 48553, 41363, 39308]),
-       values=tensor([0.9238, 0.1195, 0.2813,  ..., 0.9276, 0.6113, 0.0798]),
+       col_indices=tensor([47102, 32886, 19412,  ...,  8432,  6167, 30462]),
+       values=tensor([0.3403, 0.1048, 0.7701,  ..., 0.0994, 0.7400, 0.5573]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0373, 0.8428, 0.6841,  ..., 0.3333, 0.7324, 0.6824])
+tensor([0.6350, 0.1598, 0.4068,  ..., 0.6399, 0.8986, 0.2435])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,18 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.06578350067138672 seconds
+Time: 6.363297462463379 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '15961', '-ss', '50000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.534844875335693}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22192', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.510575294494629}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     2,  ..., 24999, 25000, 25000]),
-       col_indices=tensor([29190, 43986, 25006,  ..., 44362, 15421,  8070]),
-       values=tensor([0.3395, 0.8970, 0.1159,  ..., 0.8275, 0.4942, 0.3559]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([17655,  8136,  6685,  ...,  2023, 30575,  7570]),
+       values=tensor([0.7615, 0.3642, 0.5154,  ..., 0.9021, 0.3033, 0.6664]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7532, 0.7737, 0.3401,  ..., 0.4031, 0.1788, 0.5939])
+tensor([0.2301, 0.7680, 0.7694,  ..., 0.7461, 0.0620, 0.5948])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,18 +53,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 7.534844875335693 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22242', '-ss', '50000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.517975091934204}
+Time: 10.510575294494629 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([22981, 23025, 47875,  ..., 28752, 43497,  8642]),
-       values=tensor([0.4359, 0.4232, 0.6349,  ..., 0.3036, 0.5759, 0.5327]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([17655,  8136,  6685,  ...,  2023, 30575,  7570]),
+       values=tensor([0.7615, 0.3642, 0.5154,  ..., 0.9021, 0.3033, 0.6664]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8882, 0.3621, 0.9798,  ..., 0.1733, 0.2748, 0.2728])
+tensor([0.2301, 0.7680, 0.7694,  ..., 0.7461, 0.0620, 0.5948])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,29 +69,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.517975091934204 seconds
+Time: 10.510575294494629 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([22981, 23025, 47875,  ..., 28752, 43497,  8642]),
-       values=tensor([0.4359, 0.4232, 0.6349,  ..., 0.3036, 0.5759, 0.5327]),
-       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8882, 0.3621, 0.9798,  ..., 0.1733, 0.2748, 0.2728])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 25000
-Density: 1e-05
-Time: 10.517975091934204 seconds
-
-[18.31, 17.89, 18.04, 17.91, 18.19, 18.28, 17.95, 17.98, 18.92, 18.1]
-[47.0]
-14.215363264083862
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 22242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.517975091934204, 'TIME_S_1KI': 0.472888008809199, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 668.1220734119415, 'W': 47.0}
-[18.31, 17.89, 18.04, 17.91, 18.19, 18.28, 17.95, 17.98, 18.92, 18.1, 18.24, 18.64, 18.01, 17.83, 18.02, 18.06, 18.04, 17.88, 17.99, 17.93]
-325.92
-16.296
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 22242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.517975091934204, 'TIME_S_1KI': 0.472888008809199, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 668.1220734119415, 'W': 47.0, 'J_1KI': 30.03875880819807, 'W_1KI': 2.113119323801816, 'W_D': 30.704, 'J_D': 436.4685136604309, 'W_D_1KI': 1.3804513982555526, 'J_D_1KI': 0.062065075004745646}
+[24.17, 19.15, 18.5, 19.16, 18.56, 18.65, 18.51, 18.3, 18.41, 18.37]
+[52.91]
+14.306516408920288
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 22192, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.510575294494629, 'TIME_S_1KI': 0.47362001146785454, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 756.9577831959724, 'W': 52.91}
+[24.17, 19.15, 18.5, 19.16, 18.56, 18.65, 18.51, 18.3, 18.41, 18.37, 18.8, 18.64, 18.89, 18.42, 18.48, 18.3, 18.84, 18.69, 18.57, 18.43]
+337.955
+16.89775
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 22192, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.510575294494629, 'TIME_S_1KI': 0.47362001146785454, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 756.9577831959724, 'W': 52.91, 'J_1KI': 34.10948914906149, 'W_1KI': 2.384192501802451, 'W_D': 36.012249999999995, 'J_D': 515.2098455471396, 'W_D_1KI': 1.6227582011535686, 'J_D_1KI': 0.0731235671031709}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.json
index 7de68b9..ce7aa5d 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 11256, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.481152534484863, "TIME_S_1KI": 0.9311613836607022, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 671.3460792446136, "W": 47.13, "J_1KI": 59.64339723210853, "W_1KI": 4.187100213219616, "W_D": 30.78675, "J_D": 438.54368566060066, "W_D_1KI": 2.735141257995736, "J_D_1KI": 0.24299407053977753}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 11204, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.484663248062134, "TIME_S_1KI": 0.9357964341362134, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 758.3656199932099, "W": 52.9, "J_1KI": 67.68704212720544, "W_1KI": 4.721528025705106, "W_D": 35.66575, "J_D": 511.2982724248171, "W_D_1KI": 3.183305069617994, "J_D_1KI": 0.28412219471777883}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.output
index d1f10c6..f64ec3b 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.1096796989440918}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.11034727096557617}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      2,  ..., 124996, 124997,
+tensor(crow_indices=tensor([     0,      1,      4,  ..., 124991, 124997,
                             125000]),
-       col_indices=tensor([38708, 28625, 11454,  ...,   884, 22723, 30800]),
-       values=tensor([0.0038, 0.3289, 0.1581,  ..., 0.9719, 0.8303, 0.9998]),
+       col_indices=tensor([11731, 21946, 36023,  ..., 38897, 41892, 44011]),
+       values=tensor([0.5067, 0.6689, 0.8013,  ..., 0.5225, 0.7710, 0.9378]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.0207, 0.8237, 0.8176,  ..., 0.3561, 0.4550, 0.3366])
+tensor([0.8519, 0.4822, 0.7850,  ..., 0.3577, 0.9381, 0.9299])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 0.1096796989440918 seconds
+Time: 0.11034727096557617 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9573', '-ss', '50000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.929835081100464}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9515', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.917027235031128}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      4,  ..., 124997, 124998,
+tensor(crow_indices=tensor([     0,      2,      2,  ..., 124999, 125000,
                             125000]),
-       col_indices=tensor([ 5176, 42593, 37500,  ...,  3219, 12793, 38658]),
-       values=tensor([0.6803, 0.7664, 0.9859,  ..., 0.5422, 0.9603, 0.0980]),
+       col_indices=tensor([  795,  9459,  9721,  ..., 26673, 30658, 44375]),
+       values=tensor([0.5955, 0.9891, 0.5779,  ..., 0.4320, 0.5377, 0.0329]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.2299, 0.8541, 0.6200,  ..., 0.4981, 0.6521, 0.8502])
+tensor([0.7199, 0.8944, 0.9457,  ..., 0.0963, 0.7528, 0.5298])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 8.929835081100464 seconds
+Time: 8.917027235031128 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '11256', '-ss', '50000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.481152534484863}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '11204', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.484663248062134}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      7,  ..., 124996, 124999,
+tensor(crow_indices=tensor([     0,      4,      5,  ..., 124990, 124995,
                             125000]),
-       col_indices=tensor([ 3517, 32781, 39284,  ..., 16837, 28625, 12663]),
-       values=tensor([0.4051, 0.3118, 0.0683,  ..., 0.4752, 0.1421, 0.6822]),
+       col_indices=tensor([ 7117, 22714, 28697,  ..., 16609, 18473, 31107]),
+       values=tensor([0.4912, 0.1932, 0.7634,  ..., 0.8547, 0.4308, 0.8265]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.2287, 0.1097, 0.9835,  ..., 0.8729, 0.0701, 0.5217])
+tensor([0.3359, 0.4773, 0.2129,  ..., 0.6953, 0.5472, 0.3647])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.481152534484863 seconds
+Time: 10.484663248062134 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      7,  ..., 124996, 124999,
+tensor(crow_indices=tensor([     0,      4,      5,  ..., 124990, 124995,
                             125000]),
-       col_indices=tensor([ 3517, 32781, 39284,  ..., 16837, 28625, 12663]),
-       values=tensor([0.4051, 0.3118, 0.0683,  ..., 0.4752, 0.1421, 0.6822]),
+       col_indices=tensor([ 7117, 22714, 28697,  ..., 16609, 18473, 31107]),
+       values=tensor([0.4912, 0.1932, 0.7634,  ..., 0.8547, 0.4308, 0.8265]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.2287, 0.1097, 0.9835,  ..., 0.8729, 0.0701, 0.5217])
+tensor([0.3359, 0.4773, 0.2129,  ..., 0.6953, 0.5472, 0.3647])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.481152534484863 seconds
+Time: 10.484663248062134 seconds
 
-[18.46, 17.73, 18.38, 18.1, 18.29, 18.18, 18.04, 18.01, 18.16, 17.9]
-[47.13]
-14.244559288024902
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 11256, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.481152534484863, 'TIME_S_1KI': 0.9311613836607022, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 671.3460792446136, 'W': 47.13}
-[18.46, 17.73, 18.38, 18.1, 18.29, 18.18, 18.04, 18.01, 18.16, 17.9, 18.94, 17.91, 17.96, 17.99, 18.41, 18.07, 18.2, 17.95, 18.46, 18.75]
-326.865
-16.34325
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 11256, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.481152534484863, 'TIME_S_1KI': 0.9311613836607022, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 671.3460792446136, 'W': 47.13, 'J_1KI': 59.64339723210853, 'W_1KI': 4.187100213219616, 'W_D': 30.78675, 'J_D': 438.54368566060066, 'W_D_1KI': 2.735141257995736, 'J_D_1KI': 0.24299407053977753}
+[19.03, 18.85, 18.61, 18.49, 23.35, 19.32, 18.78, 19.05, 18.99, 18.44]
+[52.9]
+14.33583402633667
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 11204, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.484663248062134, 'TIME_S_1KI': 0.9357964341362134, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 758.3656199932099, 'W': 52.9}
+[19.03, 18.85, 18.61, 18.49, 23.35, 19.32, 18.78, 19.05, 18.99, 18.44, 23.11, 19.31, 18.54, 18.97, 18.59, 18.59, 18.45, 18.56, 18.61, 18.67]
+344.685
+17.23425
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 11204, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.484663248062134, 'TIME_S_1KI': 0.9357964341362134, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 758.3656199932099, 'W': 52.9, 'J_1KI': 67.68704212720544, 'W_1KI': 4.721528025705106, 'W_D': 35.66575, 'J_D': 511.2982724248171, 'W_D_1KI': 3.183305069617994, 'J_D_1KI': 0.28412219471777883}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.json
index b6b292e..a276d01 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 220904, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.611992359161377, "TIME_S_1KI": 0.04803893256419701, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 660.6772035217285, "W": 46.78, "J_1KI": 2.990788774860249, "W_1KI": 0.211766197081085, "W_D": 30.2935, "J_D": 427.83721387100223, "W_D_1KI": 0.13713423025386592, "J_D_1KI": 0.0006207865419090009}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 220871, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.55235505104065, "TIME_S_1KI": 0.047776100307603306, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 742.3232311749458, "W": 52.61, "J_1KI": 3.3608904345746873, "W_1KI": 0.2381933345708581, "W_D": 35.704499999999996, "J_D": 503.7878693686723, "W_D_1KI": 0.1616531821742103, "J_D_1KI": 0.000731889574340725}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.output
index bcbbe28..6023764 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.021222829818725586}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.020276308059692383}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    2,  ..., 2499, 2499, 2500]),
-       col_indices=tensor([2865, 4172, 3505,  ..., 1471, 1829, 2284]),
-       values=tensor([0.9472, 0.7106, 0.6508,  ..., 0.6327, 0.4564, 0.0632]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([2705, 4386, 4587,  ..., 1742, 2489,  353]),
+       values=tensor([0.7012, 0.7471, 0.5716,  ..., 0.4505, 0.8006, 0.6036]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.0811, 0.3767, 0.7595,  ..., 0.7571, 0.6856, 0.3676])
+tensor([0.9629, 0.3143, 0.3893,  ..., 0.8901, 0.2021, 0.9990])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.021222829818725586 seconds
+Time: 0.020276308059692383 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '49475', '-ss', '5000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.351640462875366}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '51784', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.46175479888916}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 2499, 2499, 2500]),
-       col_indices=tensor([1365, 3411,  751,  ..., 1715, 4182, 3544]),
-       values=tensor([0.2168, 0.4073, 0.8209,  ..., 0.1504, 0.5765, 0.8829]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([1585,  227, 3606,  ..., 3508, 1030, 4424]),
+       values=tensor([0.0799, 0.3568, 0.0076,  ..., 0.5931, 0.1628, 0.2374]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.3766, 0.6993, 0.2098,  ..., 0.7754, 0.7068, 0.6832])
+tensor([0.3993, 0.7902, 0.0512,  ..., 0.6094, 0.0083, 0.1392])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 2.351640462875366 seconds
+Time: 2.46175479888916 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '220904', '-ss', '5000', '-sd', '0.0001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.611992359161377}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '220871', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.55235505104065}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2499, 2500]),
-       col_indices=tensor([ 469, 3066, 4238,  ..., 2570, 4418, 4413]),
-       values=tensor([0.6778, 0.7938, 0.7053,  ..., 0.2703, 0.2957, 0.5133]),
+tensor(crow_indices=tensor([   0,    2,    2,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([   3, 4896, 3718,  ..., 1484,  995, 1337]),
+       values=tensor([0.4186, 0.1762, 0.3830,  ..., 0.8696, 0.5262, 0.8434]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.1744, 0.7493, 0.4982,  ..., 0.1073, 0.6650, 0.9357])
+tensor([0.0085, 0.7795, 0.4163,  ..., 0.6854, 0.9665, 0.8065])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.611992359161377 seconds
+Time: 10.55235505104065 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2499, 2500]),
-       col_indices=tensor([ 469, 3066, 4238,  ..., 2570, 4418, 4413]),
-       values=tensor([0.6778, 0.7938, 0.7053,  ..., 0.2703, 0.2957, 0.5133]),
+tensor(crow_indices=tensor([   0,    2,    2,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([   3, 4896, 3718,  ..., 1484,  995, 1337]),
+       values=tensor([0.4186, 0.1762, 0.3830,  ..., 0.8696, 0.5262, 0.8434]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.1744, 0.7493, 0.4982,  ..., 0.1073, 0.6650, 0.9357])
+tensor([0.0085, 0.7795, 0.4163,  ..., 0.6854, 0.9665, 0.8065])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.611992359161377 seconds
+Time: 10.55235505104065 seconds
 
-[18.28, 17.78, 18.16, 18.31, 18.21, 17.89, 17.98, 18.38, 18.01, 17.96]
-[46.78]
-14.123069763183594
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 220904, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.611992359161377, 'TIME_S_1KI': 0.04803893256419701, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 660.6772035217285, 'W': 46.78}
-[18.28, 17.78, 18.16, 18.31, 18.21, 17.89, 17.98, 18.38, 18.01, 17.96, 18.63, 18.15, 17.86, 18.23, 18.11, 18.07, 18.06, 17.82, 21.74, 19.07]
-329.73
-16.4865
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 220904, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.611992359161377, 'TIME_S_1KI': 0.04803893256419701, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 660.6772035217285, 'W': 46.78, 'J_1KI': 2.990788774860249, 'W_1KI': 0.211766197081085, 'W_D': 30.2935, 'J_D': 427.83721387100223, 'W_D_1KI': 0.13713423025386592, 'J_D_1KI': 0.0006207865419090009}
+[19.4, 18.69, 18.81, 18.65, 19.08, 18.57, 18.71, 18.57, 18.99, 18.41]
+[52.61]
+14.109926462173462
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 220871, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.55235505104065, 'TIME_S_1KI': 0.047776100307603306, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 742.3232311749458, 'W': 52.61}
+[19.4, 18.69, 18.81, 18.65, 19.08, 18.57, 18.71, 18.57, 18.99, 18.41, 19.41, 18.5, 18.98, 18.6, 18.94, 18.51, 18.72, 18.65, 19.15, 18.76]
+338.11
+16.9055
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 220871, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.55235505104065, 'TIME_S_1KI': 0.047776100307603306, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 742.3232311749458, 'W': 52.61, 'J_1KI': 3.3608904345746873, 'W_1KI': 0.2381933345708581, 'W_D': 35.704499999999996, 'J_D': 503.7878693686723, 'W_D_1KI': 0.1616531821742103, 'J_D_1KI': 0.000731889574340725}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json
index 96eeba5..75d1dff 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 111257, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.7375807762146, "TIME_S_1KI": 0.09651150737674573, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 673.9428442955017, "W": 47.23, "J_1KI": 6.057532059065961, "W_1KI": 0.42451261493658826, "W_D": 30.839999999999996, "J_D": 440.0676967620849, "W_D_1KI": 0.2771960415973826, "J_D_1KI": 0.0024914930440096588}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 108957, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.472262620925903, "TIME_S_1KI": 0.09611372028346873, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 747.4210280299187, "W": 52.85, "J_1KI": 6.859779803316159, "W_1KI": 0.48505373679524955, "W_D": 35.6375, "J_D": 503.99653522074226, "W_D_1KI": 0.32707857228080806, "J_D_1KI": 0.0030019050843985064}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.output
index 1589984..14c7cd6 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.025800704956054688}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.026506423950195312}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,    11,  ..., 24988, 24993, 25000]),
-       col_indices=tensor([  36,  564, 3279,  ..., 4511, 4767, 4922]),
-       values=tensor([0.2797, 0.2996, 0.9239,  ..., 0.1899, 0.3417, 0.3512]),
+tensor(crow_indices=tensor([    0,     6,    16,  ..., 24990, 24995, 25000]),
+       col_indices=tensor([ 221, 1679, 2610,  ..., 3544, 4870, 4971]),
+       values=tensor([0.6544, 0.8016, 0.8634,  ..., 0.0114, 0.8406, 0.9999]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.3254, 0.3602, 0.2662,  ..., 0.3074, 0.8226, 0.4658])
+tensor([0.5772, 0.0116, 0.1248,  ..., 0.2008, 0.4824, 0.3225])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.025800704956054688 seconds
+Time: 0.026506423950195312 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '40696', '-ss', '5000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.8407018184661865}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '39613', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.817412853240967}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     7,    11,  ..., 24987, 24992, 25000]),
-       col_indices=tensor([ 298,  713, 1200,  ..., 1957, 3799, 4153]),
-       values=tensor([0.8486, 0.9770, 0.8154,  ..., 0.4467, 0.7513, 0.9966]),
+tensor(crow_indices=tensor([    0,     4,    10,  ..., 24989, 24995, 25000]),
+       col_indices=tensor([ 777, 1395, 2062,  ..., 2560, 3270, 3983]),
+       values=tensor([0.6234, 0.4843, 0.1198,  ..., 0.4624, 0.3011, 0.6442]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8767, 0.4273, 0.1763,  ..., 0.9403, 0.3580, 0.5902])
+tensor([0.7878, 0.6442, 0.3549,  ..., 0.6051, 0.0116, 0.1909])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 3.8407018184661865 seconds
+Time: 3.817412853240967 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '111257', '-ss', '5000', '-sd', '0.001', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.7375807762146}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '108957', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.472262620925903}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     3,  ..., 24991, 24996, 25000]),
-       col_indices=tensor([ 575, 1907, 4405,  ..., 1224, 3086, 3740]),
-       values=tensor([0.1597, 0.6483, 0.2533,  ..., 0.7760, 0.1307, 0.6720]),
+tensor(crow_indices=tensor([    0,     5,    13,  ..., 24993, 24997, 25000]),
+       col_indices=tensor([ 217,  231, 2207,  ...,  518, 4354, 4594]),
+       values=tensor([0.3019, 0.6177, 0.0008,  ..., 0.6182, 0.7561, 0.0455]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0745, 0.7131, 0.6004,  ..., 0.2535, 0.5073, 0.4932])
+tensor([0.2891, 0.1278, 0.3759,  ..., 0.1903, 0.5943, 0.5416])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.7375807762146 seconds
+Time: 10.472262620925903 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     3,  ..., 24991, 24996, 25000]),
-       col_indices=tensor([ 575, 1907, 4405,  ..., 1224, 3086, 3740]),
-       values=tensor([0.1597, 0.6483, 0.2533,  ..., 0.7760, 0.1307, 0.6720]),
+tensor(crow_indices=tensor([    0,     5,    13,  ..., 24993, 24997, 25000]),
+       col_indices=tensor([ 217,  231, 2207,  ...,  518, 4354, 4594]),
+       values=tensor([0.3019, 0.6177, 0.0008,  ..., 0.6182, 0.7561, 0.0455]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0745, 0.7131, 0.6004,  ..., 0.2535, 0.5073, 0.4932])
+tensor([0.2891, 0.1278, 0.3759,  ..., 0.1903, 0.5943, 0.5416])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.7375807762146 seconds
+Time: 10.472262620925903 seconds
 
-[18.26, 17.96, 17.92, 18.07, 18.39, 18.16, 18.05, 18.66, 19.34, 18.38]
-[47.23]
-14.269380569458008
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 111257, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.7375807762146, 'TIME_S_1KI': 0.09651150737674573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 673.9428442955017, 'W': 47.23}
-[18.26, 17.96, 17.92, 18.07, 18.39, 18.16, 18.05, 18.66, 19.34, 18.38, 18.78, 18.12, 17.96, 17.92, 17.93, 18.7, 18.05, 17.87, 17.97, 18.04]
-327.8
-16.39
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 111257, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.7375807762146, 'TIME_S_1KI': 0.09651150737674573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 673.9428442955017, 'W': 47.23, 'J_1KI': 6.057532059065961, 'W_1KI': 0.42451261493658826, 'W_D': 30.839999999999996, 'J_D': 440.0676967620849, 'W_D_1KI': 0.2771960415973826, 'J_D_1KI': 0.0024914930440096588}
+[19.22, 22.12, 20.05, 18.42, 19.49, 18.7, 18.82, 18.59, 18.76, 18.45]
+[52.85]
+14.142308950424194
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 108957, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.472262620925903, 'TIME_S_1KI': 0.09611372028346873, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 747.4210280299187, 'W': 52.85}
+[19.22, 22.12, 20.05, 18.42, 19.49, 18.7, 18.82, 18.59, 18.76, 18.45, 19.4, 18.53, 18.99, 19.62, 19.0, 18.65, 19.18, 18.8, 18.6, 18.79]
+344.25
+17.2125
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 108957, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.472262620925903, 'TIME_S_1KI': 0.09611372028346873, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 747.4210280299187, 'W': 52.85, 'J_1KI': 6.859779803316159, 'W_1KI': 0.48505373679524955, 'W_D': 35.6375, 'J_D': 503.99653522074226, 'W_D_1KI': 0.32707857228080806, 'J_D_1KI': 0.0030019050843985064}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json
index a469b78..38f13e3 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 21150, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.43382978439331, "TIME_S_1KI": 0.4933252853141046, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 678.8009396362305, "W": 47.74, "J_1KI": 32.09460707499908, "W_1KI": 2.257210401891253, "W_D": 31.383250000000004, "J_D": 446.22914932632455, "W_D_1KI": 1.483841607565012, "J_D_1KI": 0.0701579956295514}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 21489, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.522697687149048, "TIME_S_1KI": 0.48967833250263143, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 768.4232043147086, "W": 53.65, "J_1KI": 35.75890941014978, "W_1KI": 2.4966261808367074, "W_D": 35.272999999999996, "J_D": 505.21140141272537, "W_D_1KI": 1.6414444599562565, "J_D_1KI": 0.07638533482043168}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.output
index c2c0006..93ae60b 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.06486701965332031}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.06584358215332031}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     47,     88,  ..., 249915, 249957,
+tensor(crow_indices=tensor([     0,     48,    101,  ..., 249892, 249942,
                             250000]),
-       col_indices=tensor([  72,  118,  180,  ..., 4779, 4849, 4984]),
-       values=tensor([0.8923, 0.3860, 0.0290,  ..., 0.0532, 0.0516, 0.8464]),
+       col_indices=tensor([   6,  107,  321,  ..., 4686, 4752, 4909]),
+       values=tensor([0.6573, 0.6233, 0.5853,  ..., 0.0909, 0.8119, 0.2306]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.1487, 0.9450, 0.3254,  ..., 0.6866, 0.3989, 0.7268])
+tensor([0.2370, 0.0962, 0.8634,  ..., 0.1694, 0.3412, 0.6070])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 0.06486701965332031 seconds
+Time: 0.06584358215332031 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '16186', '-ss', '5000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 8.035360336303711}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '15946', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.791264772415161}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     44,     82,  ..., 249908, 249962,
+tensor(crow_indices=tensor([     0,     37,     86,  ..., 249909, 249955,
                             250000]),
-       col_indices=tensor([  36,   43,   78,  ..., 4796, 4867, 4932]),
-       values=tensor([0.3758, 0.9832, 0.1983,  ..., 0.0743, 0.8633, 0.0592]),
+       col_indices=tensor([ 143,  471,  495,  ..., 4844, 4903, 4966]),
+       values=tensor([0.7815, 0.7659, 0.8558,  ..., 0.4556, 0.1537, 0.8448]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5985, 0.2492, 0.1240,  ..., 0.8930, 0.7764, 0.3200])
+tensor([0.0365, 0.6707, 0.0658,  ..., 0.6930, 0.0408, 0.2771])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 8.035360336303711 seconds
+Time: 7.791264772415161 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '21150', '-ss', '5000', '-sd', '0.01', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.43382978439331}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '21489', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.522697687149048}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     47,     95,  ..., 249894, 249948,
+tensor(crow_indices=tensor([     0,     40,     83,  ..., 249895, 249959,
                             250000]),
-       col_indices=tensor([ 129,  143,  228,  ..., 4613, 4768, 4965]),
-       values=tensor([0.2601, 0.0327, 0.3118,  ..., 0.8257, 0.2689, 0.3965]),
+       col_indices=tensor([  30,   48,  173,  ..., 4252, 4292, 4802]),
+       values=tensor([0.0239, 0.4781, 0.2863,  ..., 0.4474, 0.8773, 0.0233]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3768, 0.4725, 0.8369,  ..., 0.3357, 0.4139, 0.5546])
+tensor([0.9052, 0.5856, 0.4582,  ..., 0.5510, 0.4447, 0.7272])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.43382978439331 seconds
+Time: 10.522697687149048 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     47,     95,  ..., 249894, 249948,
+tensor(crow_indices=tensor([     0,     40,     83,  ..., 249895, 249959,
                             250000]),
-       col_indices=tensor([ 129,  143,  228,  ..., 4613, 4768, 4965]),
-       values=tensor([0.2601, 0.0327, 0.3118,  ..., 0.8257, 0.2689, 0.3965]),
+       col_indices=tensor([  30,   48,  173,  ..., 4252, 4292, 4802]),
+       values=tensor([0.0239, 0.4781, 0.2863,  ..., 0.4474, 0.8773, 0.0233]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3768, 0.4725, 0.8369,  ..., 0.3357, 0.4139, 0.5546])
+tensor([0.9052, 0.5856, 0.4582,  ..., 0.5510, 0.4447, 0.7272])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.43382978439331 seconds
+Time: 10.522697687149048 seconds
 
-[18.56, 17.95, 18.07, 18.36, 18.1, 18.2, 18.17, 18.16, 18.01, 17.83]
-[47.74]
-14.218704223632812
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21150, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.43382978439331, 'TIME_S_1KI': 0.4933252853141046, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 678.8009396362305, 'W': 47.74}
-[18.56, 17.95, 18.07, 18.36, 18.1, 18.2, 18.17, 18.16, 18.01, 17.83, 19.27, 18.21, 17.98, 17.93, 18.07, 18.27, 18.29, 17.78, 18.3, 18.91]
-327.135
-16.356749999999998
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21150, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.43382978439331, 'TIME_S_1KI': 0.4933252853141046, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 678.8009396362305, 'W': 47.74, 'J_1KI': 32.09460707499908, 'W_1KI': 2.257210401891253, 'W_D': 31.383250000000004, 'J_D': 446.22914932632455, 'W_D_1KI': 1.483841607565012, 'J_D_1KI': 0.0701579956295514}
+[19.6, 18.7, 18.63, 18.64, 18.6, 19.05, 18.56, 21.87, 19.97, 18.75]
+[53.65]
+14.322892904281616
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21489, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.522697687149048, 'TIME_S_1KI': 0.48967833250263143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 768.4232043147086, 'W': 53.65}
+[19.6, 18.7, 18.63, 18.64, 18.6, 19.05, 18.56, 21.87, 19.97, 18.75, 18.88, 19.62, 19.99, 35.37, 25.34, 18.41, 19.38, 18.72, 18.61, 18.93]
+367.54
+18.377000000000002
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21489, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.522697687149048, 'TIME_S_1KI': 0.48967833250263143, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 768.4232043147086, 'W': 53.65, 'J_1KI': 35.75890941014978, 'W_1KI': 2.4966261808367074, 'W_D': 35.272999999999996, 'J_D': 505.21140141272537, 'W_D_1KI': 1.6414444599562565, 'J_D_1KI': 0.07638533482043168}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json
index c9a64dd..9a457a7 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 4516, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.427689790725708, "TIME_S_1KI": 2.309054426644311, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 694.9637099742889, "W": 47.9, "J_1KI": 153.88921832911623, "W_1KI": 10.606731620903455, "W_D": 31.338749999999997, "J_D": 454.682546262145, "W_D_1KI": 6.939492914083259, "J_D_1KI": 1.5366459065729094}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 4507, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.452783107757568, "TIME_S_1KI": 2.319232994843037, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 788.3322271823882, "W": 53.8, "J_1KI": 174.91285271408657, "W_1KI": 11.936986909252274, "W_D": 36.52775, "J_D": 535.2416823691725, "W_D_1KI": 8.104670512536055, "J_D_1KI": 1.7982406284748294}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.output
index ce3acc5..6f00292 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.2486708164215088}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.24941706657409668}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     247,     481,  ..., 1249493,
-                            1249753, 1250000]),
-       col_indices=tensor([   6,   31,   64,  ..., 4955, 4959, 4978]),
-       values=tensor([0.8259, 0.7056, 0.5562,  ..., 0.4513, 0.5248, 0.2272]),
+tensor(crow_indices=tensor([      0,     269,     515,  ..., 1249442,
+                            1249742, 1250000]),
+       col_indices=tensor([  18,   33,   64,  ..., 4944, 4945, 4969]),
+       values=tensor([0.6983, 0.1365, 0.6760,  ..., 0.8759, 0.6193, 0.1743]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.7144, 0.3655, 0.7208,  ..., 0.9456, 0.6678, 0.5049])
+tensor([0.4545, 0.8492, 0.8687,  ..., 0.9140, 0.3104, 0.7444])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 0.2486708164215088 seconds
+Time: 0.24941706657409668 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4222', '-ss', '5000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.815936088562012}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4209', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.805480241775513}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     269,     519,  ..., 1249537,
-                            1249769, 1250000]),
-       col_indices=tensor([  42,   49,   74,  ..., 4955, 4973, 4990]),
-       values=tensor([0.0021, 0.9721, 0.1598,  ..., 0.6170, 0.8086, 0.1248]),
+tensor(crow_indices=tensor([      0,     258,     536,  ..., 1249488,
+                            1249729, 1250000]),
+       col_indices=tensor([  11,   74,  145,  ..., 4958, 4988, 4993]),
+       values=tensor([0.2265, 0.0876, 0.3577,  ..., 0.6186, 0.3326, 0.6793]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.9473, 0.7157, 0.7204,  ..., 0.2702, 0.4361, 0.8753])
+tensor([0.9261, 0.7700, 0.5305,  ..., 0.5419, 0.6760, 0.0747])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 9.815936088562012 seconds
+Time: 9.805480241775513 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4516', '-ss', '5000', '-sd', '0.05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.427689790725708}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4507', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.452783107757568}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     262,     521,  ..., 1249547,
-                            1249780, 1250000]),
-       col_indices=tensor([   5,   16,   32,  ..., 4965, 4966, 4994]),
-       values=tensor([0.6294, 0.1213, 0.8577,  ..., 0.8057, 0.3565, 0.7731]),
+tensor(crow_indices=tensor([      0,     254,     505,  ..., 1249513,
+                            1249748, 1250000]),
+       col_indices=tensor([   4,   15,   43,  ..., 4942, 4967, 4986]),
+       values=tensor([0.9980, 0.7569, 0.0426,  ..., 0.7541, 0.1223, 0.2328]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.5561, 0.2020, 0.8277,  ..., 0.0800, 0.4571, 0.4718])
+tensor([0.1484, 0.2157, 0.1969,  ..., 0.4129, 0.2768, 0.1680])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.427689790725708 seconds
+Time: 10.452783107757568 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     262,     521,  ..., 1249547,
-                            1249780, 1250000]),
-       col_indices=tensor([   5,   16,   32,  ..., 4965, 4966, 4994]),
-       values=tensor([0.6294, 0.1213, 0.8577,  ..., 0.8057, 0.3565, 0.7731]),
+tensor(crow_indices=tensor([      0,     254,     505,  ..., 1249513,
+                            1249748, 1250000]),
+       col_indices=tensor([   4,   15,   43,  ..., 4942, 4967, 4986]),
+       values=tensor([0.9980, 0.7569, 0.0426,  ..., 0.7541, 0.1223, 0.2328]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.5561, 0.2020, 0.8277,  ..., 0.0800, 0.4571, 0.4718])
+tensor([0.1484, 0.2157, 0.1969,  ..., 0.4129, 0.2768, 0.1680])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.427689790725708 seconds
+Time: 10.452783107757568 seconds
 
-[18.38, 21.41, 18.53, 17.98, 18.13, 17.94, 18.18, 18.19, 18.64, 17.87]
-[47.9]
-14.508636951446533
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4516, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.427689790725708, 'TIME_S_1KI': 2.309054426644311, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 694.9637099742889, 'W': 47.9}
-[18.38, 21.41, 18.53, 17.98, 18.13, 17.94, 18.18, 18.19, 18.64, 17.87, 18.41, 18.3, 18.02, 18.19, 18.18, 18.28, 18.35, 17.94, 18.16, 18.95]
-331.225
-16.56125
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4516, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.427689790725708, 'TIME_S_1KI': 2.309054426644311, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 694.9637099742889, 'W': 47.9, 'J_1KI': 153.88921832911623, 'W_1KI': 10.606731620903455, 'W_D': 31.338749999999997, 'J_D': 454.682546262145, 'W_D_1KI': 6.939492914083259, 'J_D_1KI': 1.5366459065729094}
+[18.99, 18.82, 18.82, 20.01, 18.56, 18.75, 18.66, 19.57, 18.83, 19.81]
+[53.8]
+14.653015375137329
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4507, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.452783107757568, 'TIME_S_1KI': 2.319232994843037, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 788.3322271823882, 'W': 53.8}
+[18.99, 18.82, 18.82, 20.01, 18.56, 18.75, 18.66, 19.57, 18.83, 19.81, 18.87, 18.49, 18.96, 18.99, 18.64, 23.2, 19.54, 18.4, 19.14, 18.46]
+345.445
+17.27225
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4507, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.452783107757568, 'TIME_S_1KI': 2.319232994843037, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 788.3322271823882, 'W': 53.8, 'J_1KI': 174.91285271408657, 'W_1KI': 11.936986909252274, 'W_D': 36.52775, 'J_D': 535.2416823691725, 'W_D_1KI': 8.104670512536055, 'J_D_1KI': 1.7982406284748294}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json
index 6d226de..2e2ae33 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 2077, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.487555503845215, "TIME_S_1KI": 5.04937674715706, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 724.931280415058, "W": 48.23, "J_1KI": 349.0280599013279, "W_1KI": 23.220991815117955, "W_D": 31.779249999999998, "J_D": 477.6647811140418, "W_D_1KI": 15.300553683196917, "J_D_1KI": 7.366660415597938}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 2065, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.45287299156189, "TIME_S_1KI": 5.061923966858059, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 809.5970643353462, "W": 54.129999999999995, "J_1KI": 392.05668975077293, "W_1KI": 26.213075060532685, "W_D": 36.782999999999994, "J_D": 550.1461078412532, "W_D_1KI": 17.812590799031472, "J_D_1KI": 8.625951960790058}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.output
index ec991c2..99c4ade 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.53643798828125}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.545907735824585}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     485,     953,  ..., 2499027,
-                            2499524, 2500000]),
-       col_indices=tensor([   4,    6,   18,  ..., 4982, 4984, 4999]),
-       values=tensor([0.6950, 0.7335, 0.8547,  ..., 0.7303, 0.2740, 0.2643]),
+tensor(crow_indices=tensor([      0,     463,     924,  ..., 2498984,
+                            2499500, 2500000]),
+       col_indices=tensor([   3,    5,   15,  ..., 4982, 4993, 4996]),
+       values=tensor([0.5905, 0.3756, 0.1756,  ..., 0.8297, 0.4615, 0.2237]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4725, 0.9871, 0.6689,  ..., 0.5705, 0.1526, 0.5563])
+tensor([0.9622, 0.8462, 0.4599,  ..., 0.4872, 0.1929, 0.7015])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 0.53643798828125 seconds
+Time: 0.545907735824585 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1957', '-ss', '5000', '-sd', '0.1', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.889901638031006}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1923', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.776902198791504}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     453,     921,  ..., 2498978,
-                            2499478, 2500000]),
-       col_indices=tensor([   4,   20,   34,  ..., 4974, 4986, 4991]),
-       values=tensor([0.7939, 0.2865, 0.3388,  ..., 0.3715, 0.9532, 0.1224]),
+tensor(crow_indices=tensor([      0,     532,    1021,  ..., 2498948,
+                            2499479, 2500000]),
+       col_indices=tensor([   7,    8,   20,  ..., 4964, 4986, 4994]),
+       values=tensor([0.7984, 0.5010, 0.6603,  ..., 0.6563, 0.5541, 0.6171]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8979, 0.8998, 0.6031,  ..., 0.0686, 0.1119, 0.6753])
+tensor([0.8395, 0.6596, 0.0148,  ..., 0.6891, 0.8591, 0.4265])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 9.889901638031006 seconds
+Time: 9.776902198791504 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2077', '-ss', '5000', '-sd', '0.1', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.487555503845215}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2065', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.45287299156189}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     534,    1011,  ..., 2499001,
-                            2499492, 2500000]),
-       col_indices=tensor([  12,   16,   26,  ..., 4984, 4992, 4995]),
-       values=tensor([0.3129, 0.5758, 0.2112,  ..., 0.6208, 0.5668, 0.8482]),
+tensor(crow_indices=tensor([      0,     505,    1011,  ..., 2499006,
+                            2499526, 2500000]),
+       col_indices=tensor([  27,   29,   36,  ..., 4978, 4986, 4992]),
+       values=tensor([0.2782, 0.8454, 0.7345,  ..., 0.2216, 0.1965, 0.2730]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7114, 0.1437, 0.5452,  ..., 0.6795, 0.1114, 0.8178])
+tensor([0.0758, 0.2374, 0.4573,  ..., 0.8597, 0.4706, 0.8817])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.487555503845215 seconds
+Time: 10.45287299156189 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     534,    1011,  ..., 2499001,
-                            2499492, 2500000]),
-       col_indices=tensor([  12,   16,   26,  ..., 4984, 4992, 4995]),
-       values=tensor([0.3129, 0.5758, 0.2112,  ..., 0.6208, 0.5668, 0.8482]),
+tensor(crow_indices=tensor([      0,     505,    1011,  ..., 2499006,
+                            2499526, 2500000]),
+       col_indices=tensor([  27,   29,   36,  ..., 4978, 4986, 4992]),
+       values=tensor([0.2782, 0.8454, 0.7345,  ..., 0.2216, 0.1965, 0.2730]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7114, 0.1437, 0.5452,  ..., 0.6795, 0.1114, 0.8178])
+tensor([0.0758, 0.2374, 0.4573,  ..., 0.8597, 0.4706, 0.8817])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.487555503845215 seconds
+Time: 10.45287299156189 seconds
 
-[18.45, 17.83, 21.85, 18.4, 18.31, 17.94, 17.97, 18.32, 18.18, 17.83]
-[48.23]
-15.030712842941284
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.487555503845215, 'TIME_S_1KI': 5.04937674715706, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 724.931280415058, 'W': 48.23}
-[18.45, 17.83, 21.85, 18.4, 18.31, 17.94, 17.97, 18.32, 18.18, 17.83, 18.49, 17.82, 18.18, 18.17, 18.15, 17.82, 18.07, 17.68, 17.99, 17.9]
-329.015
-16.45075
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.487555503845215, 'TIME_S_1KI': 5.04937674715706, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 724.931280415058, 'W': 48.23, 'J_1KI': 349.0280599013279, 'W_1KI': 23.220991815117955, 'W_D': 31.779249999999998, 'J_D': 477.6647811140418, 'W_D_1KI': 15.300553683196917, 'J_D_1KI': 7.366660415597938}
+[19.0, 23.3, 18.72, 18.84, 18.99, 18.54, 18.55, 18.38, 18.99, 18.44]
+[54.13]
+14.956531763076782
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2065, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.45287299156189, 'TIME_S_1KI': 5.061923966858059, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 809.5970643353462, 'W': 54.129999999999995}
+[19.0, 23.3, 18.72, 18.84, 18.99, 18.54, 18.55, 18.38, 18.99, 18.44, 18.9, 18.89, 18.67, 18.69, 18.68, 20.93, 21.41, 18.74, 18.95, 19.0]
+346.94
+17.347
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2065, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.45287299156189, 'TIME_S_1KI': 5.061923966858059, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 809.5970643353462, 'W': 54.129999999999995, 'J_1KI': 392.05668975077293, 'W_1KI': 26.213075060532685, 'W_D': 36.782999999999994, 'J_D': 550.1461078412532, 'W_D_1KI': 17.812590799031472, 'J_D_1KI': 8.625951960790058}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.json
index e6d6ed2..bc073dc 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 962, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.386102437973022, "TIME_S_1KI": 10.796364280637237, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 760.2223528313637, "W": 48.37, "J_1KI": 790.2519260201285, "W_1KI": 50.280665280665275, "W_D": 31.813249999999996, "J_D": 500.00297221857306, "W_D_1KI": 33.069906444906444, "J_D_1KI": 34.376202125682376}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 971, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.375150203704834, "TIME_S_1KI": 10.685015657780468, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 849.9418584561348, "W": 54.17, "J_1KI": 875.3263217879864, "W_1KI": 55.78784757981463, "W_D": 37.319250000000004, "J_D": 585.5490622335077, "W_D_1KI": 38.43383110195675, "J_D_1KI": 39.581700413961634}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.output
index c8b98a6..f3998cb 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 1.090480089187622}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 1.0806596279144287}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     979,    1989,  ..., 4997947,
-                            4998978, 5000000]),
-       col_indices=tensor([   0,    1,    2,  ..., 4989, 4991, 4992]),
-       values=tensor([0.4629, 0.8349, 0.1230,  ..., 0.3254, 0.2010, 0.4262]),
+tensor(crow_indices=tensor([      0,     995,    1988,  ..., 4997969,
+                            4998993, 5000000]),
+       col_indices=tensor([   2,    8,   15,  ..., 4982, 4992, 4997]),
+       values=tensor([0.7197, 0.1704, 0.3676,  ..., 0.5241, 0.8938, 0.1612]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.8820, 0.9283, 0.2134,  ..., 0.8569, 0.5183, 0.3465])
+tensor([0.6730, 0.8677, 0.3708,  ..., 0.7112, 0.4020, 0.2942])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 1.090480089187622 seconds
+Time: 1.0806596279144287 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '962', '-ss', '5000', '-sd', '0.2', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.386102437973022}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '971', '-ss', '5000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.375150203704834}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1020,    2003,  ..., 4997983,
-                            4998972, 5000000]),
-       col_indices=tensor([   2,   12,   14,  ..., 4982, 4988, 4994]),
-       values=tensor([0.5268, 0.0953, 0.2601,  ..., 0.2366, 0.8226, 0.2641]),
+tensor(crow_indices=tensor([      0,     988,    1955,  ..., 4997972,
+                            4998955, 5000000]),
+       col_indices=tensor([   3,   12,   18,  ..., 4992, 4993, 4996]),
+       values=tensor([0.6820, 0.5689, 0.9542,  ..., 0.8233, 0.5538, 0.5282]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.6710, 0.0398, 0.1150,  ..., 0.1943, 0.5070, 0.9802])
+tensor([0.8777, 0.1124, 0.1362,  ..., 0.2255, 0.2013, 0.9144])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.386102437973022 seconds
+Time: 10.375150203704834 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1020,    2003,  ..., 4997983,
-                            4998972, 5000000]),
-       col_indices=tensor([   2,   12,   14,  ..., 4982, 4988, 4994]),
-       values=tensor([0.5268, 0.0953, 0.2601,  ..., 0.2366, 0.8226, 0.2641]),
+tensor(crow_indices=tensor([      0,     988,    1955,  ..., 4997972,
+                            4998955, 5000000]),
+       col_indices=tensor([   3,   12,   18,  ..., 4992, 4993, 4996]),
+       values=tensor([0.6820, 0.5689, 0.9542,  ..., 0.8233, 0.5538, 0.5282]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.6710, 0.0398, 0.1150,  ..., 0.1943, 0.5070, 0.9802])
+tensor([0.8777, 0.1124, 0.1362,  ..., 0.2255, 0.2013, 0.9144])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.386102437973022 seconds
+Time: 10.375150203704834 seconds
 
-[18.34, 18.12, 18.35, 17.91, 17.99, 18.03, 18.28, 18.28, 17.95, 18.09]
-[48.37]
-15.71681523323059
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 962, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.386102437973022, 'TIME_S_1KI': 10.796364280637237, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 760.2223528313637, 'W': 48.37}
-[18.34, 18.12, 18.35, 17.91, 17.99, 18.03, 18.28, 18.28, 17.95, 18.09, 18.27, 18.18, 18.12, 17.89, 18.15, 21.61, 18.11, 17.8, 18.1, 21.83]
-331.13500000000005
-16.55675
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 962, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.386102437973022, 'TIME_S_1KI': 10.796364280637237, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 760.2223528313637, 'W': 48.37, 'J_1KI': 790.2519260201285, 'W_1KI': 50.280665280665275, 'W_D': 31.813249999999996, 'J_D': 500.00297221857306, 'W_D_1KI': 33.069906444906444, 'J_D_1KI': 34.376202125682376}
+[18.9, 18.56, 18.78, 18.83, 18.79, 18.5, 18.74, 18.48, 18.64, 18.83]
+[54.17]
+15.690268754959106
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 971, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.375150203704834, 'TIME_S_1KI': 10.685015657780468, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 849.9418584561348, 'W': 54.17}
+[18.9, 18.56, 18.78, 18.83, 18.79, 18.5, 18.74, 18.48, 18.64, 18.83, 19.31, 18.65, 19.01, 18.67, 18.6, 18.68, 18.61, 18.75, 18.77, 18.87]
+337.015
+16.850749999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 971, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.375150203704834, 'TIME_S_1KI': 10.685015657780468, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 849.9418584561348, 'W': 54.17, 'J_1KI': 875.3263217879864, 'W_1KI': 55.78784757981463, 'W_D': 37.319250000000004, 'J_D': 585.5490622335077, 'W_D_1KI': 38.43383110195675, 'J_D_1KI': 39.581700413961634}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.json
index 6efa366..57ad521 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 640, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.40328574180603, "TIME_S_1KI": 16.255133971571922, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 792.4070657157898, "W": 48.54, "J_1KI": 1238.1360401809216, "W_1KI": 75.84375, "W_D": 32.294250000000005, "J_D": 527.1980198185445, "W_D_1KI": 50.45976562500001, "J_D_1KI": 78.84338378906251}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 634, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.328722476959229, "TIME_S_1KI": 16.291360373752727, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 884.4766217422484, "W": 54.16, "J_1KI": 1395.0735358710542, "W_1KI": 85.42586750788642, "W_D": 36.95925, "J_D": 603.5744568339586, "W_D_1KI": 58.29534700315457, "J_D_1KI": 91.94849685040153}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.output
index e11bc1c..20f8484 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.6401786804199219}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.65598464012146}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1479,    2972,  ..., 7496976,
-                            7498517, 7500000]),
-       col_indices=tensor([   4,   10,   12,  ..., 4987, 4989, 4997]),
-       values=tensor([0.8092, 0.2326, 0.1918,  ..., 0.7537, 0.2703, 0.9406]),
+tensor(crow_indices=tensor([      0,    1512,    3010,  ..., 7497102,
+                            7498580, 7500000]),
+       col_indices=tensor([   0,    6,    7,  ..., 4982, 4993, 4994]),
+       values=tensor([0.1441, 0.1264, 0.1974,  ..., 0.8347, 0.6432, 0.4326]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.6079, 0.4244, 0.8803,  ..., 0.9929, 0.8834, 0.4182])
+tensor([0.7793, 0.6447, 0.2763,  ..., 0.7560, 0.6340, 0.7473])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 1.6401786804199219 seconds
+Time: 1.65598464012146 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '640', '-ss', '5000', '-sd', '0.3', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.40328574180603}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '634', '-ss', '5000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.328722476959229}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1469,    2947,  ..., 7496962,
-                            7498492, 7500000]),
-       col_indices=tensor([   5,    8,   10,  ..., 4979, 4981, 4995]),
-       values=tensor([0.5097, 0.4133, 0.1946,  ..., 0.6762, 0.7827, 0.5941]),
+tensor(crow_indices=tensor([      0,    1455,    2926,  ..., 7496907,
+                            7498447, 7500000]),
+       col_indices=tensor([   7,    8,   12,  ..., 4996, 4997, 4999]),
+       values=tensor([0.5602, 0.6149, 0.8310,  ..., 0.9658, 0.2109, 0.7676]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.3256, 0.8526, 0.3288,  ..., 0.0837, 0.1622, 0.2040])
+tensor([0.2196, 0.3329, 0.8031,  ..., 0.2668, 0.7746, 0.1687])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.40328574180603 seconds
+Time: 10.328722476959229 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1469,    2947,  ..., 7496962,
-                            7498492, 7500000]),
-       col_indices=tensor([   5,    8,   10,  ..., 4979, 4981, 4995]),
-       values=tensor([0.5097, 0.4133, 0.1946,  ..., 0.6762, 0.7827, 0.5941]),
+tensor(crow_indices=tensor([      0,    1455,    2926,  ..., 7496907,
+                            7498447, 7500000]),
+       col_indices=tensor([   7,    8,   12,  ..., 4996, 4997, 4999]),
+       values=tensor([0.5602, 0.6149, 0.8310,  ..., 0.9658, 0.2109, 0.7676]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.3256, 0.8526, 0.3288,  ..., 0.0837, 0.1622, 0.2040])
+tensor([0.2196, 0.3329, 0.8031,  ..., 0.2668, 0.7746, 0.1687])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.40328574180603 seconds
+Time: 10.328722476959229 seconds
 
-[18.61, 18.25, 18.17, 17.83, 17.98, 17.89, 17.97, 17.91, 17.89, 18.07]
-[48.54]
-16.32482624053955
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 640, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.40328574180603, 'TIME_S_1KI': 16.255133971571922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 792.4070657157898, 'W': 48.54}
-[18.61, 18.25, 18.17, 17.83, 17.98, 17.89, 17.97, 17.91, 17.89, 18.07, 18.46, 18.32, 17.92, 18.02, 17.98, 18.33, 17.98, 17.9, 18.02, 17.97]
-324.91499999999996
-16.245749999999997
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 640, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.40328574180603, 'TIME_S_1KI': 16.255133971571922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 792.4070657157898, 'W': 48.54, 'J_1KI': 1238.1360401809216, 'W_1KI': 75.84375, 'W_D': 32.294250000000005, 'J_D': 527.1980198185445, 'W_D_1KI': 50.45976562500001, 'J_D_1KI': 78.84338378906251}
+[18.9, 18.45, 18.68, 23.55, 19.81, 18.49, 19.91, 18.65, 18.58, 18.47]
+[54.16]
+16.330809116363525
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 634, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.328722476959229, 'TIME_S_1KI': 16.291360373752727, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 884.4766217422484, 'W': 54.16}
+[18.9, 18.45, 18.68, 23.55, 19.81, 18.49, 19.91, 18.65, 18.58, 18.47, 19.6, 18.53, 19.12, 18.47, 18.97, 18.51, 18.9, 18.9, 18.76, 18.5]
+344.015
+17.20075
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 634, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.328722476959229, 'TIME_S_1KI': 16.291360373752727, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 884.4766217422484, 'W': 54.16, 'J_1KI': 1395.0735358710542, 'W_1KI': 85.42586750788642, 'W_D': 36.95925, 'J_D': 603.5744568339586, 'W_D_1KI': 58.29534700315457, 'J_D_1KI': 91.94849685040153}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.json
index 679e37e..324d8eb 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 393, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.361774921417236, "TIME_S_1KI": 26.365839494700346, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 830.257935385704, "W": 47.87, "J_1KI": 2112.615611668458, "W_1KI": 121.80661577608141, "W_D": 31.368499999999997, "J_D": 544.0556934645175, "W_D_1KI": 79.81806615776081, "J_D_1KI": 203.09940498157965}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 397, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.436140060424805, "TIME_S_1KI": 26.287506449432758, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 926.5611728620529, "W": 53.86, "J_1KI": 2333.9072364283447, "W_1KI": 135.6675062972292, "W_D": 36.199, "J_D": 622.7365001194477, "W_D_1KI": 91.18136020151132, "J_D_1KI": 229.67597028088497}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.output
index 4eb279b..b446ca1 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.output
@@ -1,14 +1,15 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 2.6700339317321777}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 2.640493392944336}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1981,     4013,  ...,  9996053,
-                             9998014, 10000000]),
-       col_indices=tensor([   1,    2,    4,  ..., 4993, 4995, 4997]),
-       values=tensor([0.1269, 0.6137, 0.4927,  ..., 0.6127, 0.1027, 0.1107]),
-       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1274, 0.2875, 0.5158,  ..., 0.6638, 0.6368, 0.8182])
+tensor(crow_indices=tensor([       0,     2010,     3987,  ...,  9995974,
+                             9997951, 10000000]),
+       col_indices=tensor([   1,    7,   10,  ..., 4992, 4995, 4997]),
+       values=tensor([7.4519e-04, 5.9544e-01, 6.5419e-01,  ...,
+                      9.4636e-01, 8.2090e-01, 7.6338e-01]), size=(5000, 5000),
+       nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3936, 0.5475, 0.9417,  ..., 0.3205, 0.6260, 0.3688])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +17,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 2.6700339317321777 seconds
+Time: 2.640493392944336 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '393', '-ss', '5000', '-sd', '0.4', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.361774921417236}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '397', '-ss', '5000', '-sd', '0.4', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.436140060424805}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2035,     3994,  ...,  9995956,
-                             9997978, 10000000]),
-       col_indices=tensor([   2,    3,    4,  ..., 4989, 4993, 4999]),
-       values=tensor([0.0874, 0.4595, 0.0218,  ..., 0.9380, 0.2756, 0.2464]),
+tensor(crow_indices=tensor([       0,     1974,     3981,  ...,  9996025,
+                             9998009, 10000000]),
+       col_indices=tensor([   4,    5,    7,  ..., 4992, 4994, 4995]),
+       values=tensor([0.8520, 0.4990, 0.8451,  ..., 0.4145, 0.7436, 0.6838]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1585, 0.7775, 0.6260,  ..., 0.0357, 0.4122, 0.0843])
+tensor([0.6229, 0.6402, 0.3556,  ..., 0.0272, 0.8417, 0.9178])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +37,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 10.361774921417236 seconds
+Time: 10.436140060424805 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2035,     3994,  ...,  9995956,
-                             9997978, 10000000]),
-       col_indices=tensor([   2,    3,    4,  ..., 4989, 4993, 4999]),
-       values=tensor([0.0874, 0.4595, 0.0218,  ..., 0.9380, 0.2756, 0.2464]),
+tensor(crow_indices=tensor([       0,     1974,     3981,  ...,  9996025,
+                             9998009, 10000000]),
+       col_indices=tensor([   4,    5,    7,  ..., 4992, 4994, 4995]),
+       values=tensor([0.8520, 0.4990, 0.8451,  ..., 0.4145, 0.7436, 0.6838]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1585, 0.7775, 0.6260,  ..., 0.0357, 0.4122, 0.0843])
+tensor([0.6229, 0.6402, 0.3556,  ..., 0.0272, 0.8417, 0.9178])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +54,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 10.361774921417236 seconds
+Time: 10.436140060424805 seconds
 
-[18.09, 21.49, 19.13, 18.18, 17.87, 18.12, 18.49, 17.91, 18.18, 17.86]
-[47.87]
-17.344013690948486
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 393, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.361774921417236, 'TIME_S_1KI': 26.365839494700346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 830.257935385704, 'W': 47.87}
-[18.09, 21.49, 19.13, 18.18, 17.87, 18.12, 18.49, 17.91, 18.18, 17.86, 18.63, 18.02, 17.95, 17.88, 17.98, 18.08, 17.96, 17.78, 18.35, 18.74]
-330.03
-16.5015
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 393, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.361774921417236, 'TIME_S_1KI': 26.365839494700346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 830.257935385704, 'W': 47.87, 'J_1KI': 2112.615611668458, 'W_1KI': 121.80661577608141, 'W_D': 31.368499999999997, 'J_D': 544.0556934645175, 'W_D_1KI': 79.81806615776081, 'J_D_1KI': 203.09940498157965}
+[18.83, 18.74, 18.78, 27.34, 18.7, 18.43, 18.59, 18.76, 18.62, 18.54]
+[53.86]
+17.2031409740448
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 397, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.436140060424805, 'TIME_S_1KI': 26.287506449432758, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 926.5611728620529, 'W': 53.86}
+[18.83, 18.74, 18.78, 27.34, 18.7, 18.43, 18.59, 18.76, 18.62, 18.54, 19.12, 18.44, 19.84, 25.25, 18.79, 18.56, 19.54, 18.82, 18.53, 18.49]
+353.21999999999997
+17.660999999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 397, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.436140060424805, 'TIME_S_1KI': 26.287506449432758, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 926.5611728620529, 'W': 53.86, 'J_1KI': 2333.9072364283447, 'W_1KI': 135.6675062972292, 'W_D': 36.199, 'J_D': 622.7365001194477, 'W_D_1KI': 91.18136020151132, 'J_D_1KI': 229.67597028088497}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.json
index afa179f..d1e779a 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 307, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.394981145858765, "TIME_S_1KI": 33.85987343927936, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 930.0089691495895, "W": 47.58, "J_1KI": 3029.3451763830276, "W_1KI": 154.98371335504885, "W_D": 31.056999999999995, "J_D": 607.0468380596636, "W_D_1KI": 101.16286644951138, "J_D_1KI": 329.5207376205583}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 311, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.450198650360107, "TIME_S_1KI": 33.60192492077205, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 962.3892062711716, "W": 53.74, "J_1KI": 3094.499055534314, "W_1KI": 172.79742765273312, "W_D": 36.356750000000005, "J_D": 651.0856675679089, "W_D_1KI": 116.90273311897107, "J_D_1KI": 375.8930325368845}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.output
index 91aa70e..ef415a1 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 3.415400505065918}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 3.3665878772735596}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2477,     4964,  ..., 12494964,
-                            12497482, 12500000]),
-       col_indices=tensor([   1,    2,    4,  ..., 4993, 4994, 4997]),
-       values=tensor([0.5791, 0.4301, 0.3570,  ..., 0.1858, 0.4639, 0.9573]),
+tensor(crow_indices=tensor([       0,     2529,     5030,  ..., 12494987,
+                            12497532, 12500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4997, 4998, 4999]),
+       values=tensor([0.6195, 0.8024, 0.8856,  ..., 0.3756, 0.3749, 0.9034]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.0093, 0.1244, 0.8882,  ..., 0.7606, 0.5225, 0.2163])
+tensor([0.1073, 0.7449, 0.4816,  ..., 0.4290, 0.8634, 0.7307])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 3.415400505065918 seconds
+Time: 3.3665878772735596 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '307', '-ss', '5000', '-sd', '0.5', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.394981145858765}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '311', '-ss', '5000', '-sd', '0.5', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.450198650360107}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2499,     4994,  ..., 12495082,
-                            12497507, 12500000]),
-       col_indices=tensor([   0,    1,    6,  ..., 4995, 4997, 4999]),
-       values=tensor([0.6585, 0.9778, 0.9803,  ..., 0.8325, 0.0849, 0.1040]),
+tensor(crow_indices=tensor([       0,     2539,     5026,  ..., 12494990,
+                            12497512, 12500000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4995, 4997, 4998]),
+       values=tensor([0.4822, 0.2881, 0.1292,  ..., 0.9343, 0.6851, 0.3724]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.3486, 0.4233, 0.3644,  ..., 0.4149, 0.2376, 0.4812])
+tensor([0.5234, 0.0534, 0.5804,  ..., 0.3109, 0.1531, 0.9809])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 10.394981145858765 seconds
+Time: 10.450198650360107 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2499,     4994,  ..., 12495082,
-                            12497507, 12500000]),
-       col_indices=tensor([   0,    1,    6,  ..., 4995, 4997, 4999]),
-       values=tensor([0.6585, 0.9778, 0.9803,  ..., 0.8325, 0.0849, 0.1040]),
+tensor(crow_indices=tensor([       0,     2539,     5026,  ..., 12494990,
+                            12497512, 12500000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4995, 4997, 4998]),
+       values=tensor([0.4822, 0.2881, 0.1292,  ..., 0.9343, 0.6851, 0.3724]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.3486, 0.4233, 0.3644,  ..., 0.4149, 0.2376, 0.4812])
+tensor([0.5234, 0.0534, 0.5804,  ..., 0.3109, 0.1531, 0.9809])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 10.394981145858765 seconds
+Time: 10.450198650360107 seconds
 
-[18.48, 21.56, 18.9, 18.32, 18.22, 17.83, 18.09, 17.98, 18.55, 18.34]
-[47.58]
-19.546216249465942
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 307, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.394981145858765, 'TIME_S_1KI': 33.85987343927936, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 930.0089691495895, 'W': 47.58}
-[18.48, 21.56, 18.9, 18.32, 18.22, 17.83, 18.09, 17.98, 18.55, 18.34, 18.35, 17.93, 17.78, 17.82, 18.22, 18.23, 18.5, 17.76, 18.34, 17.69]
-330.46000000000004
-16.523000000000003
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 307, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.394981145858765, 'TIME_S_1KI': 33.85987343927936, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 930.0089691495895, 'W': 47.58, 'J_1KI': 3029.3451763830276, 'W_1KI': 154.98371335504885, 'W_D': 31.056999999999995, 'J_D': 607.0468380596636, 'W_D_1KI': 101.16286644951138, 'J_D_1KI': 329.5207376205583}
+[18.87, 18.66, 18.79, 18.47, 22.55, 19.06, 18.88, 19.73, 18.81, 19.09]
+[53.74]
+17.908247232437134
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 311, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.450198650360107, 'TIME_S_1KI': 33.60192492077205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 962.3892062711716, 'W': 53.74}
+[18.87, 18.66, 18.79, 18.47, 22.55, 19.06, 18.88, 19.73, 18.81, 19.09, 19.09, 18.53, 18.69, 18.99, 18.59, 18.7, 23.26, 18.99, 18.96, 18.96]
+347.665
+17.38325
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 311, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.450198650360107, 'TIME_S_1KI': 33.60192492077205, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 962.3892062711716, 'W': 53.74, 'J_1KI': 3094.499055534314, 'W_1KI': 172.79742765273312, 'W_D': 36.356750000000005, 'J_D': 651.0856675679089, 'W_D_1KI': 116.90273311897107, 'J_D_1KI': 375.8930325368845}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json
index ca05b98..cd4e257 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 355542, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.588425874710083, "TIME_S_1KI": 0.02978108317641821, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 655.0935684347153, "W": 46.57, "J_1KI": 1.8425209073322286, "W_1KI": 0.13098311873140162, "W_D": 30.1475, "J_D": 424.08059597134593, "W_D_1KI": 0.08479307648604104, "J_D_1KI": 0.00023848962003375422}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 342879, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.04533338546753, "TIME_S_1KI": 0.029297021355835527, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 722.5925984573364, "W": 52.32, "J_1KI": 2.1074273970040056, "W_1KI": 0.1525902723701364, "W_D": 35.464, "J_D": 489.7940350093841, "W_D_1KI": 0.10343007299951294, "J_D_1KI": 0.0003016518159453129}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.output
index a05082b..f9f4749 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,75 +1,156 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.022435426712036133}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.02335357666015625}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   1,  ..., 250, 250, 250]),
+       col_indices=tensor([2710, 2160, 3133, 4709,  949, 3472,   83, 3331, 4918,
+                            479, 2667, 1823, 2540, 2203, 4266, 1022, 2487, 4425,
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+                           4201, 4066, 1516, 2170, 1490, 2228, 4329]),
+       values=tensor([0.3374, 0.6348, 0.3296, 0.2038, 0.9495, 0.0396, 0.7791,
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+                      0.9827, 0.5203, 0.5278, 0.1121, 0.0156, 0.3815, 0.1310,
+                      0.5952, 0.6956, 0.2005, 0.0296, 0.8936, 0.9754, 0.1470,
+                      0.1604, 0.2962, 0.2133, 0.1761, 0.2122, 0.5494, 0.4118,
+                      0.2219, 0.5058, 0.7503, 0.5251, 0.7282, 0.4731, 0.1995,
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+                      0.3201, 0.9129, 0.8076, 0.7566, 0.0207, 0.1460, 0.6827,
+                      0.6923, 0.9383, 0.3672, 0.5984, 0.7063]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.9273, 0.9180, 0.5847,  ..., 0.9859, 0.6052, 0.3197])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.02335357666015625 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '44960', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.3768103122711182}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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-       values=tensor([0.9106, 0.6103, 0.8018, 0.6726, 0.7831, 0.8787, 0.8641,
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        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.9677, 0.1967, 0.0087,  ..., 0.2565, 0.6584, 0.6200])
+tensor([0.2273, 0.2120, 0.4132,  ..., 0.3100, 0.5228, 0.5364])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -77,161 +158,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.022435426712036133 seconds
+Time: 1.3768103122711182 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '46800', '-ss', '5000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.382112741470337}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 249, 250]),
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-       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.9505, 0.7309, 0.0755,  ..., 0.0952, 0.7737, 0.4268])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 250
-Density: 1e-05
-Time: 1.382112741470337 seconds
-
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '355542', '-ss', '5000', '-sd', '1e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.588425874710083}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '342879', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.04533338546753}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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+                      0.4807, 0.7608, 0.5737, 0.6535, 0.6062, 0.6670, 0.9518,
+                      0.4707, 0.2159, 0.3091, 0.0059, 0.3200, 0.6281, 0.6848,
+                      0.8189, 0.5681, 0.1616, 0.4337, 0.0591, 0.4153, 0.3459,
+                      0.0806, 0.2291, 0.7137, 0.1226, 0.6568, 0.0402, 0.6645,
+                      0.9000, 0.8252, 0.8737, 0.6408, 0.6140, 0.8365, 0.4012,
+                      0.6444, 0.2495, 0.0060, 0.6593, 0.0043, 0.6351, 0.0409,
+                      0.1464, 0.8214, 0.3535, 0.5030, 0.0812, 0.0902, 0.8829,
+                      0.4683, 0.2207, 0.1262, 0.7779, 0.0511, 0.7906, 0.3973,
+                      0.7980, 0.2539, 0.7452, 0.3440, 0.2654, 0.5777, 0.0836,
+                      0.0368, 0.9409, 0.6606, 0.7975, 0.5896, 0.3277, 0.9890,
+                      0.2059, 0.0489, 0.8196, 0.6400, 0.2541, 0.4423, 0.1987,
+                      0.6675, 0.9146, 0.3379, 0.5478, 0.3857, 0.3688, 0.5594,
+                      0.8086, 0.7778, 0.7407, 0.0158, 0.8002, 0.4253, 0.3126,
+                      0.5825, 0.1509, 0.9435, 0.1455, 0.8791, 0.0371, 0.8052,
+                      0.2046, 0.3499, 0.0013, 0.3831, 0.4555, 0.8500, 0.6225,
+                      0.6224, 0.1592, 0.1973, 0.8511, 0.9821, 0.3558, 0.1238,
+                      0.7911, 0.4500, 0.2380, 0.1023, 0.2318, 0.7731, 0.8252,
+                      0.1892, 0.6338, 0.8715, 0.5299, 0.5117, 0.3313, 0.7155,
+                      0.0565, 0.8656, 0.6506, 0.4660, 0.3607, 0.2434, 0.6528,
+                      0.7251, 0.0304, 0.4245, 0.6984, 0.1343, 0.4112, 0.2836,
+                      0.5980, 0.1947, 0.6220, 0.4071, 0.3919, 0.8165, 0.3254,
+                      0.6610, 0.4542, 0.2660, 0.3119, 0.4452, 0.7801, 0.6988,
+                      0.9613, 0.3394, 0.8118, 0.2309, 0.2364, 0.6556, 0.3089,
+                      0.9798, 0.3639, 0.5008, 0.0230, 0.8226]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.4269, 0.0147, 0.1392,  ..., 0.1737, 0.9746, 0.9710])
+tensor([0.2093, 0.5163, 0.8343,  ..., 0.9510, 0.0319, 0.6814])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -239,77 +239,77 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.588425874710083 seconds
+Time: 10.04533338546753 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([4976, 2999, 3531,  729, 4293, 4703,  527,  519, 1087,
-                           2588, 2503, 4621,  623, 2508, 4018, 2181, 4566,  129,
-                           1882,  141,  241, 4234, 3011, 3475, 1507, 1753, 1592,
-                             16, 1837, 2881, 4935, 1126, 1212, 1843, 3769, 1406,
-                           4716, 3662, 2501, 4960, 3438, 3187,  281, 2582, 4408,
-                           1833, 1310, 2092, 1021, 4933, 3776, 3565, 3765, 4067,
-                           3036, 4650, 3192, 1618, 3142,  599, 3093, 1420, 3804,
-                            496, 2492, 4465, 4733, 3170, 4826, 4434, 3018,  360,
-                           3823, 3346, 2952, 3585, 1580, 4171, 3383, 4158, 1729,
-                           4780,  335, 3992, 4542, 2789, 4029,  416, 4212,  913,
-                            792, 2949, 3671, 2278, 3431, 4080, 1970, 3530, 1625,
-                           1846,  123, 3801,  179, 2650, 2793, 3609, 4951, 1904,
-                           3327, 3068,  797, 2882, 3831, 1946, 4674, 2611, 3167,
-                           3799,  820, 4595, 2920, 4476,  339,   16, 1057, 3858,
-                           1996, 2480,  211,  316, 2763, 1963, 1855,  789, 4091,
-                           3327, 1560, 1579, 4983,  674, 1259, 1259, 1614, 4040,
-                           4463, 3393,  619, 1723, 4630,  933, 1161, 3845, 4268,
-                           4840, 4044, 3897, 1211, 4242,  540,  433, 3144, 2336,
-                           4751,  151, 2282, 2250, 2548, 3515,  851, 1606, 4107,
-                            209,  996, 4881, 3062, 1226,  876, 3179,  583, 2155,
-                            304, 1823,   35,  692, 3702, 4106, 1336, 1789, 1359,
-                           3986, 1095,  312, 3819,  955, 1411, 4676, 1229, 4251,
-                            263, 3639, 4785, 3043, 1158, 1138, 2259, 2515, 3611,
-                           4898, 3676,  269,  509, 3101, 1147,  409, 3191, 1984,
-                           2546, 1127, 3710, 4610, 4906,  997, 3399, 3628,  347,
-                            847, 2034,  588, 2961, 2738, 4337, 3827, 1890, 1227,
-                           1635, 1728, 2759, 1429, 4176, 4191, 4862, 4968, 1336,
-                            119, 3406,  554,  532, 1507, 1917, 2750]),
-       values=tensor([0.5199, 0.0559, 0.9006, 0.7777, 0.2843, 0.6207, 0.6975,
-                      0.8689, 0.5742, 0.4737, 0.6755, 0.5958, 0.4384, 0.4390,
-                      0.8599, 0.7635, 0.7028, 0.4511, 0.3241, 0.0806, 0.8242,
-                      0.9053, 0.3849, 0.6596, 0.0472, 0.0762, 0.5653, 0.2663,
-                      0.8433, 0.4091, 0.7837, 0.1469, 0.0020, 0.4944, 0.8738,
-                      0.1320, 0.2431, 0.2650, 0.7193, 0.2483, 0.4028, 0.5091,
-                      0.3427, 0.6757, 0.9747, 0.5603, 0.1148, 0.1932, 0.5560,
-                      0.7920, 0.7790, 0.7814, 0.5929, 0.3079, 0.4008, 0.4233,
-                      0.4893, 0.9629, 0.6575, 0.2210, 0.9465, 0.6464, 0.0795,
-                      0.6735, 0.0196, 0.9297, 0.4843, 0.7360, 0.6757, 0.8217,
-                      0.8318, 0.9782, 0.6754, 0.1098, 0.6940, 0.8548, 0.6163,
-                      0.6934, 0.6171, 0.1041, 0.7511, 0.4878, 0.3635, 0.8620,
-                      0.3905, 0.3542, 0.8549, 0.4722, 0.9206, 0.2789, 0.9568,
-                      0.1952, 0.7173, 0.8016, 0.5436, 0.6025, 0.2628, 0.4776,
-                      0.0648, 0.4119, 0.1873, 0.5768, 0.6229, 0.4612, 0.3781,
-                      0.9685, 0.2051, 0.1702, 0.4419, 0.4538, 0.4515, 0.1665,
-                      0.0292, 0.6419, 0.1217, 0.0198, 0.2563, 0.3495, 0.0936,
-                      0.2664, 0.5259, 0.3038, 0.7533, 0.2522, 0.7253, 0.1463,
-                      0.2087, 0.7896, 0.0364, 0.6152, 0.7986, 0.8112, 0.7556,
-                      0.0756, 0.4969, 0.3790, 0.1978, 0.1043, 0.8605, 0.5011,
-                      0.8762, 0.2381, 0.0660, 0.2738, 0.0392, 0.5623, 0.1390,
-                      0.4257, 0.6102, 0.9603, 0.2834, 0.0200, 0.2511, 0.8990,
-                      0.6324, 0.9167, 0.3730, 0.6817, 0.4652, 0.8996, 0.9136,
-                      0.8631, 0.9770, 0.2565, 0.6496, 0.1799, 0.4018, 0.4388,
-                      0.9314, 0.6833, 0.7914, 0.9455, 0.7655, 0.3170, 0.6697,
-                      0.9757, 0.4151, 0.9576, 0.1567, 0.8861, 0.0170, 0.7822,
-                      0.2221, 0.7184, 0.0581, 0.4824, 0.4815, 0.2522, 0.1559,
-                      0.1892, 0.1146, 0.0347, 0.4792, 0.0555, 0.6999, 0.5092,
-                      0.0919, 0.7115, 0.9111, 0.4498, 0.4923, 0.5300, 0.3126,
-                      0.8103, 0.3276, 0.8722, 0.4823, 0.2311, 0.5957, 0.0760,
-                      0.2892, 0.3555, 0.6604, 0.7686, 0.1412, 0.8595, 0.6702,
-                      0.1119, 0.1550, 0.8493, 0.8158, 0.3714, 0.8983, 0.3484,
-                      0.6611, 0.8110, 0.2241, 0.8267, 0.9605, 0.8151, 0.1779,
-                      0.2906, 0.1723, 0.3272, 0.4678, 0.1292, 0.9514, 0.6369,
-                      0.8054, 0.2983, 0.3742, 0.8673, 0.0274, 0.1851, 0.9052,
-                      0.8742, 0.4529, 0.2266, 0.4410, 0.5323]),
+       col_indices=tensor([4605, 1547, 1940, 3116, 2937, 2706, 1736, 4590, 1984,
+                           2418, 4294, 1059, 4879,  701, 3966, 2712, 4154, 1114,
+                            643, 4965,  781,  809, 3920, 4971, 2063, 1008, 1888,
+                           3571, 4371, 3913, 4103,  468, 4891, 2723, 3269, 2415,
+                           1604, 4901,   82,  814, 1999, 4143,  389, 1694,  665,
+                           4790, 3289,  297, 2848, 1969, 2805, 4443, 2510, 4704,
+                           1612, 1956,  961, 4927, 1995, 3037,  403, 1323, 3155,
+                           4566, 3383,  578,  736,  615, 2612, 4668, 1149, 4775,
+                           1429, 2510, 1093, 1946,  577,  522,   93,   80, 3498,
+                           3358,  983, 3198,   35, 3731, 4938, 3962,  636, 4035,
+                            659, 3478,  885, 4027, 2720, 2665,  771, 4886, 4146,
+                           3590, 2314,  550, 4103, 3341, 4473,  144, 1991, 2528,
+                            752,  261,  407, 3849, 4430,   77,  722, 2153, 2224,
+                           3142,  928, 1372, 1752, 1235, 4622, 3743, 1753, 4466,
+                           1018, 3257, 2825, 4103, 2969, 4873,  999,  938, 2587,
+                           3463, 4648, 2082, 1541,  836, 2578, 2075,  198,  826,
+                           1293, 1279, 4568, 3300, 1029,  931, 3504, 4115, 2829,
+                           1302, 2548,  300, 1991, 1334, 4955, 3119, 3649, 3694,
+                           4971,  942, 3920,  109, 1962, 1974, 4669, 3781, 3767,
+                           4554,  657, 2815, 4738, 2265, 1327,  733, 1170,  228,
+                           1195, 4791,  795, 1568, 2682, 4095, 3229, 2196,  439,
+                           2358, 1755, 2280, 1622, 4409, 2991, 1066, 1285, 2146,
+                           2092, 2573,  494,  846, 4228,  673, 4842, 3039, 3446,
+                             76,  839, 4937,  666, 1575, 1119, 4849, 2027,  412,
+                           3479, 2588, 2558, 1192, 1350, 2789, 2518, 2643, 2931,
+                           3603, 1297, 1277, 2346, 1523, 2606, 1583, 2948,  161,
+                           2409, 2148,  916, 1259, 4838, 3256, 2192, 3896,   29,
+                            394, 3591, 1309, 3907,   32, 3572, 3318]),
+       values=tensor([0.7980, 0.7333, 0.2915, 0.1833, 0.2207, 0.6598, 0.5147,
+                      0.5171, 0.5565, 0.0943, 0.5992, 0.8056, 0.1807, 0.1878,
+                      0.3625, 0.5930, 0.3092, 0.8760, 0.7030, 0.7627, 0.6150,
+                      0.4252, 0.1237, 0.9391, 0.0430, 0.8144, 0.9298, 0.8419,
+                      0.9282, 0.1145, 0.4201, 0.2592, 0.8076, 0.6482, 0.7684,
+                      0.0799, 0.3357, 0.2778, 0.6719, 0.4215, 0.6579, 0.8622,
+                      0.5198, 0.3042, 0.4639, 0.7357, 0.8400, 0.2047, 0.3583,
+                      0.9300, 0.5889, 0.9962, 0.4764, 0.1332, 0.0521, 0.4130,
+                      0.8482, 0.1381, 0.2425, 0.3904, 0.8170, 0.0142, 0.6421,
+                      0.1364, 0.3943, 0.8466, 0.4358, 0.4268, 0.3839, 0.5195,
+                      0.9398, 0.6091, 0.3110, 0.0490, 0.6042, 0.3749, 0.6524,
+                      0.6884, 0.7351, 0.4220, 0.6301, 0.3234, 0.8180, 0.0494,
+                      0.4807, 0.7608, 0.5737, 0.6535, 0.6062, 0.6670, 0.9518,
+                      0.4707, 0.2159, 0.3091, 0.0059, 0.3200, 0.6281, 0.6848,
+                      0.8189, 0.5681, 0.1616, 0.4337, 0.0591, 0.4153, 0.3459,
+                      0.0806, 0.2291, 0.7137, 0.1226, 0.6568, 0.0402, 0.6645,
+                      0.9000, 0.8252, 0.8737, 0.6408, 0.6140, 0.8365, 0.4012,
+                      0.6444, 0.2495, 0.0060, 0.6593, 0.0043, 0.6351, 0.0409,
+                      0.1464, 0.8214, 0.3535, 0.5030, 0.0812, 0.0902, 0.8829,
+                      0.4683, 0.2207, 0.1262, 0.7779, 0.0511, 0.7906, 0.3973,
+                      0.7980, 0.2539, 0.7452, 0.3440, 0.2654, 0.5777, 0.0836,
+                      0.0368, 0.9409, 0.6606, 0.7975, 0.5896, 0.3277, 0.9890,
+                      0.2059, 0.0489, 0.8196, 0.6400, 0.2541, 0.4423, 0.1987,
+                      0.6675, 0.9146, 0.3379, 0.5478, 0.3857, 0.3688, 0.5594,
+                      0.8086, 0.7778, 0.7407, 0.0158, 0.8002, 0.4253, 0.3126,
+                      0.5825, 0.1509, 0.9435, 0.1455, 0.8791, 0.0371, 0.8052,
+                      0.2046, 0.3499, 0.0013, 0.3831, 0.4555, 0.8500, 0.6225,
+                      0.6224, 0.1592, 0.1973, 0.8511, 0.9821, 0.3558, 0.1238,
+                      0.7911, 0.4500, 0.2380, 0.1023, 0.2318, 0.7731, 0.8252,
+                      0.1892, 0.6338, 0.8715, 0.5299, 0.5117, 0.3313, 0.7155,
+                      0.0565, 0.8656, 0.6506, 0.4660, 0.3607, 0.2434, 0.6528,
+                      0.7251, 0.0304, 0.4245, 0.6984, 0.1343, 0.4112, 0.2836,
+                      0.5980, 0.1947, 0.6220, 0.4071, 0.3919, 0.8165, 0.3254,
+                      0.6610, 0.4542, 0.2660, 0.3119, 0.4452, 0.7801, 0.6988,
+                      0.9613, 0.3394, 0.8118, 0.2309, 0.2364, 0.6556, 0.3089,
+                      0.9798, 0.3639, 0.5008, 0.0230, 0.8226]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.4269, 0.0147, 0.1392,  ..., 0.1737, 0.9746, 0.9710])
+tensor([0.2093, 0.5163, 0.8343,  ..., 0.9510, 0.0319, 0.6814])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -317,13 +317,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.588425874710083 seconds
+Time: 10.04533338546753 seconds
 
-[18.34, 17.96, 17.95, 19.17, 18.44, 18.12, 18.19, 19.28, 18.14, 18.1]
-[46.57]
-14.066857814788818
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 355542, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.588425874710083, 'TIME_S_1KI': 0.02978108317641821, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 655.0935684347153, 'W': 46.57}
-[18.34, 17.96, 17.95, 19.17, 18.44, 18.12, 18.19, 19.28, 18.14, 18.1, 18.29, 18.16, 18.13, 17.89, 18.15, 18.08, 18.32, 17.91, 18.06, 18.27]
-328.45
-16.4225
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 355542, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.588425874710083, 'TIME_S_1KI': 0.02978108317641821, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 655.0935684347153, 'W': 46.57, 'J_1KI': 1.8425209073322286, 'W_1KI': 0.13098311873140162, 'W_D': 30.1475, 'J_D': 424.08059597134593, 'W_D_1KI': 0.08479307648604104, 'J_D_1KI': 0.00023848962003375422}
+[19.7, 18.48, 18.66, 18.83, 18.82, 18.51, 18.76, 18.43, 19.01, 18.43]
+[52.32]
+13.811020612716675
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 342879, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.04533338546753, 'TIME_S_1KI': 0.029297021355835527, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 722.5925984573364, 'W': 52.32}
+[19.7, 18.48, 18.66, 18.83, 18.82, 18.51, 18.76, 18.43, 19.01, 18.43, 18.98, 18.69, 19.12, 18.51, 18.64, 18.66, 18.98, 18.52, 18.59, 18.71]
+337.12
+16.856
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 342879, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.04533338546753, 'TIME_S_1KI': 0.029297021355835527, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 722.5925984573364, 'W': 52.32, 'J_1KI': 2.1074273970040056, 'W_1KI': 0.1525902723701364, 'W_D': 35.464, 'J_D': 489.7940350093841, 'W_D_1KI': 0.10343007299951294, 'J_D_1KI': 0.0003016518159453129}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.json
index 0d245c9..24cd4aa 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.json
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 303638, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.314902782440186, "TIME_S_1KI": 0.033971053631100805, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 652.3264142632485, "W": 46.59, "J_1KI": 2.148368828220606, "W_1KI": 0.1534392928421344, "W_D": 30.048000000000002, "J_D": 420.71483356475835, "W_D_1KI": 0.0989599457248434, "J_D_1KI": 0.0003259142324901475}
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 308095, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.368000268936157, "TIME_S_1KI": 0.03365195887286764, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 744.0538631057739, "W": 52.44, "J_1KI": 2.4150144049912328, "W_1KI": 0.17020724127298398, "W_D": 35.279, "J_D": 500.56209451770786, "W_D_1KI": 0.11450688910887877, "J_D_1KI": 0.00037166097829850785}
diff --git a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.output
index 2579376..27c2f2a 100644
--- a/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.output
+++ b/pytorch/output_synthetic_1core/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.018550395965576172}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.019425392150878906}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1250, 1250]),
-       col_indices=tensor([2148, 2186, 2653,  ..., 4713,  108, 1050]),
-       values=tensor([0.2240, 0.7824, 0.4591,  ..., 0.0832, 0.2125, 0.9204]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([1829, 4982,  735,  ..., 4930, 1039, 2162]),
+       values=tensor([0.6306, 0.5443, 0.9048,  ..., 0.9471, 0.6437, 0.9353]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.9989, 0.0104, 0.9920,  ..., 0.8791, 0.9452, 0.9082])
+tensor([0.8363, 0.8481, 0.1501,  ..., 0.0482, 0.5984, 0.9282])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 0.018550395965576172 seconds
+Time: 0.019425392150878906 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '56602', '-ss', '5000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.9573328495025635}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '54052', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.8421094417572021}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    2,    2,  ..., 1249, 1250, 1250]),
-       col_indices=tensor([ 143, 2910, 3407,  ...,  360, 1598, 1598]),
-       values=tensor([0.9185, 0.3997, 0.2489,  ..., 0.0567, 0.3179, 0.0072]),
+tensor(crow_indices=tensor([   0,    2,    2,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([   6,  277, 1426,  ..., 3500, 4127, 1110]),
+       values=tensor([0.4840, 0.0118, 0.8124,  ..., 0.4395, 0.7635, 0.9699]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.5677, 0.2086, 0.3083,  ..., 0.5984, 0.8633, 0.3307])
+tensor([0.7095, 0.2973, 0.7856,  ..., 0.4374, 0.4236, 0.1506])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 1.9573328495025635 seconds
+Time: 1.8421094417572021 seconds
 
-['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '303638', '-ss', '5000', '-sd', '5e-05', '-c', '1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.314902782440186}
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '308095', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.368000268936157}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([4078,   67, 3564,  ..., 1146, 2529, 4353]),
-       values=tensor([0.7614, 0.2386, 0.2238,  ..., 0.8351, 0.5866, 0.5164]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([2390, 3281, 4702,  ..., 2980,  290, 2178]),
+       values=tensor([0.2443, 0.2640, 0.7541,  ..., 0.7133, 0.6253, 0.6379]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.2149, 0.3426, 0.8577,  ..., 0.2235, 0.1553, 0.5182])
+tensor([0.2175, 0.8851, 0.1665,  ..., 0.6673, 0.5931, 0.2307])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.314902782440186 seconds
+Time: 10.368000268936157 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([4078,   67, 3564,  ..., 1146, 2529, 4353]),
-       values=tensor([0.7614, 0.2386, 0.2238,  ..., 0.8351, 0.5866, 0.5164]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([2390, 3281, 4702,  ..., 2980,  290, 2178]),
+       values=tensor([0.2443, 0.2640, 0.7541,  ..., 0.7133, 0.6253, 0.6379]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.2149, 0.3426, 0.8577,  ..., 0.2235, 0.1553, 0.5182])
+tensor([0.2175, 0.8851, 0.1665,  ..., 0.6673, 0.5931, 0.2307])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.314902782440186 seconds
+Time: 10.368000268936157 seconds
 
-[18.14, 22.27, 18.32, 18.01, 17.87, 18.05, 18.11, 18.01, 18.55, 17.98]
-[46.59]
-14.001425504684448
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 303638, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.314902782440186, 'TIME_S_1KI': 0.033971053631100805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 652.3264142632485, 'W': 46.59}
-[18.14, 22.27, 18.32, 18.01, 17.87, 18.05, 18.11, 18.01, 18.55, 17.98, 18.58, 17.96, 18.17, 18.69, 18.11, 18.0, 18.22, 18.0, 18.06, 18.18]
-330.84000000000003
-16.542
-{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 303638, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.314902782440186, 'TIME_S_1KI': 0.033971053631100805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 652.3264142632485, 'W': 46.59, 'J_1KI': 2.148368828220606, 'W_1KI': 0.1534392928421344, 'W_D': 30.048000000000002, 'J_D': 420.71483356475835, 'W_D_1KI': 0.0989599457248434, 'J_D_1KI': 0.0003259142324901475}
+[18.94, 19.01, 18.85, 18.93, 18.48, 18.4, 18.7, 18.61, 18.8, 18.72]
+[52.44]
+14.18867015838623
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 308095, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.368000268936157, 'TIME_S_1KI': 0.03365195887286764, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 744.0538631057739, 'W': 52.44}
+[18.94, 19.01, 18.85, 18.93, 18.48, 18.4, 18.7, 18.61, 18.8, 18.72, 19.02, 18.82, 18.84, 18.45, 19.68, 22.53, 19.83, 18.49, 19.11, 18.7]
+343.2199999999999
+17.160999999999994
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 308095, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.368000268936157, 'TIME_S_1KI': 0.03365195887286764, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 744.0538631057739, 'W': 52.44, 'J_1KI': 2.4150144049912328, 'W_1KI': 0.17020724127298398, 'W_D': 35.279, 'J_D': 500.56209451770786, 'W_D_1KI': 0.11450688910887877, 'J_D_1KI': 0.00037166097829850785}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..1bdafea
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 426, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.062527418136597, "TIME_S_1KI": 23.62095638060234, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 310.93658995628357, "W": 22.910177826692795, "J_1KI": 729.8980984889287, "W_1KI": 53.77976015655585, "W_D": 4.4831778266927955, "J_D": 60.845622244596484, "W_D_1KI": 10.52389161195492, "J_D_1KI": 24.70397092008197}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..6496241
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.4617955684661865}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      14,      23,  ...,  999981,
+                             999993, 1000000]),
+       col_indices=tensor([ 4955,  8657, 25975,  ..., 77712, 83219, 89598]),
+       values=tensor([0.6839, 0.0631, 0.2295,  ..., 0.4308, 0.9509, 0.3745]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8318, 0.0587, 0.7825,  ..., 0.4905, 0.7506, 0.0148])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 2.4617955684661865 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 426 -ss 100000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.062527418136597}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      16,  ...,  999978,
+                             999987, 1000000]),
+       col_indices=tensor([ 4266, 12843, 25231,  ..., 84479, 87700, 95752]),
+       values=tensor([0.9986, 0.4680, 0.6719,  ..., 0.1198, 0.1607, 0.3222]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1538, 0.6601, 0.2448,  ..., 0.8405, 0.0282, 0.7640])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.062527418136597 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      16,  ...,  999978,
+                             999987, 1000000]),
+       col_indices=tensor([ 4266, 12843, 25231,  ..., 84479, 87700, 95752]),
+       values=tensor([0.9986, 0.4680, 0.6719,  ..., 0.1198, 0.1607, 0.3222]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1538, 0.6601, 0.2448,  ..., 0.8405, 0.0282, 0.7640])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.062527418136597 seconds
+
+[20.6, 20.44, 20.32, 20.4, 20.44, 20.84, 21.0, 20.88, 20.52, 20.4]
+[20.4, 20.24, 20.28, 24.24, 25.56, 27.32, 28.24, 28.56, 25.84, 24.76, 24.56, 24.68, 24.68]
+13.571985006332397
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 426, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.062527418136597, 'TIME_S_1KI': 23.62095638060234, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.93658995628357, 'W': 22.910177826692795}
+[20.6, 20.44, 20.32, 20.4, 20.44, 20.84, 21.0, 20.88, 20.52, 20.4, 20.64, 20.56, 20.4, 20.2, 20.36, 20.36, 20.12, 20.2, 20.4, 20.56]
+368.53999999999996
+18.427
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 426, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.062527418136597, 'TIME_S_1KI': 23.62095638060234, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 310.93658995628357, 'W': 22.910177826692795, 'J_1KI': 729.8980984889287, 'W_1KI': 53.77976015655585, 'W_D': 4.4831778266927955, 'J_D': 60.845622244596484, 'W_D_1KI': 10.52389161195492, 'J_D_1KI': 24.70397092008197}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..9db277a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 22.583206176757812, "TIME_S_1KI": 225.83206176757812, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 712.6889356327057, "W": 23.468990304831248, "J_1KI": 7126.889356327057, "W_1KI": 234.68990304831246, "W_D": 4.866990304831251, "J_D": 147.7971610636712, "W_D_1KI": 48.66990304831251, "J_D_1KI": 486.6990304831251}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..1c6b2fe
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 22.583206176757812}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      115,      205,  ...,  9999778,
+                             9999875, 10000000]),
+       col_indices=tensor([ 1402,  2097,  3965,  ..., 98532, 99293, 99429]),
+       values=tensor([0.3375, 0.2900, 0.6603,  ..., 0.1611, 0.9536, 0.6072]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8425, 0.9618, 0.5102,  ..., 0.7524, 0.4133, 0.9192])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 22.583206176757812 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      115,      205,  ...,  9999778,
+                             9999875, 10000000]),
+       col_indices=tensor([ 1402,  2097,  3965,  ..., 98532, 99293, 99429]),
+       values=tensor([0.3375, 0.2900, 0.6603,  ..., 0.1611, 0.9536, 0.6072]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8425, 0.9618, 0.5102,  ..., 0.7524, 0.4133, 0.9192])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 22.583206176757812 seconds
+
+[20.72, 21.0, 21.0, 21.04, 20.76, 20.44, 20.16, 20.32, 20.24, 20.52]
+[20.4, 20.36, 21.96, 22.92, 24.92, 26.56, 28.48, 28.48, 28.88, 28.96, 27.92, 26.48, 25.56, 24.72, 24.64, 24.76, 24.96, 24.76, 24.6, 24.56, 24.6, 24.36, 24.76, 24.88, 24.92, 24.68, 24.6, 24.36, 24.04]
+30.367260217666626
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 22.583206176757812, 'TIME_S_1KI': 225.83206176757812, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 712.6889356327057, 'W': 23.468990304831248}
+[20.72, 21.0, 21.0, 21.04, 20.76, 20.44, 20.16, 20.32, 20.24, 20.52, 20.24, 20.24, 20.2, 20.4, 20.64, 21.08, 21.24, 21.12, 21.0, 20.84]
+372.03999999999996
+18.601999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 22.583206176757812, 'TIME_S_1KI': 225.83206176757812, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 712.6889356327057, 'W': 23.468990304831248, 'J_1KI': 7126.889356327057, 'W_1KI': 234.68990304831246, 'W_D': 4.866990304831251, 'J_D': 147.7971610636712, 'W_D_1KI': 48.66990304831251, 'J_D_1KI': 486.6990304831251}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..21e4694
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3104, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.44502592086792, "TIME_S_1KI": 3.3650212373930155, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 348.514190454483, "W": 23.934119588627873, "J_1KI": 112.27905620311952, "W_1KI": 7.710734403552794, "W_D": 5.569119588627871, "J_D": 81.09415505290025, "W_D_1KI": 1.794175125202278, "J_D_1KI": 0.578020336727538}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..9aacb6b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.3382716178894043}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      4,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([91034, 37166, 45389,  ..., 40200, 40353,   102]),
+       values=tensor([0.2917, 0.4189, 0.5553,  ..., 0.7170, 0.1120, 0.1885]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2165, 0.9661, 0.1946,  ..., 0.3640, 0.8184, 0.1773])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.3382716178894043 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3104 -ss 100000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.44502592086792}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([  252, 18132, 64781,  ..., 90653, 85542, 48452]),
+       values=tensor([0.2676, 0.4026, 0.9927,  ..., 0.1189, 0.3190, 0.1177]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6783, 0.3478, 0.4100,  ..., 0.2741, 0.0736, 0.8098])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.44502592086792 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([  252, 18132, 64781,  ..., 90653, 85542, 48452]),
+       values=tensor([0.2676, 0.4026, 0.9927,  ..., 0.1189, 0.3190, 0.1177]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6783, 0.3478, 0.4100,  ..., 0.2741, 0.0736, 0.8098])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.44502592086792 seconds
+
+[20.36, 20.36, 20.36, 20.36, 20.36, 20.52, 20.6, 20.76, 20.96, 20.92]
+[20.92, 20.6, 23.92, 25.4, 27.24, 28.24, 28.24, 29.08, 26.32, 25.76, 25.4, 25.2, 25.44, 25.64]
+14.56139588356018
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3104, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.44502592086792, 'TIME_S_1KI': 3.3650212373930155, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.514190454483, 'W': 23.934119588627873}
+[20.36, 20.36, 20.36, 20.36, 20.36, 20.52, 20.6, 20.76, 20.96, 20.92, 20.24, 20.2, 20.48, 20.2, 20.2, 20.2, 20.12, 20.28, 20.28, 20.6]
+367.30000000000007
+18.365000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3104, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.44502592086792, 'TIME_S_1KI': 3.3650212373930155, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 348.514190454483, 'W': 23.934119588627873, 'J_1KI': 112.27905620311952, 'W_1KI': 7.710734403552794, 'W_D': 5.569119588627871, 'J_D': 81.09415505290025, 'W_D_1KI': 1.794175125202278, 'J_D_1KI': 0.578020336727538}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..9e7c05a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 868, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.049697399139404, "TIME_S_1KI": 12.730066128040788, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 347.2841278934479, "W": 23.818433138573972, "J_1KI": 400.0969215362303, "W_1KI": 27.44059117347232, "W_D": 5.198433138573975, "J_D": 75.79563728809362, "W_D_1KI": 5.988978270246515, "J_D_1KI": 6.899744550975249}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..5c6adba
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.209580421447754}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,     11,  ..., 499991, 499997,
+                            500000]),
+       col_indices=tensor([ 8709, 33303, 39829,  ..., 65447, 85964, 93697]),
+       values=tensor([0.2765, 0.3303, 0.4846,  ..., 0.1571, 0.7749, 0.0327]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.2847, 0.7446, 0.1507,  ..., 0.7274, 0.5755, 0.0187])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 1.209580421447754 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 868 -ss 100000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.049697399139404}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     19,  ..., 499985, 499993,
+                            500000]),
+       col_indices=tensor([  930,  5720, 18229,  ..., 18263, 29630, 53753]),
+       values=tensor([0.0983, 0.1468, 0.4729,  ..., 0.5988, 0.3077, 0.5585]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.4408, 0.1732, 0.5273,  ..., 0.8772, 0.6136, 0.9894])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 11.049697399139404 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     19,  ..., 499985, 499993,
+                            500000]),
+       col_indices=tensor([  930,  5720, 18229,  ..., 18263, 29630, 53753]),
+       values=tensor([0.0983, 0.1468, 0.4729,  ..., 0.5988, 0.3077, 0.5585]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.4408, 0.1732, 0.5273,  ..., 0.8772, 0.6136, 0.9894])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 11.049697399139404 seconds
+
+[20.76, 20.76, 20.64, 20.6, 20.48, 20.24, 20.32, 20.36, 20.4, 20.64]
+[20.8, 20.68, 23.8, 24.88, 26.88, 27.84, 27.84, 28.76, 26.08, 26.28, 25.56, 25.36, 25.48, 25.4]
+14.580477476119995
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 868, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.049697399139404, 'TIME_S_1KI': 12.730066128040788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 347.2841278934479, 'W': 23.818433138573972}
+[20.76, 20.76, 20.64, 20.6, 20.48, 20.24, 20.32, 20.36, 20.4, 20.64, 20.68, 20.8, 20.6, 20.6, 20.92, 21.0, 21.0, 21.08, 21.04, 21.04]
+372.4
+18.619999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 868, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.049697399139404, 'TIME_S_1KI': 12.730066128040788, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 347.2841278934479, 'W': 23.818433138573972, 'J_1KI': 400.0969215362303, 'W_1KI': 27.44059117347232, 'W_D': 5.198433138573975, 'J_D': 75.79563728809362, 'W_D_1KI': 5.988978270246515, 'J_D_1KI': 6.899744550975249}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..669cf9a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 32669, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.073116302490234, "TIME_S_1KI": 0.33894873741131454, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 342.2707403564453, "W": 23.410284009847928, "J_1KI": 10.476927373242074, "W_1KI": 0.7165901622286549, "W_D": 4.943284009847925, "J_D": 72.2734280853271, "W_D_1KI": 0.15131421255159097, "J_D_1KI": 0.004631736892821665}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..4a81d8b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.03962588310241699}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9995,  9999, 10000]),
+       col_indices=tensor([5736, 4740, 5169,  ..., 5050, 7314, 6933]),
+       values=tensor([0.2904, 0.9920, 0.0901,  ..., 0.6475, 0.2992, 0.6153]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.6798, 0.4263, 0.2506,  ..., 0.2181, 0.0906, 0.7562])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.03962588310241699 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 26497 -ss 10000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.51607346534729}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9996,  9998, 10000]),
+       col_indices=tensor([5374, 4189, 5897,  ..., 9913, 4567, 8496]),
+       values=tensor([0.8167, 0.6460, 0.7856,  ..., 0.9381, 0.0308, 0.1187]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.9083, 0.0911, 0.6427,  ..., 0.4641, 0.3576, 0.6926])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 8.51607346534729 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32669 -ss 10000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.073116302490234}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     5,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([2638,  262,  675,  ..., 9893, 8606, 4272]),
+       values=tensor([0.0918, 0.5777, 0.8540,  ..., 0.4523, 0.7955, 0.7135]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1165, 0.4058, 0.2834,  ..., 0.7342, 0.6568, 0.2677])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 11.073116302490234 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     5,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([2638,  262,  675,  ..., 9893, 8606, 4272]),
+       values=tensor([0.0918, 0.5777, 0.8540,  ..., 0.4523, 0.7955, 0.7135]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1165, 0.4058, 0.2834,  ..., 0.7342, 0.6568, 0.2677])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 11.073116302490234 seconds
+
+[20.76, 20.6, 20.6, 20.6, 20.44, 20.48, 20.48, 20.48, 20.48, 20.64]
+[20.44, 20.4, 23.56, 25.72, 27.84, 28.36, 29.08, 29.08, 26.36, 24.68, 23.72, 23.68, 23.44, 23.32]
+14.620529174804688
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32669, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.073116302490234, 'TIME_S_1KI': 0.33894873741131454, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.2707403564453, 'W': 23.410284009847928}
+[20.76, 20.6, 20.6, 20.6, 20.44, 20.48, 20.48, 20.48, 20.48, 20.64, 20.6, 20.88, 20.92, 20.76, 20.64, 20.48, 20.16, 20.16, 20.08, 20.2]
+369.34000000000003
+18.467000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 32669, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.073116302490234, 'TIME_S_1KI': 0.33894873741131454, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.2707403564453, 'W': 23.410284009847928, 'J_1KI': 10.476927373242074, 'W_1KI': 0.7165901622286549, 'W_D': 4.943284009847925, 'J_D': 72.2734280853271, 'W_D_1KI': 0.15131421255159097, 'J_D_1KI': 0.004631736892821665}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..3604623
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 4348, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.135539293289185, "TIME_S_1KI": 2.3310807942247433, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 299.55518106460573, "W": 22.140804095253714, "J_1KI": 68.89493584742543, "W_1KI": 5.092181254658168, "W_D": 3.514804095253716, "J_D": 47.55372806835178, "W_D_1KI": 0.8083726070040745, "J_D_1KI": 0.1859182628804219}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..4918d44
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2414851188659668}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     19,  ...,  99982,  99991,
+                            100000]),
+       col_indices=tensor([ 302, 1349, 1385,  ..., 9083, 9115, 9373]),
+       values=tensor([0.3908, 0.9700, 0.7778,  ..., 0.9299, 0.7856, 0.5693]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.9198, 0.1049, 0.3911,  ..., 0.9152, 0.2471, 0.8814])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.2414851188659668 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4348 -ss 10000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.135539293289185}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     23,  ...,  99987,  99990,
+                            100000]),
+       col_indices=tensor([  62,  627, 2703,  ..., 9273, 9381, 9947]),
+       values=tensor([0.4329, 0.2872, 0.8964,  ..., 0.9783, 0.1219, 0.9101]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8278, 0.7584, 0.9132,  ..., 0.6086, 0.4680, 0.0616])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.135539293289185 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     23,  ...,  99987,  99990,
+                            100000]),
+       col_indices=tensor([  62,  627, 2703,  ..., 9273, 9381, 9947]),
+       values=tensor([0.4329, 0.2872, 0.8964,  ..., 0.9783, 0.1219, 0.9101]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8278, 0.7584, 0.9132,  ..., 0.6086, 0.4680, 0.0616])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.135539293289185 seconds
+
+[20.76, 20.76, 21.0, 21.0, 20.84, 20.76, 20.68, 20.44, 20.6, 20.72]
+[20.88, 20.96, 21.56, 22.88, 24.32, 25.12, 25.84, 25.2, 25.2, 24.92, 24.16, 24.04, 24.2]
+13.529552936553955
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4348, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.135539293289185, 'TIME_S_1KI': 2.3310807942247433, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 299.55518106460573, 'W': 22.140804095253714}
+[20.76, 20.76, 21.0, 21.0, 20.84, 20.76, 20.68, 20.44, 20.6, 20.72, 20.52, 20.44, 20.36, 20.68, 20.84, 20.96, 20.92, 20.68, 20.36, 20.4]
+372.52
+18.625999999999998
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 4348, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.135539293289185, 'TIME_S_1KI': 2.3310807942247433, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 299.55518106460573, 'W': 22.140804095253714, 'J_1KI': 68.89493584742543, 'W_1KI': 5.092181254658168, 'W_D': 3.514804095253716, 'J_D': 47.55372806835178, 'W_D_1KI': 0.8083726070040745, 'J_D_1KI': 0.1859182628804219}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..1a09527
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 493, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.442772150039673, "TIME_S_1KI": 21.18209361062814, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 317.52540328025816, "W": 21.825092476629965, "J_1KI": 644.0677551323695, "W_1KI": 44.26996445563888, "W_D": 3.2900924766299617, "J_D": 47.86636948227874, "W_D_1KI": 6.673615571257529, "J_D_1KI": 13.536745580644075}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..449d7aa
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.1293396949768066}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      94,     210,  ...,  999806,
+                             999898, 1000000]),
+       col_indices=tensor([ 197,  225,  349,  ..., 9664, 9718, 9909]),
+       values=tensor([0.2825, 0.4405, 0.0615,  ..., 0.4764, 0.3721, 0.7741]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2003, 0.0291, 0.9415,  ..., 0.2751, 0.8368, 0.8186])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 2.1293396949768066 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 493 -ss 10000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.442772150039673}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     103,     211,  ...,  999799,
+                             999895, 1000000]),
+       col_indices=tensor([  29,  259,  296,  ..., 9649, 9833, 9895]),
+       values=tensor([0.6562, 0.6337, 0.8410,  ..., 0.1779, 0.9179, 0.3279]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2837, 0.1453, 0.4499,  ..., 0.4322, 0.7993, 0.4344])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.442772150039673 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     103,     211,  ...,  999799,
+                             999895, 1000000]),
+       col_indices=tensor([  29,  259,  296,  ..., 9649, 9833, 9895]),
+       values=tensor([0.6562, 0.6337, 0.8410,  ..., 0.1779, 0.9179, 0.3279]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2837, 0.1453, 0.4499,  ..., 0.4322, 0.7993, 0.4344])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.442772150039673 seconds
+
+[20.48, 20.72, 20.88, 20.72, 20.92, 20.68, 20.44, 20.48, 20.4, 20.4]
+[20.48, 20.48, 20.76, 21.64, 23.08, 24.24, 25.0, 25.48, 24.92, 24.24, 24.2, 24.12, 24.0, 23.96]
+14.54863953590393
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 493, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.442772150039673, 'TIME_S_1KI': 21.18209361062814, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 317.52540328025816, 'W': 21.825092476629965}
+[20.48, 20.72, 20.88, 20.72, 20.92, 20.68, 20.44, 20.48, 20.4, 20.4, 20.6, 20.6, 20.36, 20.32, 20.48, 20.48, 20.64, 20.64, 20.8, 20.8]
+370.70000000000005
+18.535000000000004
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 493, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.442772150039673, 'TIME_S_1KI': 21.18209361062814, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 317.52540328025816, 'W': 21.825092476629965, 'J_1KI': 644.0677551323695, 'W_1KI': 44.26996445563888, 'W_D': 3.2900924766299617, 'J_D': 47.86636948227874, 'W_D_1KI': 6.673615571257529, 'J_D_1KI': 13.536745580644075}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..ad31b62
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.699479103088379, "TIME_S_1KI": 106.99479103088379, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 372.40043621063234, "W": 23.7347384855202, "J_1KI": 3724.0043621063232, "W_1KI": 237.34738485520202, "W_D": 5.215738485520202, "J_D": 81.83546190547948, "W_D_1KI": 52.15738485520202, "J_D_1KI": 521.5738485520202}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..f0aea1a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.699479103088379}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     534,    1091,  ..., 4998975,
+                            4999490, 5000000]),
+       col_indices=tensor([   4,   42,   44,  ..., 9941, 9942, 9945]),
+       values=tensor([0.3387, 0.3479, 0.7697,  ..., 0.0992, 0.1573, 0.7910]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.0536, 0.4974, 0.9494,  ..., 0.5617, 0.8582, 0.7161])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.699479103088379 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     534,    1091,  ..., 4998975,
+                            4999490, 5000000]),
+       col_indices=tensor([   4,   42,   44,  ..., 9941, 9942, 9945]),
+       values=tensor([0.3387, 0.3479, 0.7697,  ..., 0.0992, 0.1573, 0.7910]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.0536, 0.4974, 0.9494,  ..., 0.5617, 0.8582, 0.7161])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.699479103088379 seconds
+
+[20.52, 20.4, 20.48, 20.48, 20.48, 20.64, 20.76, 20.8, 20.8, 20.56]
+[20.56, 20.2, 20.28, 23.96, 25.84, 29.28, 30.68, 31.08, 27.28, 26.24, 25.12, 24.52, 24.52, 24.32, 24.2]
+15.69010066986084
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.699479103088379, 'TIME_S_1KI': 106.99479103088379, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 372.40043621063234, 'W': 23.7347384855202}
+[20.52, 20.4, 20.48, 20.48, 20.48, 20.64, 20.76, 20.8, 20.8, 20.56, 20.6, 20.6, 20.6, 20.68, 20.68, 20.68, 20.56, 20.4, 20.28, 20.44]
+370.38
+18.519
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.699479103088379, 'TIME_S_1KI': 106.99479103088379, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 372.40043621063234, 'W': 23.7347384855202, 'J_1KI': 3724.0043621063232, 'W_1KI': 237.34738485520202, 'W_D': 5.215738485520202, 'J_D': 81.83546190547948, 'W_D_1KI': 52.15738485520202, 'J_D_1KI': 521.5738485520202}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..f0b391a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.31538224220276, "TIME_S_1KI": 213.1538224220276, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 656.8145585250853, "W": 23.224732449579832, "J_1KI": 6568.145585250853, "W_1KI": 232.24732449579832, "W_D": 4.7157324495798285, "J_D": 133.36479693436596, "W_D_1KI": 47.157324495798285, "J_D_1KI": 471.57324495798287}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..f5c642e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.1 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.31538224220276}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      941,     1920,  ...,  9998069,
+                             9999051, 10000000]),
+       col_indices=tensor([   4,   12,   19,  ..., 9982, 9986, 9989]),
+       values=tensor([0.3288, 0.1903, 0.7853,  ..., 0.1848, 0.4723, 0.3439]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8250, 0.2999, 0.1337,  ..., 0.5908, 0.0422, 0.7676])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 21.31538224220276 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      941,     1920,  ...,  9998069,
+                             9999051, 10000000]),
+       col_indices=tensor([   4,   12,   19,  ..., 9982, 9986, 9989]),
+       values=tensor([0.3288, 0.1903, 0.7853,  ..., 0.1848, 0.4723, 0.3439]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8250, 0.2999, 0.1337,  ..., 0.5908, 0.0422, 0.7676])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 21.31538224220276 seconds
+
+[20.48, 20.28, 20.4, 20.48, 20.4, 20.6, 20.96, 20.96, 21.04, 20.88]
+[20.92, 20.84, 20.88, 24.28, 26.48, 27.96, 29.4, 28.08, 28.12, 26.52, 25.88, 24.92, 24.44, 24.32, 24.56, 24.84, 24.84, 24.96, 24.88, 24.72, 24.72, 24.48, 24.56, 24.28, 24.16, 24.12, 24.48]
+28.280823469161987
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.31538224220276, 'TIME_S_1KI': 213.1538224220276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 656.8145585250853, 'W': 23.224732449579832}
+[20.48, 20.28, 20.4, 20.48, 20.4, 20.6, 20.96, 20.96, 21.04, 20.88, 20.44, 20.52, 20.68, 20.36, 20.52, 20.4, 20.4, 20.4, 20.52, 20.72]
+370.18000000000006
+18.509000000000004
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.31538224220276, 'TIME_S_1KI': 213.1538224220276, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 656.8145585250853, 'W': 23.224732449579832, 'J_1KI': 6568.145585250853, 'W_1KI': 232.24732449579832, 'W_D': 4.7157324495798285, 'J_D': 133.36479693436596, 'W_D_1KI': 47.157324495798285, 'J_D_1KI': 471.57324495798287}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..6cc4360
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.396695137023926, "TIME_S_1KI": 423.96695137023926, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1244.9685798645019, "W": 23.740989470007165, "J_1KI": 12449.68579864502, "W_1KI": 237.40989470007165, "W_D": 5.275989470007168, "J_D": 276.6709081840517, "W_D_1KI": 52.75989470007168, "J_D_1KI": 527.5989470007169}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..8c655a7
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.2 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.396695137023926}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1981,     3970,  ..., 19995969,
+                            19997989, 20000000]),
+       col_indices=tensor([   3,    4,    9,  ..., 9978, 9982, 9987]),
+       values=tensor([0.8747, 0.4611, 0.0013,  ..., 0.7048, 0.8145, 0.2728]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.1891, 0.5511, 0.0831,  ..., 0.7428, 0.4718, 0.5050])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 42.396695137023926 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1981,     3970,  ..., 19995969,
+                            19997989, 20000000]),
+       col_indices=tensor([   3,    4,    9,  ..., 9978, 9982, 9987]),
+       values=tensor([0.8747, 0.4611, 0.0013,  ..., 0.7048, 0.8145, 0.2728]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.1891, 0.5511, 0.0831,  ..., 0.7428, 0.4718, 0.5050])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 42.396695137023926 seconds
+
+[20.28, 20.48, 20.28, 20.48, 20.68, 20.72, 20.92, 20.84, 20.6, 20.6]
+[20.68, 20.48, 20.48, 24.96, 25.96, 28.64, 31.0, 30.08, 29.88, 29.2, 29.04, 27.32, 26.6, 25.64, 24.48, 24.28, 24.48, 24.6, 24.64, 24.64, 24.8, 24.8, 24.96, 24.76, 25.0, 24.92, 24.88, 24.72, 24.76, 24.72, 24.68, 24.72, 24.56, 24.6, 24.6, 24.56, 24.72, 24.8, 24.76, 24.64, 24.64, 24.64, 24.68, 24.56, 24.52, 24.68, 24.44, 24.28, 24.24, 24.04]
+52.43962478637695
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.396695137023926, 'TIME_S_1KI': 423.96695137023926, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1244.9685798645019, 'W': 23.740989470007165}
+[20.28, 20.48, 20.28, 20.48, 20.68, 20.72, 20.92, 20.84, 20.6, 20.6, 20.92, 20.6, 20.68, 20.68, 20.32, 20.36, 20.28, 20.24, 20.16, 20.16]
+369.29999999999995
+18.464999999999996
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.396695137023926, 'TIME_S_1KI': 423.96695137023926, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1244.9685798645019, 'W': 23.740989470007165, 'J_1KI': 12449.68579864502, 'W_1KI': 237.40989470007165, 'W_D': 5.275989470007168, 'J_D': 276.6709081840517, 'W_D_1KI': 52.75989470007168, 'J_D_1KI': 527.5989470007169}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..33ea6fd
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 62.99070644378662, "TIME_S_1KI": 629.9070644378662, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1817.8575790786747, "W": 23.739082320260188, "J_1KI": 18178.57579078675, "W_1KI": 237.3908232026019, "W_D": 5.089082320260189, "J_D": 389.7044856929784, "W_D_1KI": 50.890823202601894, "J_D_1KI": 508.908232026019}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..58e2c22
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.3 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 62.99070644378662}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2976,     6021,  ..., 29993904,
+                            29996986, 30000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9993, 9995, 9997]),
+       values=tensor([0.2230, 0.6279, 0.9702,  ..., 0.2815, 0.5420, 0.7025]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.3830, 0.2972, 0.7622,  ..., 0.1887, 0.7379, 0.3841])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 62.99070644378662 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2976,     6021,  ..., 29993904,
+                            29996986, 30000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9993, 9995, 9997]),
+       values=tensor([0.2230, 0.6279, 0.9702,  ..., 0.2815, 0.5420, 0.7025]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.3830, 0.2972, 0.7622,  ..., 0.1887, 0.7379, 0.3841])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 62.99070644378662 seconds
+
+[20.4, 20.36, 20.44, 20.44, 20.72, 20.96, 20.88, 21.12, 20.72, 20.36]
+[20.44, 20.2, 23.44, 25.08, 27.68, 28.88, 31.04, 29.08, 29.08, 29.48, 28.4, 29.16, 28.52, 28.08, 27.8, 26.96, 25.8, 24.96, 24.8, 24.68, 24.76, 24.68, 24.72, 24.68, 24.52, 24.44, 24.4, 24.76, 24.6, 24.88, 24.88, 24.72, 24.44, 24.48, 24.24, 24.32, 24.24, 24.16, 24.36, 24.56, 24.28, 24.24, 24.28, 24.16, 23.76, 23.92, 24.36, 24.4, 24.52, 24.84, 24.76, 24.44, 24.44, 24.24, 24.32, 24.04, 24.08, 24.28, 24.08, 24.08, 24.0, 23.88, 24.04, 24.28, 24.16, 24.4, 24.48, 24.4, 24.48, 24.6, 24.52, 24.52, 24.52]
+76.5765733718872
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 62.99070644378662, 'TIME_S_1KI': 629.9070644378662, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1817.8575790786747, 'W': 23.739082320260188}
+[20.4, 20.36, 20.44, 20.44, 20.72, 20.96, 20.88, 21.12, 20.72, 20.36, 20.28, 20.84, 20.84, 20.8, 21.0, 21.12, 21.0, 20.52, 20.56, 20.32]
+373.0
+18.65
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 62.99070644378662, 'TIME_S_1KI': 629.9070644378662, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1817.8575790786747, 'W': 23.739082320260188, 'J_1KI': 18178.57579078675, 'W_1KI': 237.3908232026019, 'W_D': 5.089082320260189, 'J_D': 389.7044856929784, 'W_D_1KI': 50.890823202601894, 'J_D_1KI': 508.908232026019}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..256b7e3
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 147223, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.998100280761719, "TIME_S_1KI": 0.07470368271779354, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.2257844543456, "W": 22.66095026687019, "J_1KI": 2.256616048133414, "W_1KI": 0.15392262259884795, "W_D": 4.3619502668701955, "J_D": 63.94931951642035, "W_D_1KI": 0.029628184909084827, "J_D_1KI": 0.00020124698524744657}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..a769041
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1200 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.015372514724731445}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ...,  999, 1000, 1000]),
+       col_indices=tensor([3209, 9868, 3248, 6619,  340, 2292, 7833, 3392, 6373,
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+                      0.3116, 0.0546, 0.1399, 0.8659, 0.1354, 0.9518]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4774, 0.5622, 0.8471,  ..., 0.2935, 0.6471, 0.0068])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.015372514724731445 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 68303 -ss 10000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.871366500854492}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([6744, 9183, 5203, 6638, 1533, 7743,  539, 8215, 8490,
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+       values=tensor([8.7997e-01, 2.8521e-01, 4.5005e-01, 5.0855e-01,
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+                      2.2972e-01, 1.5188e-01, 7.6577e-01, 9.9898e-01,
+                      3.1108e-01, 6.0390e-01, 1.0443e-01, 2.6637e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5445, 0.4387, 0.2146,  ..., 0.9330, 0.4366, 0.2965])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 4.871366500854492 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 147223 -ss 10000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.998100280761719}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.3098, 0.3277, 0.3122, 0.0248, 0.5693, 0.1331]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8452, 0.1047, 0.0563,  ..., 0.6079, 0.4820, 0.4351])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.998100280761719 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8452, 0.1047, 0.0563,  ..., 0.6079, 0.4820, 0.4351])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.998100280761719 seconds
+
+[20.36, 20.16, 20.2, 20.32, 20.32, 20.36, 20.56, 20.6, 20.52, 20.56]
+[20.68, 20.8, 21.2, 24.88, 26.6, 27.2, 27.72, 25.88, 24.68, 23.88, 23.88, 23.8, 23.88, 23.68]
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+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 147223, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.998100280761719, 'TIME_S_1KI': 0.07470368271779354, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.2257844543456, 'W': 22.66095026687019}
+[20.36, 20.16, 20.2, 20.32, 20.32, 20.36, 20.56, 20.6, 20.52, 20.56, 20.32, 20.36, 20.24, 20.2, 20.24, 20.36, 20.24, 20.2, 20.36, 20.24]
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diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..f94bbe2
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 52408, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.675631761550903, "TIME_S_1KI": 0.2037023309714338, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 322.76721565246584, "W": 22.07477701860741, "J_1KI": 6.1587394224634755, "W_1KI": 0.42121006370415603, "W_D": 3.4607770186074056, "J_D": 50.6018865489959, "W_D_1KI": 0.06603528122819809, "J_D_1KI": 0.0012600229207029095}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..a9bfe94
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.02814483642578125}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    3,  ..., 4997, 4998, 5000]),
+       col_indices=tensor([7223,  597, 5381,  ..., 4437, 2871, 7175]),
+       values=tensor([0.8424, 0.9605, 0.7186,  ..., 0.3316, 0.2968, 0.8125]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.9947, 0.8149, 0.3597,  ..., 0.7445, 0.4060, 0.0098])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.02814483642578125 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 37307 -ss 10000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.474437952041626}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([7873, 9438, 5376,  ..., 1254, 8934, 6510]),
+       values=tensor([0.8139, 0.0055, 0.6843,  ..., 0.4362, 0.9226, 0.6386]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.9683, 0.4961, 0.7880,  ..., 0.7466, 0.9086, 0.6990])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 7.474437952041626 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 52408 -ss 10000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.675631761550903}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([6316, 4387, 8598,  ...,  977, 3012, 3071]),
+       values=tensor([0.0249, 0.1066, 0.4899,  ..., 0.3057, 0.2915, 0.5832]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.6250, 0.8754, 0.6636,  ..., 0.3831, 0.1537, 0.5147])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.675631761550903 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([6316, 4387, 8598,  ...,  977, 3012, 3071]),
+       values=tensor([0.0249, 0.1066, 0.4899,  ..., 0.3057, 0.2915, 0.5832]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.6250, 0.8754, 0.6636,  ..., 0.3831, 0.1537, 0.5147])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.675631761550903 seconds
+
+[20.6, 20.52, 20.48, 20.48, 20.44, 20.28, 20.56, 20.6, 20.76, 21.08]
+[21.08, 20.76, 21.6, 22.6, 23.96, 24.64, 25.4, 24.84, 24.84, 24.72, 23.92, 24.04, 24.08, 23.92]
+14.621539115905762
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 52408, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.675631761550903, 'TIME_S_1KI': 0.2037023309714338, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.76721565246584, 'W': 22.07477701860741}
+[20.6, 20.52, 20.48, 20.48, 20.44, 20.28, 20.56, 20.6, 20.76, 21.08, 20.48, 20.64, 20.8, 20.76, 20.92, 21.0, 20.88, 20.76, 20.84, 20.96]
+372.2800000000001
+18.614000000000004
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 52408, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.675631761550903, 'TIME_S_1KI': 0.2037023309714338, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 322.76721565246584, 'W': 22.07477701860741, 'J_1KI': 6.1587394224634755, 'W_1KI': 0.42121006370415603, 'W_D': 3.4607770186074056, 'J_D': 50.6018865489959, 'W_D_1KI': 0.06603528122819809, 'J_D_1KI': 0.0012600229207029095}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..118d60f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 98.35910129547119, "TIME_S_1KI": 983.5910129547119, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2706.022798595428, "W": 24.09983015121324, "J_1KI": 27060.22798595428, "W_1KI": 240.9983015121324, "W_D": 5.635830151213241, "J_D": 632.8129610252375, "W_D_1KI": 56.358301512132414, "J_D_1KI": 563.5830151213241}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..22bc540
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 98.35910129547119}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       62,      113,  ..., 24999916,
+                            24999964, 25000000]),
+       col_indices=tensor([ 13628,  17541,  24252,  ..., 467551, 469636,
+                           477818]),
+       values=tensor([0.8374, 0.1433, 0.7046,  ..., 0.7606, 0.4438, 0.1648]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.5427, 0.9990, 0.7165,  ..., 0.2818, 0.2990, 0.5329])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 98.35910129547119 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       62,      113,  ..., 24999916,
+                            24999964, 25000000]),
+       col_indices=tensor([ 13628,  17541,  24252,  ..., 467551, 469636,
+                           477818]),
+       values=tensor([0.8374, 0.1433, 0.7046,  ..., 0.7606, 0.4438, 0.1648]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.5427, 0.9990, 0.7165,  ..., 0.2818, 0.2990, 0.5329])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 98.35910129547119 seconds
+
+[20.28, 20.32, 20.44, 20.32, 20.6, 20.64, 20.64, 20.52, 20.76, 20.8]
+[20.68, 20.52, 20.6, 22.08, 24.8, 25.76, 28.52, 28.48, 30.56, 30.0, 29.28, 29.8, 28.92, 28.32, 28.32, 27.32, 26.84, 25.96, 25.16, 25.08, 24.72, 24.92, 24.96, 25.12, 25.32, 25.44, 25.32, 25.24, 25.2, 25.08, 25.12, 25.28, 25.16, 25.32, 25.2, 25.04, 25.04, 25.04, 25.28, 25.16, 25.28, 25.36, 25.36, 25.36, 25.44, 25.48, 25.28, 25.24, 25.12, 25.04, 25.04, 25.2, 25.16, 25.36, 25.24, 25.36, 25.12, 25.12, 25.12, 25.2, 25.32, 25.36, 25.16, 25.16, 25.2, 25.28, 25.4, 25.44, 25.48, 25.28, 25.24, 25.36, 25.36, 25.24, 25.36, 25.2, 25.24, 25.52, 25.52, 25.52, 25.52, 25.4, 25.08, 25.2, 25.24, 25.44, 25.12, 25.12, 24.92, 24.8, 24.8, 25.08, 25.12, 25.32, 25.28, 25.2, 25.04, 25.08, 25.16, 25.4, 25.44, 25.36, 25.36, 25.36, 25.28, 25.04, 25.16]
+112.28389501571655
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 98.35910129547119, 'TIME_S_1KI': 983.5910129547119, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2706.022798595428, 'W': 24.09983015121324}
+[20.28, 20.32, 20.44, 20.32, 20.6, 20.64, 20.64, 20.52, 20.76, 20.8, 20.48, 20.64, 20.56, 20.44, 20.36, 20.4, 20.36, 20.52, 20.64, 20.68]
+369.28
+18.464
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 98.35910129547119, 'TIME_S_1KI': 983.5910129547119, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2706.022798595428, 'W': 24.09983015121324, 'J_1KI': 27060.22798595428, 'W_1KI': 240.9983015121324, 'W_D': 5.635830151213241, 'J_D': 632.8129610252375, 'W_D_1KI': 56.358301512132414, 'J_D_1KI': 563.5830151213241}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..36dce3a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.078357696533203, "TIME_S_1KI": 100.78357696533203, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 355.02523105621333, "W": 24.261238712111865, "J_1KI": 3550.2523105621335, "W_1KI": 242.61238712111864, "W_D": 5.657238712111866, "J_D": 82.78482829093929, "W_D_1KI": 56.57238712111866, "J_D_1KI": 565.7238712111866}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..8996689
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.078357696533203}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       0,       6,  ..., 2499989,
+                            2499997, 2500000]),
+       col_indices=tensor([ 10944, 177257, 201447,  ..., 125511, 168548,
+                           443200]),
+       values=tensor([0.0549, 0.4670, 0.3111,  ..., 0.0129, 0.0661, 0.9327]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3951, 0.3409, 0.2222,  ..., 0.4533, 0.5999, 0.5088])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.078357696533203 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       0,       6,  ..., 2499989,
+                            2499997, 2500000]),
+       col_indices=tensor([ 10944, 177257, 201447,  ..., 125511, 168548,
+                           443200]),
+       values=tensor([0.0549, 0.4670, 0.3111,  ..., 0.0129, 0.0661, 0.9327]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3951, 0.3409, 0.2222,  ..., 0.4533, 0.5999, 0.5088])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.078357696533203 seconds
+
+[20.6, 20.6, 20.76, 20.88, 20.68, 20.64, 20.36, 20.76, 20.88, 20.96]
+[21.0, 20.96, 24.12, 26.84, 26.84, 28.92, 30.04, 30.92, 26.56, 25.12, 24.8, 25.2, 25.28, 25.52]
+14.633433818817139
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.078357696533203, 'TIME_S_1KI': 100.78357696533203, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 355.02523105621333, 'W': 24.261238712111865}
+[20.6, 20.6, 20.76, 20.88, 20.68, 20.64, 20.36, 20.76, 20.88, 20.96, 20.52, 20.48, 20.56, 20.6, 20.6, 20.64, 20.64, 20.76, 20.8, 20.8]
+372.08
+18.604
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.078357696533203, 'TIME_S_1KI': 100.78357696533203, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 355.02523105621333, 'W': 24.261238712111865, 'J_1KI': 3550.2523105621335, 'W_1KI': 242.61238712111864, 'W_D': 5.657238712111866, 'J_D': 82.78482829093929, 'W_D_1KI': 56.57238712111866, 'J_D_1KI': 565.7238712111866}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..741a72d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 50.092703104019165, "TIME_S_1KI": 500.92703104019165, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1421.3682743358613, "W": 24.212233919725165, "J_1KI": 14213.682743358613, "W_1KI": 242.12233919725165, "W_D": 5.561233919725165, "J_D": 326.46972955346126, "W_D_1KI": 55.61233919725165, "J_D_1KI": 556.1233919725165}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..2787c93
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 50.092703104019165}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       19,       45,  ..., 12499947,
+                            12499973, 12500000]),
+       col_indices=tensor([ 17397,  55872, 132943,  ..., 437400, 464141,
+                           486359]),
+       values=tensor([0.6537, 0.5151, 0.3039,  ..., 0.7629, 0.2656, 0.5446]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4135, 0.5444, 0.9798,  ..., 0.8106, 0.6562, 0.9974])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 50.092703104019165 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       19,       45,  ..., 12499947,
+                            12499973, 12500000]),
+       col_indices=tensor([ 17397,  55872, 132943,  ..., 437400, 464141,
+                           486359]),
+       values=tensor([0.6537, 0.5151, 0.3039,  ..., 0.7629, 0.2656, 0.5446]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4135, 0.5444, 0.9798,  ..., 0.8106, 0.6562, 0.9974])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 50.092703104019165 seconds
+
+[20.76, 20.64, 20.64, 20.64, 20.48, 20.64, 20.6, 20.8, 21.08, 20.96]
+[20.92, 20.92, 23.96, 24.8, 26.64, 28.44, 28.44, 30.32, 28.64, 28.92, 27.6, 26.6, 25.88, 25.0, 25.04, 25.08, 25.08, 25.16, 25.16, 25.12, 25.08, 24.88, 25.08, 25.12, 25.2, 25.28, 25.36, 25.32, 25.32, 25.04, 25.04, 25.28, 25.16, 25.12, 25.24, 25.16, 25.16, 25.36, 25.24, 25.2, 25.28, 25.12, 24.96, 25.12, 25.2, 25.32, 25.36, 25.48, 25.56, 25.52, 25.52, 25.36, 25.4, 25.2, 25.28, 25.56]
+58.704549074172974
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 50.092703104019165, 'TIME_S_1KI': 500.92703104019165, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1421.3682743358613, 'W': 24.212233919725165}
+[20.76, 20.64, 20.64, 20.64, 20.48, 20.64, 20.6, 20.8, 21.08, 20.96, 20.76, 20.4, 20.48, 20.92, 20.8, 20.92, 20.88, 20.72, 20.64, 21.0]
+373.02
+18.651
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 50.092703104019165, 'TIME_S_1KI': 500.92703104019165, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1421.3682743358613, 'W': 24.212233919725165, 'J_1KI': 14213.682743358613, 'W_1KI': 242.12233919725165, 'W_D': 5.561233919725165, 'J_D': 326.46972955346126, 'W_D_1KI': 55.61233919725165, 'J_D_1KI': 556.1233919725165}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..8bb383c
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1701, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.195863723754883, "TIME_S_1KI": 5.994040989861777, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 298.5689013671875, "W": 22.02886990511616, "J_1KI": 175.5255152070473, "W_1KI": 12.950540802537425, "W_D": 3.5278699051161624, "J_D": 47.81508294677735, "W_D_1KI": 2.0739975926608833, "J_D_1KI": 1.2192813595889966}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..7e66808
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.6172366142272949}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,     11,  ..., 249984, 249992,
+                            250000]),
+       col_indices=tensor([   55, 33912,  7825,  ..., 25553, 31300, 45367]),
+       values=tensor([0.2156, 0.3825, 0.1471,  ..., 0.6075, 0.9514, 0.6641]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8972, 0.9948, 0.0628,  ..., 0.4950, 0.5589, 0.8119])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.6172366142272949 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1701 -ss 50000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.195863723754883}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     10,  ..., 249986, 249995,
+                            250000]),
+       col_indices=tensor([ 4095,  7631, 26458,  ..., 36946, 37655, 49733]),
+       values=tensor([0.3588, 0.4994, 0.4557,  ..., 0.6547, 0.8163, 0.6645]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0295, 0.0838, 0.1870,  ..., 0.2542, 0.3969, 0.7673])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.195863723754883 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     10,  ..., 249986, 249995,
+                            250000]),
+       col_indices=tensor([ 4095,  7631, 26458,  ..., 36946, 37655, 49733]),
+       values=tensor([0.3588, 0.4994, 0.4557,  ..., 0.6547, 0.8163, 0.6645]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0295, 0.0838, 0.1870,  ..., 0.2542, 0.3969, 0.7673])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.195863723754883 seconds
+
+[20.6, 20.6, 20.68, 20.56, 20.68, 20.6, 20.6, 20.48, 20.36, 20.44]
+[20.44, 20.16, 20.24, 22.04, 22.92, 24.8, 25.72, 25.84, 25.56, 24.68, 24.84, 25.0, 25.2]
+13.55352783203125
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1701, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.195863723754883, 'TIME_S_1KI': 5.994040989861777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 298.5689013671875, 'W': 22.02886990511616}
+[20.6, 20.6, 20.68, 20.56, 20.68, 20.6, 20.6, 20.48, 20.36, 20.44, 20.56, 20.52, 20.52, 20.44, 20.44, 20.44, 20.44, 20.56, 20.88, 20.84]
+370.02
+18.500999999999998
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1701, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.195863723754883, 'TIME_S_1KI': 5.994040989861777, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 298.5689013671875, 'W': 22.02886990511616, 'J_1KI': 175.5255152070473, 'W_1KI': 12.950540802537425, 'W_D': 3.5278699051161624, 'J_D': 47.81508294677735, 'W_D_1KI': 2.0739975926608833, 'J_D_1KI': 1.2192813595889966}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..911c49b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 193, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.676417112350464, "TIME_S_1KI": 55.318223380054214, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 327.75970675468443, "W": 22.393652951640597, "J_1KI": 1698.2368225631317, "W_1KI": 116.02928990487356, "W_D": 4.002652951640599, "J_D": 58.58393717646598, "W_D_1KI": 20.73913446445906, "J_D_1KI": 107.45665525626457}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..7534e40
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.431778192520142}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      57,      95,  ..., 2499895,
+                            2499944, 2500000]),
+       col_indices=tensor([   14,  1180,  1352,  ..., 49220, 49912, 49936]),
+       values=tensor([0.8618, 0.4205, 0.6419,  ..., 0.4989, 0.5508, 0.1652]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3965, 0.7585, 0.8670,  ..., 0.1152, 0.9413, 0.0865])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 5.431778192520142 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 193 -ss 50000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.676417112350464}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      57,     106,  ..., 2499905,
+                            2499942, 2500000]),
+       col_indices=tensor([  275,  2452,  2625,  ..., 47289, 48937, 49987]),
+       values=tensor([0.8108, 0.0031, 0.6812,  ..., 0.9899, 0.5982, 0.1156]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1893, 0.7932, 0.1409,  ..., 0.0408, 0.4757, 0.3205])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.676417112350464 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      57,     106,  ..., 2499905,
+                            2499942, 2500000]),
+       col_indices=tensor([  275,  2452,  2625,  ..., 47289, 48937, 49987]),
+       values=tensor([0.8108, 0.0031, 0.6812,  ..., 0.9899, 0.5982, 0.1156]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1893, 0.7932, 0.1409,  ..., 0.0408, 0.4757, 0.3205])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.676417112350464 seconds
+
+[20.32, 20.4, 20.88, 20.88, 20.88, 20.88, 20.64, 20.24, 20.48, 20.52]
+[20.48, 20.64, 20.88, 22.0, 23.72, 25.32, 26.2, 26.2, 26.28, 25.08, 24.6, 24.56, 24.36, 24.36]
+14.636276960372925
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.676417112350464, 'TIME_S_1KI': 55.318223380054214, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.75970675468443, 'W': 22.393652951640597}
+[20.32, 20.4, 20.88, 20.88, 20.88, 20.88, 20.64, 20.24, 20.48, 20.52, 20.24, 20.24, 20.24, 20.4, 20.4, 20.2, 20.16, 20.08, 20.16, 20.24]
+367.81999999999994
+18.391
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.676417112350464, 'TIME_S_1KI': 55.318223380054214, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 327.75970675468443, 'W': 22.393652951640597, 'J_1KI': 1698.2368225631317, 'W_1KI': 116.02928990487356, 'W_D': 4.002652951640599, 'J_D': 58.58393717646598, 'W_D_1KI': 20.73913446445906, 'J_D_1KI': 107.45665525626457}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..0d711cb
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 53.1586058139801, "TIME_S_1KI": 531.586058139801, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1662.8207085037227, "W": 23.65320095688087, "J_1KI": 16628.20708503723, "W_1KI": 236.53200956880872, "W_D": 4.901200956880867, "J_D": 344.55456842803903, "W_D_1KI": 49.01200956880867, "J_D_1KI": 490.12009568808674}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..142816b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 53.1586058139801}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      489,      973,  ..., 24998974,
+                            24999478, 25000000]),
+       col_indices=tensor([  275,   454,   699,  ..., 49715, 49729, 49796]),
+       values=tensor([0.3350, 0.9556, 0.9308,  ..., 0.7756, 0.4208, 0.8843]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3743, 0.4258, 0.0327,  ..., 0.7931, 0.5462, 0.4257])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 53.1586058139801 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      489,      973,  ..., 24998974,
+                            24999478, 25000000]),
+       col_indices=tensor([  275,   454,   699,  ..., 49715, 49729, 49796]),
+       values=tensor([0.3350, 0.9556, 0.9308,  ..., 0.7756, 0.4208, 0.8843]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3743, 0.4258, 0.0327,  ..., 0.7931, 0.5462, 0.4257])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 53.1586058139801 seconds
+
+[20.44, 20.76, 21.08, 21.12, 21.36, 21.16, 21.04, 21.0, 20.96, 20.84]
+[21.0, 20.84, 20.84, 23.72, 24.72, 26.8, 28.0, 29.48, 28.44, 28.04, 28.08, 27.92, 28.6, 27.68, 27.36, 26.52, 25.72, 24.68, 24.64, 24.72, 24.64, 24.64, 24.68, 24.68, 24.56, 24.76, 24.76, 24.64, 24.68, 24.64, 24.8, 24.64, 25.04, 24.96, 24.84, 24.88, 24.68, 24.68, 24.64, 24.8, 24.72, 24.72, 24.68, 24.56, 24.36, 24.36, 24.48, 24.4, 24.44, 24.4, 24.6, 24.56, 24.72, 24.96, 24.92, 24.84, 24.76, 24.72, 24.76, 24.8, 24.88, 24.92, 24.76, 24.68, 24.56, 24.64, 24.6]
+70.30002880096436
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 53.1586058139801, 'TIME_S_1KI': 531.586058139801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1662.8207085037227, 'W': 23.65320095688087}
+[20.44, 20.76, 21.08, 21.12, 21.36, 21.16, 21.04, 21.0, 20.96, 20.84, 20.6, 20.6, 20.72, 20.72, 20.68, 20.56, 20.76, 20.64, 20.6, 20.68]
+375.04
+18.752000000000002
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 53.1586058139801, 'TIME_S_1KI': 531.586058139801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1662.8207085037227, 'W': 23.65320095688087, 'J_1KI': 16628.20708503723, 'W_1KI': 236.53200956880872, 'W_D': 4.901200956880867, 'J_D': 344.55456842803903, 'W_D_1KI': 49.01200956880867, 'J_D_1KI': 490.12009568808674}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..458d682
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 10429, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.529776096343994, "TIME_S_1KI": 1.0096630641810331, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 331.7018713665009, "W": 22.733124806095596, "J_1KI": 31.80572167671885, "W_1KI": 2.1797990992516634, "W_D": 4.373124806095593, "J_D": 63.8088117790222, "W_D_1KI": 0.41932350235838456, "J_D_1KI": 0.040207450604888735}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..f7f583d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.1077582836151123}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24997, 24998, 25000]),
+       col_indices=tensor([30956, 25020,  4290,  ...,  1571,  5930, 34059]),
+       values=tensor([0.1925, 0.5429, 0.7430,  ..., 0.0669, 0.5504, 0.8934]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9564, 0.4579, 0.9465,  ..., 0.7236, 0.9546, 0.7676])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.1077582836151123 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 9744 -ss 50000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.809481859207153}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([47588, 45161, 40455,  ..., 30522, 42036,  2005]),
+       values=tensor([0.2055, 0.2802, 0.2448,  ..., 0.0926, 0.8451, 0.9361]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5605, 0.1853, 0.0043,  ..., 0.6007, 0.1968, 0.9775])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 9.809481859207153 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10429 -ss 50000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.529776096343994}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([20158, 23859, 20874,  ..., 41939, 15422, 41283]),
+       values=tensor([0.7225, 0.1851, 0.6655,  ..., 0.6086, 0.8791, 0.8414]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1130, 0.7539, 0.9598,  ..., 0.4914, 0.7455, 0.6539])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.529776096343994 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([20158, 23859, 20874,  ..., 41939, 15422, 41283]),
+       values=tensor([0.7225, 0.1851, 0.6655,  ..., 0.6086, 0.8791, 0.8414]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1130, 0.7539, 0.9598,  ..., 0.4914, 0.7455, 0.6539])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.529776096343994 seconds
+
+[20.44, 20.44, 20.32, 20.64, 20.6, 20.48, 20.6, 20.36, 20.32, 20.16]
+[20.36, 20.16, 20.76, 22.32, 23.88, 23.88, 25.12, 26.36, 26.64, 26.44, 26.08, 26.2, 25.88, 25.48]
+14.591125249862671
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10429, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.529776096343994, 'TIME_S_1KI': 1.0096630641810331, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 331.7018713665009, 'W': 22.733124806095596}
+[20.44, 20.44, 20.32, 20.64, 20.6, 20.48, 20.6, 20.36, 20.32, 20.16, 20.56, 20.32, 20.36, 20.56, 20.36, 20.28, 20.44, 20.16, 20.16, 20.44]
+367.20000000000005
+18.360000000000003
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 10429, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.529776096343994, 'TIME_S_1KI': 1.0096630641810331, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 331.7018713665009, 'W': 22.733124806095596, 'J_1KI': 31.80572167671885, 'W_1KI': 2.1797990992516634, 'W_D': 4.373124806095593, 'J_D': 63.8088117790222, 'W_D_1KI': 0.41932350235838456, 'J_D_1KI': 0.040207450604888735}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..fea28ac
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 3217, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.183324813842773, "TIME_S_1KI": 3.1654724320307035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 313.5762239074707, "W": 23.142141761096784, "J_1KI": 97.47473543906457, "W_1KI": 7.193702754459679, "W_D": 4.7631417610967866, "J_D": 64.54061265373231, "W_D_1KI": 1.4806160276956128, "J_D_1KI": 0.46024744410805496}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..dbda68a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.3263556957244873}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ..., 124996, 124997,
+                            125000]),
+       col_indices=tensor([ 8999, 37078,  2648,  ..., 24880, 43913, 47673]),
+       values=tensor([0.7939, 0.1706, 0.9831,  ..., 0.2838, 0.4924, 0.0921]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.2834, 0.1318, 0.3567,  ..., 0.3503, 0.0519, 0.6169])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.3263556957244873 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3217 -ss 50000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.183324813842773}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 125000, 125000,
+                            125000]),
+       col_indices=tensor([ 9508, 26799,  1812,  ..., 32912, 38580, 39384]),
+       values=tensor([0.1038, 0.2683, 0.7729,  ..., 0.6337, 0.2232, 0.8870]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7924, 0.9644, 0.0933,  ..., 0.2945, 0.3904, 0.9557])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.183324813842773 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 125000, 125000,
+                            125000]),
+       col_indices=tensor([ 9508, 26799,  1812,  ..., 32912, 38580, 39384]),
+       values=tensor([0.1038, 0.2683, 0.7729,  ..., 0.6337, 0.2232, 0.8870]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7924, 0.9644, 0.0933,  ..., 0.2945, 0.3904, 0.9557])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.183324813842773 seconds
+
+[20.36, 20.32, 20.32, 20.32, 20.36, 20.28, 20.48, 20.52, 20.64, 20.52]
+[20.52, 20.48, 21.56, 23.32, 25.2, 26.32, 27.16, 27.16, 26.96, 26.76, 25.76, 25.84, 25.52]
+13.550008773803711
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3217, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.183324813842773, 'TIME_S_1KI': 3.1654724320307035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 313.5762239074707, 'W': 23.142141761096784}
+[20.36, 20.32, 20.32, 20.32, 20.36, 20.28, 20.48, 20.52, 20.64, 20.52, 20.52, 20.6, 20.44, 20.6, 20.48, 20.32, 20.2, 20.36, 20.4, 20.48]
+367.5799999999999
+18.378999999999998
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 3217, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.183324813842773, 'TIME_S_1KI': 3.1654724320307035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 313.5762239074707, 'W': 23.142141761096784, 'J_1KI': 97.47473543906457, 'W_1KI': 7.193702754459679, 'W_D': 4.7631417610967866, 'J_D': 64.54061265373231, 'W_D_1KI': 1.4806160276956128, 'J_D_1KI': 0.46024744410805496}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..769abe1
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 97993, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.781827449798584, "TIME_S_1KI": 0.1100265064831017, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 315.41705104827884, "W": 21.541217597275665, "J_1KI": 3.218771249459439, "W_1KI": 0.21982404454681115, "W_D": 2.808217597275668, "J_D": 41.119296494484, "W_D_1KI": 0.02865732855689353, "J_D_1KI": 0.00029244260872606745}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..bb7f54b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.0191800594329834}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([3313, 1621, 3812,  ..., 4525, 1664, 4698]),
+       values=tensor([0.0941, 0.2796, 0.9707,  ..., 0.4661, 0.7642, 0.2416]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.5336, 0.9402, 0.6361,  ..., 0.0126, 0.4753, 0.7232])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.0191800594329834 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 54744 -ss 5000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.86583399772644}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([ 440, 3019, 2397,  ..., 2648, 4224, 1471]),
+       values=tensor([0.9686, 0.9548, 0.6770,  ..., 0.0683, 0.1247, 0.7029]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3648, 0.8360, 0.9424,  ..., 0.5773, 0.5768, 0.8650])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 5.86583399772644 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 97993 -ss 5000 -sd 0.0001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.781827449798584}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 2498, 2500, 2500]),
+       col_indices=tensor([3713, 3378, 4473,  ..., 4286, 2104, 3764]),
+       values=tensor([0.2566, 0.6316, 0.0221,  ..., 0.9864, 0.6559, 0.8912]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3213, 0.3541, 0.7168,  ..., 0.5598, 0.7087, 0.6560])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.781827449798584 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 2498, 2500, 2500]),
+       col_indices=tensor([3713, 3378, 4473,  ..., 4286, 2104, 3764]),
+       values=tensor([0.2566, 0.6316, 0.0221,  ..., 0.9864, 0.6559, 0.8912]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3213, 0.3541, 0.7168,  ..., 0.5598, 0.7087, 0.6560])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.781827449798584 seconds
+
+[20.44, 20.48, 20.88, 20.96, 20.96, 20.92, 20.72, 20.72, 20.56, 20.48]
+[20.44, 20.32, 20.6, 21.56, 23.32, 24.04, 24.72, 24.96, 24.56, 23.56, 23.68, 23.44, 23.52, 23.56]
+14.642489433288574
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 97993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.781827449798584, 'TIME_S_1KI': 0.1100265064831017, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.41705104827884, 'W': 21.541217597275665}
+[20.44, 20.48, 20.88, 20.96, 20.96, 20.92, 20.72, 20.72, 20.56, 20.48, 20.36, 20.44, 20.44, 20.32, 20.4, 20.68, 21.08, 21.76, 21.76, 21.88]
+374.65999999999997
+18.732999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 97993, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.781827449798584, 'TIME_S_1KI': 0.1100265064831017, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.41705104827884, 'W': 21.541217597275665, 'J_1KI': 3.218771249459439, 'W_1KI': 0.21982404454681115, 'W_D': 2.808217597275668, 'J_D': 41.119296494484, 'W_D_1KI': 0.02865732855689353, 'J_D_1KI': 0.00029244260872606745}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..1de7362
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 17801, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.451899290084839, "TIME_S_1KI": 0.5871523672875029, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 335.3643196678161, "W": 22.918247866906853, "J_1KI": 18.839633709781257, "W_1KI": 1.2874696852371694, "W_D": 4.382247866906855, "J_D": 64.12573871421813, "W_D_1KI": 0.24617987005824704, "J_D_1KI": 0.013829552837382564}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..6e9f06a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06674790382385254}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     6,    11,  ..., 24994, 24998, 25000]),
+       col_indices=tensor([ 153, 1166, 1591,  ..., 4476, 1654, 3013]),
+       values=tensor([0.9133, 0.8479, 0.0929,  ..., 0.2328, 0.6185, 0.0308]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9627, 0.4329, 0.3045,  ..., 0.2813, 0.7730, 0.0924])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.06674790382385254 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 15730 -ss 5000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.27815866470337}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     5,  ..., 24988, 24995, 25000]),
+       col_indices=tensor([1294,  410,  634,  ..., 1096, 2182, 3875]),
+       values=tensor([0.7576, 0.8466, 0.6529,  ..., 0.8373, 0.3120, 0.9707]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6367, 0.3440, 0.8123,  ..., 0.8035, 0.7344, 0.3858])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 9.27815866470337 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17801 -ss 5000 -sd 0.001 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.451899290084839}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     5,  ..., 24991, 24996, 25000]),
+       col_indices=tensor([ 139, 2091, 2694,  ..., 3635, 3692, 4401]),
+       values=tensor([0.7198, 0.5125, 0.0166,  ..., 0.6335, 0.1279, 0.4059]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0330, 0.5472, 0.9005,  ..., 0.3693, 0.0673, 0.4597])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.451899290084839 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     5,  ..., 24991, 24996, 25000]),
+       col_indices=tensor([ 139, 2091, 2694,  ..., 3635, 3692, 4401]),
+       values=tensor([0.7198, 0.5125, 0.0166,  ..., 0.6335, 0.1279, 0.4059]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0330, 0.5472, 0.9005,  ..., 0.3693, 0.0673, 0.4597])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.451899290084839 seconds
+
+[20.28, 20.36, 20.4, 20.6, 20.68, 20.64, 20.52, 20.48, 20.32, 20.24]
+[20.4, 20.48, 23.8, 23.8, 25.36, 27.2, 28.0, 28.44, 25.2, 23.88, 23.72, 23.92, 24.08, 24.08]
+14.63306975364685
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 17801, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.451899290084839, 'TIME_S_1KI': 0.5871523672875029, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.3643196678161, 'W': 22.918247866906853}
+[20.28, 20.36, 20.4, 20.6, 20.68, 20.64, 20.52, 20.48, 20.32, 20.24, 20.44, 20.64, 20.96, 20.68, 20.56, 20.68, 20.72, 20.76, 20.76, 20.96]
+370.71999999999997
+18.535999999999998
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 17801, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.451899290084839, 'TIME_S_1KI': 0.5871523672875029, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 335.3643196678161, 'W': 22.918247866906853, 'J_1KI': 18.839633709781257, 'W_1KI': 1.2874696852371694, 'W_D': 4.382247866906855, 'J_D': 64.12573871421813, 'W_D_1KI': 0.24617987005824704, 'J_D_1KI': 0.013829552837382564}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..d1eb1c9
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 1927, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.278133630752563, "TIME_S_1KI": 5.333748640764174, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 315.9295339012146, "W": 21.68469080536072, "J_1KI": 163.94890186881918, "W_1KI": 11.253082929611168, "W_D": 3.326690805360723, "J_D": 48.46736737012868, "W_D_1KI": 1.7263574495904115, "J_D_1KI": 0.8958782820915472}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..9788e34
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.5448694229125977}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     55,    104,  ..., 249896, 249951,
+                            250000]),
+       col_indices=tensor([ 128,  142,  245,  ..., 4657, 4734, 4838]),
+       values=tensor([0.1820, 0.1438, 0.1562,  ..., 0.6881, 0.0081, 0.4382]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7604, 0.0772, 0.6951,  ..., 0.4926, 0.6864, 0.3702])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.5448694229125977 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1927 -ss 5000 -sd 0.01 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.278133630752563}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     52,    109,  ..., 249898, 249957,
+                            250000]),
+       col_indices=tensor([  25,  140,  158,  ..., 4486, 4823, 4835]),
+       values=tensor([0.8176, 0.1521, 0.6094,  ..., 0.2740, 0.3181, 0.5161]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4533, 0.4508, 0.3256,  ..., 0.6556, 0.1742, 0.9221])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.278133630752563 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     52,    109,  ..., 249898, 249957,
+                            250000]),
+       col_indices=tensor([  25,  140,  158,  ..., 4486, 4823, 4835]),
+       values=tensor([0.8176, 0.1521, 0.6094,  ..., 0.2740, 0.3181, 0.5161]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4533, 0.4508, 0.3256,  ..., 0.6556, 0.1742, 0.9221])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.278133630752563 seconds
+
+[20.28, 20.32, 20.56, 20.6, 20.48, 20.56, 20.76, 20.68, 20.88, 20.88]
+[20.72, 20.48, 20.56, 21.28, 22.24, 24.0, 24.68, 25.2, 25.04, 23.88, 23.88, 24.04, 24.2, 24.4]
+14.56924319267273
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1927, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.278133630752563, 'TIME_S_1KI': 5.333748640764174, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.9295339012146, 'W': 21.68469080536072}
+[20.28, 20.32, 20.56, 20.6, 20.48, 20.56, 20.76, 20.68, 20.88, 20.88, 20.16, 20.08, 20.16, 20.16, 20.24, 20.36, 20.08, 20.2, 20.28, 20.2]
+367.15999999999997
+18.357999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 1927, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.278133630752563, 'TIME_S_1KI': 5.333748640764174, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 315.9295339012146, 'W': 21.68469080536072, 'J_1KI': 163.94890186881918, 'W_1KI': 11.253082929611168, 'W_D': 3.326690805360723, 'J_D': 48.46736737012868, 'W_D_1KI': 1.7263574495904115, 'J_D_1KI': 0.8958782820915472}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..942bb7d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 393, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.605815887451172, "TIME_S_1KI": 26.986808873921557, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 343.57593024253845, "W": 23.547531420592268, "J_1KI": 874.239008250734, "W_1KI": 59.917382749598644, "W_D": 5.065531420592269, "J_D": 73.90985657548903, "W_D_1KI": 12.889392927715695, "J_D_1KI": 32.79743747510355}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..5869b7e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.6673474311828613}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     248,     507,  ..., 1249488,
+                            1249771, 1250000]),
+       col_indices=tensor([   0,   22,   35,  ..., 4958, 4983, 4999]),
+       values=tensor([0.4233, 0.1325, 0.2059,  ..., 0.9744, 0.8399, 0.1366]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6304, 0.5951, 0.1863,  ..., 0.0552, 0.3796, 0.7701])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 2.6673474311828613 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 393 -ss 5000 -sd 0.05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.605815887451172}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     284,     548,  ..., 1249494,
+                            1249762, 1250000]),
+       col_indices=tensor([   9,   27,   28,  ..., 4894, 4914, 4954]),
+       values=tensor([0.8223, 0.3728, 0.3102,  ..., 0.8633, 0.4361, 0.2072]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.4176, 0.5149, 0.4165,  ..., 0.2240, 0.9505, 0.5242])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.605815887451172 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     284,     548,  ..., 1249494,
+                            1249762, 1250000]),
+       col_indices=tensor([   9,   27,   28,  ..., 4894, 4914, 4954]),
+       values=tensor([0.8223, 0.3728, 0.3102,  ..., 0.8633, 0.4361, 0.2072]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.4176, 0.5149, 0.4165,  ..., 0.2240, 0.9505, 0.5242])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.605815887451172 seconds
+
+[20.32, 20.32, 20.44, 20.4, 20.52, 20.52, 20.64, 20.8, 20.92, 20.88]
+[20.92, 21.04, 24.24, 26.28, 27.72, 27.72, 28.36, 29.08, 25.2, 24.24, 24.28, 24.28, 24.28, 24.2]
+14.59074091911316
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 393, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.605815887451172, 'TIME_S_1KI': 26.986808873921557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 343.57593024253845, 'W': 23.547531420592268}
+[20.32, 20.32, 20.44, 20.4, 20.52, 20.52, 20.64, 20.8, 20.92, 20.88, 20.24, 20.28, 20.52, 20.64, 20.6, 20.56, 20.52, 20.48, 20.48, 20.56]
+369.64
+18.482
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 393, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.605815887451172, 'TIME_S_1KI': 26.986808873921557, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 343.57593024253845, 'W': 23.547531420592268, 'J_1KI': 874.239008250734, 'W_1KI': 59.917382749598644, 'W_D': 5.065531420592269, 'J_D': 73.90985657548903, 'W_D_1KI': 12.889392927715695, 'J_D_1KI': 32.79743747510355}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..5939696
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 194, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.912250995635986, "TIME_S_1KI": 56.248716472350445, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 326.6964877033234, "W": 22.305376949262342, "J_1KI": 1684.0025139346567, "W_1KI": 114.97616984155846, "W_D": 3.7133769492623436, "J_D": 54.38810604286199, "W_D_1KI": 19.14111829516672, "J_D_1KI": 98.66555822250888}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..469c726
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.1 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.392450332641602}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     508,    1024,  ..., 2499064,
+                            2499534, 2500000]),
+       col_indices=tensor([   1,    4,   10,  ..., 4973, 4986, 4993]),
+       values=tensor([0.4448, 0.2935, 0.6096,  ..., 0.6772, 0.8304, 0.1969]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5801, 0.6662, 0.3258,  ..., 0.9572, 0.7518, 0.3845])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 5.392450332641602 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 194 -ss 5000 -sd 0.1 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.912250995635986}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     510,    1021,  ..., 2499033,
+                            2499527, 2500000]),
+       col_indices=tensor([  27,   33,   84,  ..., 4958, 4963, 4982]),
+       values=tensor([0.5404, 0.4129, 0.3312,  ..., 0.4218, 0.5770, 0.4495]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8518, 0.9058, 0.3829,  ..., 0.5160, 0.0011, 0.3108])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.912250995635986 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     510,    1021,  ..., 2499033,
+                            2499527, 2500000]),
+       col_indices=tensor([  27,   33,   84,  ..., 4958, 4963, 4982]),
+       values=tensor([0.5404, 0.4129, 0.3312,  ..., 0.4218, 0.5770, 0.4495]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8518, 0.9058, 0.3829,  ..., 0.5160, 0.0011, 0.3108])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.912250995635986 seconds
+
+[20.28, 20.36, 20.6, 20.76, 20.96, 20.8, 20.76, 20.64, 20.48, 20.76]
+[20.88, 20.88, 20.64, 21.88, 22.92, 24.88, 26.08, 26.2, 25.72, 24.96, 24.48, 24.56, 24.76, 24.68]
+14.646535158157349
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 194, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.912250995635986, 'TIME_S_1KI': 56.248716472350445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.6964877033234, 'W': 22.305376949262342}
+[20.28, 20.36, 20.6, 20.76, 20.96, 20.8, 20.76, 20.64, 20.48, 20.76, 20.68, 20.08, 20.2, 20.52, 20.52, 20.72, 20.72, 21.32, 21.0, 21.08]
+371.84
+18.592
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 194, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.912250995635986, 'TIME_S_1KI': 56.248716472350445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 326.6964877033234, 'W': 22.305376949262342, 'J_1KI': 1684.0025139346567, 'W_1KI': 114.97616984155846, 'W_D': 3.7133769492623436, 'J_D': 54.38810604286199, 'W_D_1KI': 19.14111829516672, 'J_D_1KI': 98.66555822250888}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..8d5c96a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.527036905288696, "TIME_S_1KI": 105.27036905288696, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 363.19735393524167, "W": 23.113061201495835, "J_1KI": 3631.973539352417, "W_1KI": 231.13061201495836, "W_D": 4.666061201495836, "J_D": 73.32222533869742, "W_D_1KI": 46.66061201495836, "J_D_1KI": 466.6061201495836}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..9300b4b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.2 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.527036905288696}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     996,    2006,  ..., 4997968,
+                            4998974, 5000000]),
+       col_indices=tensor([   4,    8,   12,  ..., 4976, 4983, 4993]),
+       values=tensor([0.4991, 0.7024, 0.1537,  ..., 0.4726, 0.2476, 0.0939]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1571, 0.7792, 0.7385,  ..., 0.2151, 0.4821, 0.5033])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.527036905288696 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     996,    2006,  ..., 4997968,
+                            4998974, 5000000]),
+       col_indices=tensor([   4,    8,   12,  ..., 4976, 4983, 4993]),
+       values=tensor([0.4991, 0.7024, 0.1537,  ..., 0.4726, 0.2476, 0.0939]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1571, 0.7792, 0.7385,  ..., 0.2151, 0.4821, 0.5033])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.527036905288696 seconds
+
+[20.48, 20.44, 20.48, 20.64, 20.72, 20.72, 20.68, 20.72, 20.68, 20.6]
+[20.4, 20.48, 21.0, 23.96, 25.48, 27.4, 28.52, 27.56, 26.24, 25.04, 24.48, 24.4, 24.4, 24.32, 24.48]
+15.71394419670105
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.527036905288696, 'TIME_S_1KI': 105.27036905288696, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 363.19735393524167, 'W': 23.113061201495835}
+[20.48, 20.44, 20.48, 20.64, 20.72, 20.72, 20.68, 20.72, 20.68, 20.6, 20.4, 20.16, 20.32, 20.32, 20.16, 20.56, 20.48, 20.52, 20.48, 20.24]
+368.94
+18.447
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.527036905288696, 'TIME_S_1KI': 105.27036905288696, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 363.19735393524167, 'W': 23.113061201495835, 'J_1KI': 3631.973539352417, 'W_1KI': 231.13061201495836, 'W_D': 4.666061201495836, 'J_D': 73.32222533869742, 'W_D_1KI': 46.66061201495836, 'J_D_1KI': 466.6061201495836}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..fef5f93
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 15.927062749862671, "TIME_S_1KI": 159.2706274986267, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 517.2976983642578, "W": 23.521442010494468, "J_1KI": 5172.9769836425785, "W_1KI": 235.21442010494468, "W_D": 5.048442010494465, "J_D": 111.02837280082699, "W_D_1KI": 50.484420104944654, "J_D_1KI": 504.8442010494465}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..eaea98f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.3 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 15.927062749862671}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1527,    3024,  ..., 7496971,
+                            7498460, 7500000]),
+       col_indices=tensor([   0,    3,    4,  ..., 4985, 4992, 4996]),
+       values=tensor([0.7552, 0.2419, 0.2481,  ..., 0.7383, 0.7786, 0.4470]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.6238, 0.3406, 0.3665,  ..., 0.0040, 0.2464, 0.8126])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 15.927062749862671 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1527,    3024,  ..., 7496971,
+                            7498460, 7500000]),
+       col_indices=tensor([   0,    3,    4,  ..., 4985, 4992, 4996]),
+       values=tensor([0.7552, 0.2419, 0.2481,  ..., 0.7383, 0.7786, 0.4470]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.6238, 0.3406, 0.3665,  ..., 0.0040, 0.2464, 0.8126])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 15.927062749862671 seconds
+
+[20.4, 20.32, 20.32, 20.4, 20.48, 20.52, 20.8, 20.76, 20.76, 20.64]
+[20.8, 20.8, 21.12, 22.88, 23.72, 26.16, 27.92, 27.96, 27.28, 26.72, 25.24, 24.32, 24.24, 24.16, 24.2, 24.68, 24.8, 24.68, 24.68, 24.56, 24.64]
+21.99260139465332
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 15.927062749862671, 'TIME_S_1KI': 159.2706274986267, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 517.2976983642578, 'W': 23.521442010494468}
+[20.4, 20.32, 20.32, 20.4, 20.48, 20.52, 20.8, 20.76, 20.76, 20.64, 20.56, 20.16, 20.24, 20.6, 20.64, 20.76, 20.8, 20.6, 20.28, 20.44]
+369.46000000000004
+18.473000000000003
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 15.927062749862671, 'TIME_S_1KI': 159.2706274986267, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 517.2976983642578, 'W': 23.521442010494468, 'J_1KI': 5172.9769836425785, 'W_1KI': 235.21442010494468, 'W_D': 5.048442010494465, 'J_D': 111.02837280082699, 'W_D_1KI': 50.484420104944654, 'J_D_1KI': 504.8442010494465}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..d2af780
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 22.84249472618103, "TIME_S_1KI": 228.4249472618103, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 682.8522075366976, "W": 24.128078827131084, "J_1KI": 6828.522075366976, "W_1KI": 241.28078827131083, "W_D": 3.4220788271310845, "J_D": 96.84874200773261, "W_D_1KI": 34.220788271310845, "J_D_1KI": 342.2078827131084}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..c75f057
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.4 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 22.84249472618103}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2006,     4067,  ...,  9995955,
+                             9997971, 10000000]),
+       col_indices=tensor([   1,    3,    4,  ..., 4995, 4998, 4999]),
+       values=tensor([0.5438, 0.4529, 0.4674,  ..., 0.4313, 0.1734, 0.8643]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4316, 0.5719, 0.8319,  ..., 0.7407, 0.2442, 0.5797])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 22.84249472618103 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2006,     4067,  ...,  9995955,
+                             9997971, 10000000]),
+       col_indices=tensor([   1,    3,    4,  ..., 4995, 4998, 4999]),
+       values=tensor([0.5438, 0.4529, 0.4674,  ..., 0.4313, 0.1734, 0.8643]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4316, 0.5719, 0.8319,  ..., 0.7407, 0.2442, 0.5797])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 22.84249472618103 seconds
+
+[26.88, 26.16, 25.48, 25.52, 24.68, 24.6, 24.36, 24.16, 24.16, 24.28]
+[24.4, 24.52, 24.52, 28.04, 29.2, 30.8, 31.68, 30.2, 29.88, 28.12, 26.84, 25.72, 24.28, 24.16, 24.04, 24.12, 24.24, 24.24, 24.4, 24.44, 24.48, 24.52, 24.68, 24.48, 24.68, 24.6, 24.68]
+28.301142930984497
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 22.84249472618103, 'TIME_S_1KI': 228.4249472618103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 682.8522075366976, 'W': 24.128078827131084}
+[26.88, 26.16, 25.48, 25.52, 24.68, 24.6, 24.36, 24.16, 24.16, 24.28, 20.44, 20.64, 20.68, 20.8, 20.92, 21.32, 21.36, 21.44, 21.44, 21.2]
+414.12
+20.706
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 22.84249472618103, 'TIME_S_1KI': 228.4249472618103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 682.8522075366976, 'W': 24.128078827131084, 'J_1KI': 6828.522075366976, 'W_1KI': 241.28078827131083, 'W_D': 3.4220788271310845, 'J_D': 96.84874200773261, 'W_D_1KI': 34.220788271310845, 'J_D_1KI': 342.2078827131084}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..1c5a213
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.49390745162964, "TIME_S_1KI": 264.9390745162964, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 833.4256306743622, "W": 24.07917164557845, "J_1KI": 8334.256306743622, "W_1KI": 240.79171645578452, "W_D": 5.603171645578453, "J_D": 193.9363584108353, "W_D_1KI": 56.031716455784526, "J_D_1KI": 560.3171645578453}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..99ea343
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.5 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.49390745162964}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2475,     4991,  ..., 12495070,
+                            12497536, 12500000]),
+       col_indices=tensor([   3,    6,    7,  ..., 4992, 4996, 4999]),
+       values=tensor([0.7861, 0.1444, 0.2009,  ..., 0.5207, 0.8919, 0.5019]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4801, 0.0235, 0.0420,  ..., 0.5930, 0.2408, 0.0610])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 26.49390745162964 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2475,     4991,  ..., 12495070,
+                            12497536, 12500000]),
+       col_indices=tensor([   3,    6,    7,  ..., 4992, 4996, 4999]),
+       values=tensor([0.7861, 0.1444, 0.2009,  ..., 0.5207, 0.8919, 0.5019]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4801, 0.0235, 0.0420,  ..., 0.5930, 0.2408, 0.0610])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 26.49390745162964 seconds
+
+[20.72, 20.56, 20.56, 20.36, 20.36, 20.36, 20.28, 20.56, 20.72, 21.08]
+[21.48, 21.52, 24.72, 26.76, 28.04, 29.8, 31.48, 31.48, 29.76, 28.44, 27.2, 25.96, 25.12, 24.52, 24.64, 24.64, 24.72, 24.52, 24.56, 24.68, 24.68, 24.6, 24.52, 24.2, 24.32, 24.24, 24.16, 24.4, 24.44, 24.44, 24.44, 24.6, 24.36]
+34.611889600753784
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.49390745162964, 'TIME_S_1KI': 264.9390745162964, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 833.4256306743622, 'W': 24.07917164557845}
+[20.72, 20.56, 20.56, 20.36, 20.36, 20.36, 20.28, 20.56, 20.72, 21.08, 20.64, 20.64, 20.36, 20.36, 20.56, 20.64, 20.68, 20.56, 20.56, 20.36]
+369.52
+18.476
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.49390745162964, 'TIME_S_1KI': 264.9390745162964, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 833.4256306743622, 'W': 24.07917164557845, 'J_1KI': 8334.256306743622, 'W_1KI': 240.79171645578452, 'W_D': 5.603171645578453, 'J_D': 193.9363584108353, 'W_D_1KI': 56.031716455784526, 'J_D_1KI': 560.3171645578453}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..732c4d4
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 289937, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.948570489883423, "TIME_S_1KI": 0.037761894790535266, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 337.0917576313019, "W": 22.992700250818316, "J_1KI": 1.1626379442130597, "W_1KI": 0.07930240104166876, "W_D": 4.582700250818316, "J_D": 67.18612713575365, "W_D_1KI": 0.015805848342289243, "J_D_1KI": 5.4514768181671336e-05}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..90b44fb
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,329 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.012730121612548828}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([ 601, 2647,  820, 2703, 4832, 2905, 4036,  996, 2456,
+                           2955, 1267, 3283, 2251, 4368, 2032, 1143, 1874, 4481,
+                           3312,  337, 3271, 2673, 2707,  912, 1355,  576, 4171,
+                           1338, 4509, 2894, 3669,  436,  629, 3354, 3800, 1938,
+                           3841, 3356, 3452, 2739,  639, 4726, 1185, 2215, 4780,
+                           2365,  813, 3529, 3133, 2027, 1430, 1360, 1560, 4504,
+                           4891,  990,  715, 1174, 4133, 1335, 2115, 2803,  644,
+                           1222,  359, 4157, 1393,  961, 2251, 4773,  836, 2710,
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+                      0.9943, 0.0815, 0.8720, 0.8261, 0.8846]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4430, 0.7360, 0.8513,  ..., 0.2058, 0.5954, 0.5363])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.012730121612548828 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 82481 -ss 5000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.9870200157165527}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1850, 2920,  982, 2920, 4207, 4970,  436, 2573, 4448,
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+                      0.5184, 0.3878, 0.1200, 0.4588, 0.2915]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.1901, 0.2382, 0.6937,  ..., 0.7632, 0.8746, 0.1540])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 2.9870200157165527 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 289937 -ss 5000 -sd 1e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.948570489883423}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([4712, 4178, 2903,   24, 4753,   51, 2819, 4572, 4453,
+                           3780, 2899, 3226, 3780, 3989,  722, 4731, 1139, 1109,
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+                           3429,  693,  870, 4939, 3305, 4250, 3680]),
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+                      0.4951, 0.3638, 0.3016, 0.7272, 0.7832, 0.5085, 0.1101,
+                      0.2648, 0.6399, 0.4137, 0.5843, 0.7184]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.0011, 0.9357, 0.8539,  ..., 0.1995, 0.1479, 0.1616])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.948570489883423 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([4712, 4178, 2903,   24, 4753,   51, 2819, 4572, 4453,
+                           3780, 2899, 3226, 3780, 3989,  722, 4731, 1139, 1109,
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+                           2678, 4365,  692, 1811, 1383, 2901,  749, 3344, 1016,
+                           4896, 4731,  857, 4171, 1998, 4569, 2011, 3832, 2691,
+                           1005, 4276, 2954, 4491, 2491, 2981,  645, 4461, 2128,
+                           3675, 4293, 1741, 3314, 1065, 1939, 1615, 3365, 3901,
+                            589, 3305, 4000, 4212,  790, 4927, 4076, 2238, 4107,
+                           3701, 3348, 1617, 1179, 3888, 4445, 2667, 3215, 4009,
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+                           4749, 4910, 1969, 2508,  532, 1736, 4315, 1491,  537,
+                           3309, 3121, 4585, 2996, 3358,  502, 4286, 4572, 2864,
+                           1049,  469,  825, 1143,  635, 2773, 2543, 3425, 3473,
+                           2174, 4228, 3516, 1137, 2463, 4638, 1994, 2452, 2065,
+                             96, 3029, 2790, 1834, 4863,  978, 4811, 3677, 2912,
+                           1938, 2797,  895, 1501, 2558, 1230,  534, 2633, 3017,
+                           4982, 4618, 4241, 2899, 2098, 2010, 1636, 2502, 2716,
+                           4980,  363,  466,   23, 1737, 1476, 1286, 4720,  833,
+                           2653,  201, 3769, 3397, 3009, 4570, 2692, 2095, 4797,
+                           3941, 2845, 1360, 1763, 3589, 3716, 2365,  196, 1112,
+                            123, 2267, 4731,  228, 4673, 1590, 3794, 3816, 2846,
+                            863, 3759, 1182,  304, 2540,   66, 4385, 3694, 3525,
+                             31, 4315, 4266, 4089, 2728, 1405, 1294, 4022, 2222,
+                            370,  101, 3253, 4145, 1994, 1358,  981, 2203, 2167,
+                           3742, 4696,  614, 2733,  396, 4399,  427, 1682, 4896,
+                           3429,  693,  870, 4939, 3305, 4250, 3680]),
+       values=tensor([0.3479, 0.5084, 0.6603, 0.8257, 0.8683, 0.9247, 0.0338,
+                      0.9486, 0.8504, 0.5745, 0.3925, 0.3196, 0.6449, 0.2119,
+                      0.0164, 0.7309, 0.7682, 0.1461, 0.7397, 0.9951, 0.7123,
+                      0.4571, 0.7549, 0.0282, 0.5968, 0.6667, 0.3749, 0.3789,
+                      0.4293, 0.3353, 0.3273, 0.0531, 0.5787, 0.8917, 0.4198,
+                      0.7695, 0.7895, 0.7926, 0.6654, 0.0192, 0.0703, 0.9096,
+                      0.9289, 0.6077, 0.6990, 0.6780, 0.1687, 0.0557, 0.0641,
+                      0.1726, 0.7968, 0.1192, 0.9982, 0.1104, 0.3778, 0.1311,
+                      0.0584, 0.9615, 0.6551, 0.7173, 0.4827, 0.9281, 0.2508,
+                      0.5901, 0.8616, 0.6261, 0.7668, 0.8880, 0.5680, 0.6476,
+                      0.9494, 0.3895, 0.7153, 0.7995, 0.4681, 0.0628, 0.0354,
+                      0.8123, 0.7147, 0.5397, 0.7785, 0.1737, 0.3550, 0.8870,
+                      0.9193, 0.0915, 0.0963, 0.4243, 0.0483, 0.3655, 0.7711,
+                      0.4395, 0.3161, 0.5266, 0.7991, 0.4530, 0.0590, 0.9302,
+                      0.7021, 0.5336, 0.6784, 0.9823, 0.0943, 0.7391, 0.7084,
+                      0.0171, 0.4786, 0.7623, 0.5776, 0.2256, 0.8698, 0.1309,
+                      0.6095, 0.6277, 0.0828, 0.3536, 0.7932, 0.1162, 0.9939,
+                      0.6893, 0.6054, 0.2963, 0.4057, 0.5571, 0.8162, 0.7161,
+                      0.6029, 0.7576, 0.8687, 0.3351, 0.8262, 0.5784, 0.6376,
+                      0.1057, 0.2968, 0.0568, 0.6646, 0.7354, 0.2403, 0.0158,
+                      0.7552, 0.5770, 0.3899, 0.7014, 0.1196, 0.2500, 0.6112,
+                      0.3203, 0.8311, 0.8445, 0.8722, 0.6620, 0.5633, 0.3401,
+                      0.0024, 0.6473, 0.3675, 0.6286, 0.4764, 0.3994, 0.7176,
+                      0.9295, 0.7610, 0.0448, 0.1910, 0.5959, 0.2410, 0.6714,
+                      0.3638, 0.8788, 0.4303, 0.8357, 0.1493, 0.7533, 0.2046,
+                      0.6241, 0.3330, 0.7519, 0.0927, 0.5403, 0.3301, 0.0842,
+                      0.3044, 0.5311, 0.1859, 0.7234, 0.6523, 0.1074, 0.7205,
+                      0.0951, 0.9394, 0.8290, 0.0965, 0.9119, 0.9547, 0.5884,
+                      0.2956, 0.6206, 0.7425, 0.9894, 0.3994, 0.9059, 0.3500,
+                      0.1825, 0.6628, 0.6687, 0.3257, 0.5028, 0.7592, 0.5362,
+                      0.2886, 0.3968, 0.4420, 0.4118, 0.6245, 0.3599, 0.5238,
+                      0.6126, 0.6306, 0.0343, 0.1672, 0.1822, 0.1255, 0.6333,
+                      0.3425, 0.1597, 0.8225, 0.7857, 0.7675, 0.1595, 0.4863,
+                      0.8578, 0.1155, 0.8038, 0.8906, 0.6082, 0.3640, 0.5820,
+                      0.4951, 0.3638, 0.3016, 0.7272, 0.7832, 0.5085, 0.1101,
+                      0.2648, 0.6399, 0.4137, 0.5843, 0.7184]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.0011, 0.9357, 0.8539,  ..., 0.1995, 0.1479, 0.1616])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.948570489883423 seconds
+
+[20.16, 20.24, 20.28, 20.24, 20.08, 20.16, 20.2, 20.28, 20.28, 20.36]
+[20.72, 20.56, 20.44, 24.96, 27.52, 28.24, 29.2, 26.64, 25.12, 24.04, 23.96, 23.92, 24.04, 24.2]
+14.660816431045532
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 289937, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.948570489883423, 'TIME_S_1KI': 0.037761894790535266, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 337.0917576313019, 'W': 22.992700250818316}
+[20.16, 20.24, 20.28, 20.24, 20.08, 20.16, 20.2, 20.28, 20.28, 20.36, 20.4, 20.4, 20.44, 20.84, 20.84, 20.76, 20.64, 20.8, 20.84, 20.84]
+368.2
+18.41
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 289937, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.948570489883423, 'TIME_S_1KI': 0.037761894790535266, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 337.0917576313019, 'W': 22.992700250818316, 'J_1KI': 1.1626379442130597, 'W_1KI': 0.07930240104166876, 'W_D': 4.582700250818316, 'J_D': 67.18612713575365, 'W_D_1KI': 0.015805848342289243, 'J_D_1KI': 5.4514768181671336e-05}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..8ad2558
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 1, "ITERATIONS": 154350, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.289572715759277, "TIME_S_1KI": 0.0666638983852237, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 297.04585779190063, "W": 21.84801552939114, "J_1KI": 1.9244953533650835, "W_1KI": 0.14154852950690727, "W_D": 3.3270155293911436, "J_D": 45.23413947987558, "W_D_1KI": 0.0215550082888963, "J_D_1KI": 0.0001396501994745468}
diff --git a/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..f253283
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/altra_1_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.014810323715209961}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4902, 4751,  573,  ..., 1409, 1871,  577]),
+       values=tensor([0.0874, 0.7756, 0.4965,  ..., 0.1251, 0.3364, 0.3476]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.4221, 0.7918, 0.4416,  ..., 0.8475, 0.7362, 0.1103])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.014810323715209961 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 70896 -ss 5000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.822846412658691}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([1594, 2931, 2652,  ..., 4428,  449, 1795]),
+       values=tensor([0.3058, 0.1710, 0.0965,  ..., 0.7799, 0.8373, 0.5140]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.0899, 0.4612, 0.1283,  ..., 0.7452, 0.2953, 0.1670])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 4.822846412658691 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 154350 -ss 5000 -sd 5e-05 -c 1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.289572715759277}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([ 621, 1968, 1113,  ..., 1968,  726, 3393]),
+       values=tensor([0.9316, 0.3440, 0.3874,  ..., 0.4845, 0.3520, 0.3225]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.4702, 0.8122, 0.0166,  ..., 0.1291, 0.0008, 0.5220])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.289572715759277 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([ 621, 1968, 1113,  ..., 1968,  726, 3393]),
+       values=tensor([0.9316, 0.3440, 0.3874,  ..., 0.4845, 0.3520, 0.3225]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.4702, 0.8122, 0.0166,  ..., 0.1291, 0.0008, 0.5220])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.289572715759277 seconds
+
+[20.4, 20.44, 20.52, 20.68, 20.64, 20.48, 20.24, 20.36, 20.2, 20.16]
+[20.36, 20.44, 21.32, 23.28, 23.28, 24.96, 25.6, 26.08, 24.88, 23.92, 23.64, 23.6, 23.6]
+13.59601092338562
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 154350, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.289572715759277, 'TIME_S_1KI': 0.0666638983852237, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 297.04585779190063, 'W': 21.84801552939114}
+[20.4, 20.44, 20.52, 20.68, 20.64, 20.48, 20.24, 20.36, 20.2, 20.16, 20.52, 20.64, 20.96, 21.2, 21.24, 21.04, 20.76, 20.36, 20.08, 20.08]
+370.41999999999996
+18.520999999999997
+{'CPU': 'Altra', 'CORES': 1, 'ITERATIONS': 154350, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.289572715759277, 'TIME_S_1KI': 0.0666638983852237, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 297.04585779190063, 'W': 21.84801552939114, 'J_1KI': 1.9244953533650835, 'W_1KI': 0.14154852950690727, 'W_D': 3.3270155293911436, 'J_D': 45.23413947987558, 'W_D_1KI': 0.0215550082888963, 'J_D_1KI': 0.0001396501994745468}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..57ab5e0
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 6238, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.769784212112427, "TIME_S_1KI": 1.7264803161449866, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 915.7679015994072, "W": 67.11, "J_1KI": 146.8047293362307, "W_1KI": 10.75825585123437, "W_D": 31.7095, "J_D": 432.70067465007304, "W_D_1KI": 5.083279897403013, "J_D_1KI": 0.8148893711771422}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..fe560b9
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.18373441696166992}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      18,  ...,  999979,
+                             999991, 1000000]),
+       col_indices=tensor([ 9419, 17690, 19775,  ..., 65904, 78906, 97730]),
+       values=tensor([0.1002, 0.0063, 0.1334,  ..., 0.8477, 0.2339, 0.2955]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9261, 0.9567, 0.5751,  ..., 0.3199, 0.0262, 0.3042])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.18373441696166992 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '5714', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.617395401000977}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      15,  ...,  999976,
+                             999989, 1000000]),
+       col_indices=tensor([12342, 20602, 31374,  ..., 83399, 88988, 97850]),
+       values=tensor([0.3594, 0.1684, 0.5761,  ..., 0.4601, 0.3694, 0.1608]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5848, 0.1566, 0.7046,  ..., 0.5634, 0.8550, 0.2097])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 9.617395401000977 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '6238', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.769784212112427}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       7,      15,  ...,  999972,
+                             999990, 1000000]),
+       col_indices=tensor([ 1977,  7363, 16479,  ..., 91067, 93957, 95744]),
+       values=tensor([0.8934, 0.4616, 0.7140,  ..., 0.3224, 0.7140, 0.9696]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0584, 0.0097, 0.6336,  ..., 0.7366, 0.8575, 0.7006])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.769784212112427 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       7,      15,  ...,  999972,
+                             999990, 1000000]),
+       col_indices=tensor([ 1977,  7363, 16479,  ..., 91067, 93957, 95744]),
+       values=tensor([0.8934, 0.4616, 0.7140,  ..., 0.3224, 0.7140, 0.9696]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0584, 0.0097, 0.6336,  ..., 0.7366, 0.8575, 0.7006])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.769784212112427 seconds
+
+[39.95, 40.61, 39.69, 39.43, 39.55, 38.89, 39.33, 39.12, 39.11, 38.94]
+[67.11]
+13.645774126052856
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6238, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.769784212112427, 'TIME_S_1KI': 1.7264803161449866, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 915.7679015994072, 'W': 67.11}
+[39.95, 40.61, 39.69, 39.43, 39.55, 38.89, 39.33, 39.12, 39.11, 38.94, 39.57, 38.89, 39.02, 39.08, 39.5, 39.31, 39.6, 39.05, 39.01, 39.18]
+708.01
+35.4005
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 6238, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.769784212112427, 'TIME_S_1KI': 1.7264803161449866, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 915.7679015994072, 'W': 67.11, 'J_1KI': 146.8047293362307, 'W_1KI': 10.75825585123437, 'W_D': 31.7095, 'J_D': 432.70067465007304, 'W_D_1KI': 5.083279897403013, 'J_D_1KI': 0.8148893711771422}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..a6d9713
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 631, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.591248035430908, "TIME_S_1KI": 16.78486217976372, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1175.510341129303, "W": 76.44, "J_1KI": 1862.932394816645, "W_1KI": 121.14104595879556, "W_D": 40.61775, "J_D": 624.6282726112604, "W_D_1KI": 64.37044374009508, "J_D_1KI": 102.0133815215453}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..4526ab4
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 1.6621592044830322}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       87,      180,  ...,  9999810,
+                             9999900, 10000000]),
+       col_indices=tensor([ 1316,  2180,  2488,  ..., 99391, 99679, 99852]),
+       values=tensor([0.4838, 0.8512, 0.8260,  ..., 0.7772, 0.9919, 0.5400]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7127, 0.0015, 0.2736,  ..., 0.7345, 0.7377, 0.4477])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 1.6621592044830322 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '631', '-ss', '100000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.591248035430908}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      104,      198,  ...,  9999801,
+                             9999900, 10000000]),
+       col_indices=tensor([ 1720,  2057,  4608,  ..., 98148, 99667, 99757]),
+       values=tensor([0.5091, 0.6981, 0.1321,  ..., 0.4342, 0.6647, 0.6565]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3567, 0.4501, 0.1430,  ..., 0.3086, 0.4387, 0.0746])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.591248035430908 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      104,      198,  ...,  9999801,
+                             9999900, 10000000]),
+       col_indices=tensor([ 1720,  2057,  4608,  ..., 98148, 99667, 99757]),
+       values=tensor([0.5091, 0.6981, 0.1321,  ..., 0.4342, 0.6647, 0.6565]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3567, 0.4501, 0.1430,  ..., 0.3086, 0.4387, 0.0746])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.591248035430908 seconds
+
+[39.78, 39.0, 39.24, 38.88, 39.37, 39.28, 39.34, 38.84, 41.87, 41.9]
+[76.44]
+15.378209590911865
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 631, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.591248035430908, 'TIME_S_1KI': 16.78486217976372, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1175.510341129303, 'W': 76.44}
+[39.78, 39.0, 39.24, 38.88, 39.37, 39.28, 39.34, 38.84, 41.87, 41.9, 39.74, 39.44, 39.02, 44.31, 40.1, 38.93, 39.71, 39.12, 39.62, 39.33]
+716.4449999999999
+35.82225
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 631, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.591248035430908, 'TIME_S_1KI': 16.78486217976372, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1175.510341129303, 'W': 76.44, 'J_1KI': 1862.932394816645, 'W_1KI': 121.14104595879556, 'W_D': 40.61775, 'J_D': 624.6282726112604, 'W_D_1KI': 64.37044374009508, 'J_D_1KI': 102.0133815215453}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..87f4ecf
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 12301, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.471284627914429, "TIME_S_1KI": 0.8512547457860685, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 857.4546840524674, "W": 65.29, "J_1KI": 69.70609576883729, "W_1KI": 5.307698561092595, "W_D": 29.40700000000001, "J_D": 386.2026327757837, "W_D_1KI": 2.3906186488903347, "J_D_1KI": 0.19434343946755017}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..e82259e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.09777641296386719}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([10415, 34481, 41161,  ..., 69185,  8793, 68858]),
+       values=tensor([0.7697, 0.4410, 0.3075,  ..., 0.8657, 0.1828, 0.6667]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2533, 0.9138, 0.2717,  ..., 0.2019, 0.7103, 0.0862])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.09777641296386719 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '10738', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.165135860443115}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      2,  ...,  99996,  99996,
+                            100000]),
+       col_indices=tensor([17140, 55127, 70380,  ...,  9005, 21920, 77148]),
+       values=tensor([0.4913, 0.5196, 0.1867,  ..., 0.0903, 0.8718, 0.1023]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0174, 0.3477, 0.7027,  ..., 0.9312, 0.2138, 0.3974])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 9.165135860443115 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12301', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.471284627914429}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      5,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([46597,   403, 54918,  ..., 58141, 94085, 20979]),
+       values=tensor([0.5040, 0.7325, 0.7996,  ..., 0.9839, 0.2631, 0.4936]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2071, 0.7418, 0.9347,  ..., 0.4731, 0.1489, 0.5724])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.471284627914429 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      5,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([46597,   403, 54918,  ..., 58141, 94085, 20979]),
+       values=tensor([0.5040, 0.7325, 0.7996,  ..., 0.9839, 0.2631, 0.4936]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2071, 0.7418, 0.9347,  ..., 0.4731, 0.1489, 0.5724])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.471284627914429 seconds
+
+[39.92, 39.64, 39.53, 39.59, 39.42, 39.38, 39.39, 39.02, 44.4, 39.0]
+[65.29]
+13.13301706314087
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12301, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.471284627914429, 'TIME_S_1KI': 0.8512547457860685, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.4546840524674, 'W': 65.29}
+[39.92, 39.64, 39.53, 39.59, 39.42, 39.38, 39.39, 39.02, 44.4, 39.0, 39.77, 38.96, 39.1, 39.29, 38.97, 44.08, 39.5, 39.1, 39.5, 38.89]
+717.66
+35.882999999999996
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 12301, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.471284627914429, 'TIME_S_1KI': 0.8512547457860685, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 857.4546840524674, 'W': 65.29, 'J_1KI': 69.70609576883729, 'W_1KI': 5.307698561092595, 'W_D': 29.40700000000001, 'J_D': 386.2026327757837, 'W_D_1KI': 2.3906186488903347, 'J_D_1KI': 0.19434343946755017}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..96abffc
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 7670, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.499921083450317, "TIME_S_1KI": 1.3689597240482814, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 873.9648419952392, "W": 65.94, "J_1KI": 113.94587248960094, "W_1KI": 8.597131681877444, "W_D": 30.4285, "J_D": 403.29753100776674, "W_D_1KI": 3.9672099087353327, "J_D_1KI": 0.5172372762366796}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..7c108ed
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.15105009078979492}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      7,  ..., 499997, 499999,
+                            500000]),
+       col_indices=tensor([ 4363, 49954, 63940,  ...,   740, 19551, 36085]),
+       values=tensor([0.7532, 0.6946, 0.3669,  ..., 0.0744, 0.6590, 0.6868]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.0672, 0.6383, 0.6761,  ..., 0.1188, 0.9489, 0.0863])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 0.15105009078979492 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '6951', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.51450252532959}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     13,  ..., 499996, 499997,
+                            500000]),
+       col_indices=tensor([ 4260, 42899, 54575,  ...,  5425, 31756, 61151]),
+       values=tensor([0.4952, 0.8247, 0.2969,  ..., 0.2331, 0.9267, 0.2319]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.7470, 0.2926, 0.2731,  ..., 0.9830, 0.8295, 0.9958])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 9.51450252532959 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '7670', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.499921083450317}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     18,  ..., 499989, 499996,
+                            500000]),
+       col_indices=tensor([16754, 23077, 28797,  ..., 22620, 46442, 72952]),
+       values=tensor([0.6737, 0.8129, 0.9335,  ..., 0.4581, 0.1021, 0.2391]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.9975, 0.9245, 0.4309,  ..., 0.4303, 0.6144, 0.3183])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.499921083450317 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     18,  ..., 499989, 499996,
+                            500000]),
+       col_indices=tensor([16754, 23077, 28797,  ..., 22620, 46442, 72952]),
+       values=tensor([0.6737, 0.8129, 0.9335,  ..., 0.4581, 0.1021, 0.2391]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.9975, 0.9245, 0.4309,  ..., 0.4303, 0.6144, 0.3183])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.499921083450317 seconds
+
+[39.66, 38.93, 40.25, 39.53, 39.7, 39.41, 40.1, 39.55, 39.0, 39.05]
+[65.94]
+13.25394058227539
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 7670, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.499921083450317, 'TIME_S_1KI': 1.3689597240482814, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 873.9648419952392, 'W': 65.94}
+[39.66, 38.93, 40.25, 39.53, 39.7, 39.41, 40.1, 39.55, 39.0, 39.05, 39.72, 39.51, 39.22, 39.24, 39.32, 39.23, 40.03, 39.22, 39.23, 39.09]
+710.23
+35.5115
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 7670, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.499921083450317, 'TIME_S_1KI': 1.3689597240482814, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 873.9648419952392, 'W': 65.94, 'J_1KI': 113.94587248960094, 'W_1KI': 8.597131681877444, 'W_D': 30.4285, 'J_D': 403.29753100776674, 'W_D_1KI': 3.9672099087353327, 'J_D_1KI': 0.5172372762366796}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..a0173f2
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 237172, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.292231321334839, "TIME_S_1KI": 0.04339564249293693, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 862.8344623732567, "W": 65.91, "J_1KI": 3.638011495342016, "W_1KI": 0.2778995834246875, "W_D": 30.572500000000005, "J_D": 400.22768321812157, "W_D_1KI": 0.12890433946671614, "J_D_1KI": 0.0005435057235538602}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..104c518
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.016118526458740234}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     3,  ...,  9995,  9998, 10000]),
+       col_indices=tensor([3770, 7218, 7901,  ..., 7147, 2189, 2422]),
+       values=tensor([0.0682, 0.4925, 0.4932,  ..., 0.9859, 0.2682, 0.5675]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1703, 0.4753, 0.7272,  ..., 0.9852, 0.8357, 0.1698])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.016118526458740234 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65142', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.8839359283447266}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([6160, 1315,  448,  ..., 9882, 6598, 7658]),
+       values=tensor([0.4764, 0.2622, 0.7017,  ..., 0.9860, 0.1866, 0.7529]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.4338, 0.9515, 0.6308,  ..., 0.9365, 0.1556, 0.4912])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 2.8839359283447266 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '237172', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.292231321334839}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     3,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([1278, 7265, 6993,  ..., 9863, 6468, 3133]),
+       values=tensor([0.6288, 0.8682, 0.0748,  ..., 0.3062, 0.2031, 0.3525]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1078, 0.8244, 0.8698,  ..., 0.0830, 0.2322, 0.6518])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.292231321334839 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     3,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([1278, 7265, 6993,  ..., 9863, 6468, 3133]),
+       values=tensor([0.6288, 0.8682, 0.0748,  ..., 0.3062, 0.2031, 0.3525]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1078, 0.8244, 0.8698,  ..., 0.0830, 0.2322, 0.6518])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.292231321334839 seconds
+
+[40.27, 39.31, 39.28, 38.86, 41.16, 39.32, 39.02, 39.09, 38.81, 39.11]
+[65.91]
+13.091100931167603
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 237172, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.292231321334839, 'TIME_S_1KI': 0.04339564249293693, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.8344623732567, 'W': 65.91}
+[40.27, 39.31, 39.28, 38.86, 41.16, 39.32, 39.02, 39.09, 38.81, 39.11, 39.9, 39.01, 39.35, 39.15, 39.0, 38.86, 39.09, 38.89, 38.89, 40.04]
+706.7499999999999
+35.33749999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 237172, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.292231321334839, 'TIME_S_1KI': 0.04339564249293693, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 862.8344623732567, 'W': 65.91, 'J_1KI': 3.638011495342016, 'W_1KI': 0.2778995834246875, 'W_D': 30.572500000000005, 'J_D': 400.22768321812157, 'W_D_1KI': 0.12890433946671614, 'J_D_1KI': 0.0005435057235538602}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..deb9985
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 75716, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.424914360046387, "TIME_S_1KI": 0.1376844307682179, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 940.2084917426108, "W": 66.49, "J_1KI": 12.41756685169067, "W_1KI": 0.8781499286808601, "W_D": 31.17374999999999, "J_D": 440.81552819162596, "W_D_1KI": 0.4117194516350572, "J_D_1KI": 0.005437680960894093}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..169706c
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.02692556381225586}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     22,  ...,  99984,  99990,
+                            100000]),
+       col_indices=tensor([ 947, 1869, 5338,  ..., 6268, 7050, 7942]),
+       values=tensor([0.2237, 0.7540, 0.0617,  ..., 0.6862, 0.3906, 0.7890]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6838, 0.4222, 0.9597,  ..., 0.5474, 0.0680, 0.5394])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.02692556381225586 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '38996', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 5.952186584472656}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     19,  ...,  99978,  99990,
+                            100000]),
+       col_indices=tensor([   7,  556,  703,  ..., 8117, 8865, 9056]),
+       values=tensor([0.2495, 0.4435, 0.2550,  ..., 0.5409, 0.7823, 0.3947]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8603, 0.6651, 0.2785,  ..., 0.2036, 0.7755, 0.1415])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 5.952186584472656 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '68791', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.539567232131958}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     19,  ...,  99987,  99995,
+                            100000]),
+       col_indices=tensor([ 696,  997, 2062,  ..., 1211, 1590, 9690]),
+       values=tensor([0.0377, 0.1568, 0.2160,  ..., 0.8237, 0.6309, 0.0587]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0823, 0.1873, 0.3356,  ..., 0.2591, 0.5771, 0.7059])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 9.539567232131958 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '75716', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.424914360046387}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     17,  ...,  99986,  99994,
+                            100000]),
+       col_indices=tensor([1284, 3776, 5103,  ..., 6955, 7171, 8445]),
+       values=tensor([0.9684, 0.2053, 0.3935,  ..., 0.8592, 0.0314, 0.3677]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4306, 0.9725, 0.6597,  ..., 0.5969, 0.7821, 0.5134])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.424914360046387 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     17,  ...,  99986,  99994,
+                            100000]),
+       col_indices=tensor([1284, 3776, 5103,  ..., 6955, 7171, 8445]),
+       values=tensor([0.9684, 0.2053, 0.3935,  ..., 0.8592, 0.0314, 0.3677]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4306, 0.9725, 0.6597,  ..., 0.5969, 0.7821, 0.5134])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.424914360046387 seconds
+
+[40.22, 38.81, 38.98, 39.63, 38.95, 39.23, 38.84, 39.29, 39.31, 38.77]
+[66.49]
+14.140599966049194
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75716, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.424914360046387, 'TIME_S_1KI': 0.1376844307682179, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 940.2084917426108, 'W': 66.49}
+[40.22, 38.81, 38.98, 39.63, 38.95, 39.23, 38.84, 39.29, 39.31, 38.77, 41.54, 39.08, 39.06, 38.87, 39.0, 38.96, 39.2, 38.82, 39.06, 41.94]
+706.325
+35.316250000000004
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 75716, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.424914360046387, 'TIME_S_1KI': 0.1376844307682179, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 940.2084917426108, 'W': 66.49, 'J_1KI': 12.41756685169067, 'W_1KI': 0.8781499286808601, 'W_D': 31.17374999999999, 'J_D': 440.81552819162596, 'W_D_1KI': 0.4117194516350572, 'J_D_1KI': 0.005437680960894093}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..db6444c
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 10206, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.48923373222351, "TIME_S_1KI": 1.0277516884404774, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 888.1208229660987, "W": 67.21, "J_1KI": 87.01948098825189, "W_1KI": 6.585341955712326, "W_D": 31.27349999999999, "J_D": 413.25169702470293, "W_D_1KI": 3.0642269253380356, "J_D_1KI": 0.30023779397785966}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..191d2ac
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.1162419319152832}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     100,     185,  ...,  999774,
+                             999893, 1000000]),
+       col_indices=tensor([  36,  100,  149,  ..., 9802, 9836, 9872]),
+       values=tensor([0.2938, 0.2320, 0.9118,  ..., 0.8681, 0.8272, 0.2716]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9985, 0.1887, 0.5488,  ..., 0.6608, 0.9222, 0.7055])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.1162419319152832 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '9032', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.29158329963684}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      95,     205,  ...,  999782,
+                             999891, 1000000]),
+       col_indices=tensor([  54,  212,  264,  ..., 9693, 9804, 9961]),
+       values=tensor([0.9421, 0.7916, 0.1774,  ..., 0.7420, 0.5713, 0.3525]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0899, 0.7410, 0.9990,  ..., 0.5022, 0.0295, 0.8248])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 9.29158329963684 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '10206', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.48923373222351}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     108,     196,  ...,  999774,
+                             999884, 1000000]),
+       col_indices=tensor([  16,  259,  309,  ..., 9528, 9603, 9788]),
+       values=tensor([0.1649, 0.9890, 0.6907,  ..., 0.8956, 0.0145, 0.7596]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7287, 0.8351, 0.4943,  ..., 0.5583, 0.1274, 0.9823])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.48923373222351 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     108,     196,  ...,  999774,
+                             999884, 1000000]),
+       col_indices=tensor([  16,  259,  309,  ..., 9528, 9603, 9788]),
+       values=tensor([0.1649, 0.9890, 0.6907,  ..., 0.8956, 0.0145, 0.7596]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7287, 0.8351, 0.4943,  ..., 0.5583, 0.1274, 0.9823])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.48923373222351 seconds
+
+[39.9, 39.07, 39.52, 38.94, 39.35, 44.79, 39.52, 39.98, 39.47, 39.28]
+[67.21]
+13.214117288589478
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10206, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.48923373222351, 'TIME_S_1KI': 1.0277516884404774, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.1208229660987, 'W': 67.21}
+[39.9, 39.07, 39.52, 38.94, 39.35, 44.79, 39.52, 39.98, 39.47, 39.28, 40.29, 39.27, 43.98, 38.92, 39.16, 39.39, 39.32, 39.34, 39.08, 39.79]
+718.73
+35.9365
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 10206, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.48923373222351, 'TIME_S_1KI': 1.0277516884404774, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.1208229660987, 'W': 67.21, 'J_1KI': 87.01948098825189, 'W_1KI': 6.585341955712326, 'W_D': 31.27349999999999, 'J_D': 413.25169702470293, 'W_D_1KI': 3.0642269253380356, 'J_D_1KI': 0.30023779397785966}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..f94ac11
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1725, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.28298306465149, "TIME_S_1KI": 5.961149602696516, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1049.8662384581567, "W": 75.33, "J_1KI": 608.6181092511052, "W_1KI": 43.66956521739131, "W_D": 39.905, "J_D": 556.151762188673, "W_D_1KI": 23.133333333333336, "J_D_1KI": 13.410628019323674}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..bf02e51
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.6085023880004883}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     508,     930,  ..., 4999014,
+                            4999519, 5000000]),
+       col_indices=tensor([  33,   44,   68,  ..., 9921, 9984, 9990]),
+       values=tensor([0.7535, 0.2308, 0.9086,  ..., 0.5781, 0.9835, 0.5048]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1644, 0.2567, 0.4067,  ..., 0.0618, 0.3860, 0.0437])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 0.6085023880004883 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1725', '-ss', '10000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.28298306465149}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     453,     934,  ..., 4998993,
+                            4999474, 5000000]),
+       col_indices=tensor([  76,   82,   85,  ..., 9960, 9963, 9989]),
+       values=tensor([0.2757, 0.2788, 0.5904,  ..., 0.0782, 0.3342, 0.9799]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.0884, 0.4732, 0.8375,  ..., 0.9901, 0.5525, 0.7748])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.28298306465149 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     453,     934,  ..., 4998993,
+                            4999474, 5000000]),
+       col_indices=tensor([  76,   82,   85,  ..., 9960, 9963, 9989]),
+       values=tensor([0.2757, 0.2788, 0.5904,  ..., 0.0782, 0.3342, 0.9799]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.0884, 0.4732, 0.8375,  ..., 0.9901, 0.5525, 0.7748])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.28298306465149 seconds
+
+[40.86, 38.91, 38.99, 39.35, 38.92, 38.87, 39.32, 39.52, 39.41, 41.56]
+[75.33]
+13.936894178390503
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1725, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.28298306465149, 'TIME_S_1KI': 5.961149602696516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.8662384581567, 'W': 75.33}
+[40.86, 38.91, 38.99, 39.35, 38.92, 38.87, 39.32, 39.52, 39.41, 41.56, 39.65, 38.96, 39.21, 38.88, 39.0, 40.01, 39.33, 39.71, 39.42, 39.31]
+708.5
+35.425
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1725, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.28298306465149, 'TIME_S_1KI': 5.961149602696516, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.8662384581567, 'W': 75.33, 'J_1KI': 608.6181092511052, 'W_1KI': 43.66956521739131, 'W_D': 39.905, 'J_D': 556.151762188673, 'W_D_1KI': 23.133333333333336, 'J_D_1KI': 13.410628019323674}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..02b08b6
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 700, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.428780555725098, "TIME_S_1KI": 14.89825793675014, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1164.5532444572448, "W": 76.66, "J_1KI": 1663.6474920817784, "W_1KI": 109.5142857142857, "W_D": 40.888999999999996, "J_D": 621.1507645788192, "W_D_1KI": 58.412857142857135, "J_D_1KI": 83.44693877551019}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..f0881c0
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 1.498870611190796}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      987,     1979,  ...,  9997999,
+                             9999011, 10000000]),
+       col_indices=tensor([   3,    7,   20,  ..., 9954, 9962, 9986]),
+       values=tensor([0.9369, 0.1464, 0.7342,  ..., 0.7208, 0.8895, 0.6454]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7880, 0.5272, 0.7128,  ..., 0.1762, 0.3407, 0.4321])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 1.498870611190796 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '700', '-ss', '10000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.428780555725098}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      995,     2027,  ...,  9997983,
+                             9998981, 10000000]),
+       col_indices=tensor([  16,   21,   33,  ..., 9977, 9983, 9988]),
+       values=tensor([0.3684, 0.6722, 0.7880,  ..., 0.5048, 0.0966, 0.9792]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4422, 0.8800, 0.2165,  ..., 0.4558, 0.6103, 0.1393])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.428780555725098 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      995,     2027,  ...,  9997983,
+                             9998981, 10000000]),
+       col_indices=tensor([  16,   21,   33,  ..., 9977, 9983, 9988]),
+       values=tensor([0.3684, 0.6722, 0.7880,  ..., 0.5048, 0.0966, 0.9792]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4422, 0.8800, 0.2165,  ..., 0.4558, 0.6103, 0.1393])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.428780555725098 seconds
+
+[40.18, 38.97, 39.04, 39.1, 38.99, 38.9, 38.95, 39.03, 44.22, 39.22]
+[76.66]
+15.191145896911621
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 700, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.428780555725098, 'TIME_S_1KI': 14.89825793675014, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1164.5532444572448, 'W': 76.66}
+[40.18, 38.97, 39.04, 39.1, 38.99, 38.9, 38.95, 39.03, 44.22, 39.22, 40.59, 39.18, 39.25, 44.66, 38.99, 39.16, 38.92, 38.8, 39.17, 40.19]
+715.42
+35.771
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 700, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.428780555725098, 'TIME_S_1KI': 14.89825793675014, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1164.5532444572448, 'W': 76.66, 'J_1KI': 1663.6474920817784, 'W_1KI': 109.5142857142857, 'W_D': 40.888999999999996, 'J_D': 621.1507645788192, 'W_D_1KI': 58.412857142857135, 'J_D_1KI': 83.44693877551019}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..a0b1b52
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 343, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.229193925857544, "TIME_S_1KI": 29.822722815911206, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1277.0280195808411, "W": 76.08, "J_1KI": 3723.1137597109073, "W_1KI": 221.8075801749271, "W_D": 40.66575, "J_D": 682.5880939441324, "W_D_1KI": 118.55903790087464, "J_D_1KI": 345.65317172266657}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..9529016
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 3.0540103912353516}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2046,     4073,  ..., 19996013,
+                            19997969, 20000000]),
+       col_indices=tensor([   3,    8,   17,  ..., 9981, 9984, 9987]),
+       values=tensor([0.5017, 0.4094, 0.1287,  ..., 0.4741, 0.2195, 0.3916]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.6824, 0.3340, 0.1820,  ..., 0.2779, 0.3641, 0.6445])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 3.0540103912353516 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '343', '-ss', '10000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.229193925857544}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1942,     3919,  ..., 19996087,
+                            19998013, 20000000]),
+       col_indices=tensor([   9,   10,   17,  ..., 9985, 9988, 9989]),
+       values=tensor([0.3594, 0.3340, 0.0020,  ..., 0.8034, 0.9201, 0.1838]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.0654, 0.1078, 0.4601,  ..., 0.8409, 0.3729, 0.1721])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.229193925857544 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1942,     3919,  ..., 19996087,
+                            19998013, 20000000]),
+       col_indices=tensor([   9,   10,   17,  ..., 9985, 9988, 9989]),
+       values=tensor([0.3594, 0.3340, 0.0020,  ..., 0.8034, 0.9201, 0.1838]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.0654, 0.1078, 0.4601,  ..., 0.8409, 0.3729, 0.1721])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.229193925857544 seconds
+
+[39.53, 39.95, 39.42, 39.3, 39.16, 38.94, 39.07, 38.8, 39.46, 38.91]
+[76.08]
+16.78533148765564
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 343, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.229193925857544, 'TIME_S_1KI': 29.822722815911206, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1277.0280195808411, 'W': 76.08}
+[39.53, 39.95, 39.42, 39.3, 39.16, 38.94, 39.07, 38.8, 39.46, 38.91, 39.85, 38.96, 39.07, 39.05, 39.38, 38.91, 39.06, 39.15, 39.29, 44.34]
+708.285
+35.414249999999996
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 343, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.229193925857544, 'TIME_S_1KI': 29.822722815911206, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1277.0280195808411, 'W': 76.08, 'J_1KI': 3723.1137597109073, 'W_1KI': 221.8075801749271, 'W_D': 40.66575, 'J_D': 682.5880939441324, 'W_D_1KI': 118.55903790087464, 'J_D_1KI': 345.65317172266657}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..a1f4a32
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 233, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.295273542404175, "TIME_S_1KI": 44.18572335795783, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1458.0208019256593, "W": 76.48, "J_1KI": 6257.6000082646315, "W_1KI": 328.2403433476395, "W_D": 40.760999999999996, "J_D": 777.0709454405307, "W_D_1KI": 174.9399141630901, "J_D_1KI": 750.8150822450219}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..d790296
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 4.5049638748168945}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3029,     6057,  ..., 29993789,
+                            29996881, 30000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9988, 9991, 9998]),
+       values=tensor([0.8599, 0.6300, 0.6697,  ..., 0.0214, 0.0757, 0.9206]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.5404, 0.3446, 0.4295,  ..., 0.2969, 0.5137, 0.1316])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 4.5049638748168945 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '233', '-ss', '10000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.295273542404175}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2988,     6007,  ..., 29993964,
+                            29997046, 30000000]),
+       col_indices=tensor([   9,   16,   24,  ..., 9996, 9997, 9999]),
+       values=tensor([0.2433, 0.7720, 0.0178,  ..., 0.3342, 0.8303, 0.6867]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.4151, 0.6857, 0.4615,  ..., 0.0665, 0.4824, 0.1217])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.295273542404175 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2988,     6007,  ..., 29993964,
+                            29997046, 30000000]),
+       col_indices=tensor([   9,   16,   24,  ..., 9996, 9997, 9999]),
+       values=tensor([0.2433, 0.7720, 0.0178,  ..., 0.3342, 0.8303, 0.6867]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.4151, 0.6857, 0.4615,  ..., 0.0665, 0.4824, 0.1217])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.295273542404175 seconds
+
+[40.6, 39.0, 39.72, 40.47, 39.9, 39.66, 39.13, 38.97, 39.1, 39.62]
+[76.48]
+19.064079523086548
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+35.71900000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 233, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.295273542404175, 'TIME_S_1KI': 44.18572335795783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1458.0208019256593, 'W': 76.48, 'J_1KI': 6257.6000082646315, 'W_1KI': 328.2403433476395, 'W_D': 40.760999999999996, 'J_D': 777.0709454405307, 'W_D_1KI': 174.9399141630901, 'J_D_1KI': 750.8150822450219}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..9c3e1bc
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 362205, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.420795440673828, "TIME_S_1KI": 0.02877043508696409, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 858.7538476467132, "W": 65.88, "J_1KI": 2.3709055580312617, "W_1KI": 0.18188594856503915, "W_D": 30.515249999999995, "J_D": 397.7700113752484, "W_D_1KI": 0.08424856089783408, "J_D_1KI": 0.00023259911071860987}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..3b7f750
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1521 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.01423192024230957}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ...,  998,  999, 1000]),
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+                      5.3153e-01, 5.4974e-01, 9.8914e-01, 7.2178e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.1442, 0.3479, 0.0736,  ..., 0.7713, 0.5566, 0.1675])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.01423192024230957 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '73777', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.1387269496917725}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1000, 1000, 1000]),
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0752, 0.0265, 0.5369,  ..., 0.1368, 0.6161, 0.4463])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 2.1387269496917725 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '362205', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.420795440673828}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      2.6251e-01, 5.7401e-01, 2.8597e-02, 8.6585e-02]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8046, 0.6398, 0.4516,  ..., 0.6060, 0.1172, 0.9615])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.420795440673828 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([ 367, 5928, 4913, 9662, 9837, 1139, 7195,  709, 1186,
+                             80, 7429, 8354, 3731, 4238, 1204, 8286, 5350, 6518,
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8046, 0.6398, 0.4516,  ..., 0.6060, 0.1172, 0.9615])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.420795440673828 seconds
+
+[40.1, 39.18, 39.44, 38.84, 39.26, 38.78, 38.88, 39.27, 39.5, 39.18]
+[65.88]
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+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 362205, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.420795440673828, 'TIME_S_1KI': 0.02877043508696409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 858.7538476467132, 'W': 65.88}
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diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..44b0019
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 288650, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.542811155319214, "TIME_S_1KI": 0.036524549299564224, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 872.9266104698181, "W": 66.0, "J_1KI": 3.0241697920312425, "W_1KI": 0.22865061493157804, "W_D": 30.442750000000004, "J_D": 402.64070561939485, "W_D_1KI": 0.10546596223800452, "J_D_1KI": 0.00036537662303136846}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..ec44bfd
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.014984130859375}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([5455, 6096, 7620,  ..., 8334, 1515, 9556]),
+       values=tensor([0.3295, 0.9699, 0.1085,  ..., 0.1358, 0.2338, 0.9968]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.0525, 0.2160, 0.5197,  ..., 0.9729, 0.0490, 0.2973])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.014984130859375 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '70074', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.549025535583496}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([3016, 6372, 3284,  ..., 9865, 6936, 5486]),
+       values=tensor([0.2981, 0.0450, 0.6145,  ..., 0.3998, 0.9695, 0.2536]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.7165, 0.5810, 0.9668,  ..., 0.2745, 0.2690, 0.0815])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 2.549025535583496 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '288650', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.542811155319214}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([4532, 8082, 1862,  ..., 2662, 2473, 4062]),
+       values=tensor([0.2290, 0.0977, 0.7273,  ..., 0.3334, 0.1586, 0.6128]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.0256, 0.2861, 0.7976,  ..., 0.1212, 0.6310, 0.3680])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.542811155319214 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([4532, 8082, 1862,  ..., 2662, 2473, 4062]),
+       values=tensor([0.2290, 0.0977, 0.7273,  ..., 0.3334, 0.1586, 0.6128]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.0256, 0.2861, 0.7976,  ..., 0.1212, 0.6310, 0.3680])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.542811155319214 seconds
+
+[40.67, 38.88, 39.03, 38.97, 39.55, 38.85, 39.41, 38.82, 39.41, 38.86]
+[66.0]
+13.226160764694214
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 288650, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.542811155319214, 'TIME_S_1KI': 0.036524549299564224, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 872.9266104698181, 'W': 66.0}
+[40.67, 38.88, 39.03, 38.97, 39.55, 38.85, 39.41, 38.82, 39.41, 38.86, 39.72, 38.82, 38.97, 39.27, 39.27, 39.76, 38.89, 38.89, 45.01, 39.44]
+711.145
+35.557249999999996
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 288650, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.542811155319214, 'TIME_S_1KI': 0.036524549299564224, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 872.9266104698181, 'W': 66.0, 'J_1KI': 3.0241697920312425, 'W_1KI': 0.22865061493157804, 'W_D': 30.442750000000004, 'J_D': 402.64070561939485, 'W_D_1KI': 0.10546596223800452, 'J_D_1KI': 0.00036537662303136846}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..c479701
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 203, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.8138906955719, "TIME_S_1KI": 53.27039751513251, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1609.7031775331498, "W": 81.98, "J_1KI": 7929.572303118964, "W_1KI": 403.8423645320197, "W_D": 30.52650000000002, "J_D": 599.3974633930925, "W_D_1KI": 150.37684729064048, "J_D_1KI": 740.7726467519235}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..e1438f6
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 5.424488306045532}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       44,       99,  ..., 24999917,
+                            24999946, 25000000]),
+       col_indices=tensor([  4827,  10869,  14232,  ..., 471243, 483745,
+                           496563]),
+       values=tensor([0.8207, 0.6147, 0.2995,  ..., 0.3197, 0.5880, 0.5650]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.7008, 0.9045, 0.7559,  ..., 0.2377, 0.3193, 0.3380])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 5.424488306045532 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '193', '-ss', '500000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.982287168502808}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       53,      103,  ..., 24999914,
+                            24999954, 25000000]),
+       col_indices=tensor([   956,  25275,  30712,  ..., 470941, 489379,
+                           489461]),
+       values=tensor([0.2897, 0.9352, 0.3996,  ..., 0.3187, 0.8556, 0.7054]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.6888, 0.9226, 0.2376,  ..., 0.2155, 0.1168, 0.1817])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 9.982287168502808 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '203', '-ss', '500000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.8138906955719}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       60,      106,  ..., 24999892,
+                            24999946, 25000000]),
+       col_indices=tensor([ 18694,  24514,  28811,  ..., 477104, 482132,
+                           483877]),
+       values=tensor([0.0999, 0.2209, 0.5662,  ..., 0.8643, 0.1918, 0.8434]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.7305, 0.0261, 0.0866,  ..., 0.4657, 0.9743, 0.1757])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.8138906955719 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       60,      106,  ..., 24999892,
+                            24999946, 25000000]),
+       col_indices=tensor([ 18694,  24514,  28811,  ..., 477104, 482132,
+                           483877]),
+       values=tensor([0.0999, 0.2209, 0.5662,  ..., 0.8643, 0.1918, 0.8434]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.7305, 0.0261, 0.0866,  ..., 0.4657, 0.9743, 0.1757])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.8138906955719 seconds
+
+[48.25, 66.08, 64.62, 65.48, 60.76, 53.27, 65.63, 72.69, 68.45, 64.01]
+[81.98]
+19.635315656661987
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 203, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.8138906955719, 'TIME_S_1KI': 53.27039751513251, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1609.7031775331498, 'W': 81.98}
+[48.25, 66.08, 64.62, 65.48, 60.76, 53.27, 65.63, 72.69, 68.45, 64.01, 67.95, 64.4, 68.38, 60.78, 45.98, 39.37, 40.58, 39.5, 41.17, 43.65]
+1029.0699999999997
+51.453499999999984
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 203, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.8138906955719, 'TIME_S_1KI': 53.27039751513251, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1609.7031775331498, 'W': 81.98, 'J_1KI': 7929.572303118964, 'W_1KI': 403.8423645320197, 'W_D': 30.52650000000002, 'J_D': 599.3974633930925, 'W_D_1KI': 150.37684729064048, 'J_D_1KI': 740.7726467519235}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..9445831
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1357, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.301345348358154, "TIME_S_1KI": 7.591264073955898, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 974.2936608052254, "W": 72.47, "J_1KI": 717.9761686110725, "W_1KI": 53.404568901989684, "W_D": 37.03175, "J_D": 497.85841415101294, "W_D_1KI": 27.28942520265291, "J_D_1KI": 20.11011437188866}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..d835f69
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.7734920978546143}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,      10,  ..., 2499990,
+                            2499995, 2500000]),
+       col_indices=tensor([ 99860, 161360, 168008,  ..., 375780, 443860,
+                           468048]),
+       values=tensor([0.7731, 0.7975, 0.7314,  ..., 0.7653, 0.4860, 0.2739]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9501, 0.6169, 0.8449,  ..., 0.9228, 0.9726, 0.5004])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 0.7734920978546143 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1357', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.301345348358154}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       2,      10,  ..., 2499991,
+                            2499994, 2500000]),
+       col_indices=tensor([238281, 305722, 262347,  ..., 326599, 364388,
+                           410788]),
+       values=tensor([0.2261, 0.8621, 0.1222,  ..., 0.7643, 0.7262, 0.6796]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7756, 0.5142, 0.7476,  ..., 0.1970, 0.9731, 0.3396])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.301345348358154 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       2,      10,  ..., 2499991,
+                            2499994, 2500000]),
+       col_indices=tensor([238281, 305722, 262347,  ..., 326599, 364388,
+                           410788]),
+       values=tensor([0.2261, 0.8621, 0.1222,  ..., 0.7643, 0.7262, 0.6796]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7756, 0.5142, 0.7476,  ..., 0.1970, 0.9731, 0.3396])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.301345348358154 seconds
+
+[40.45, 39.15, 39.07, 39.07, 39.08, 39.31, 39.47, 39.12, 39.58, 39.08]
+[72.47]
+13.444096326828003
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.301345348358154, 'TIME_S_1KI': 7.591264073955898, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 974.2936608052254, 'W': 72.47}
+[40.45, 39.15, 39.07, 39.07, 39.08, 39.31, 39.47, 39.12, 39.58, 39.08, 39.87, 38.95, 39.89, 38.93, 39.13, 39.06, 39.33, 39.5, 40.16, 40.53]
+708.7649999999999
+35.43825
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1357, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.301345348358154, 'TIME_S_1KI': 7.591264073955898, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 974.2936608052254, 'W': 72.47, 'J_1KI': 717.9761686110725, 'W_1KI': 53.404568901989684, 'W_D': 37.03175, 'J_D': 497.85841415101294, 'W_D_1KI': 27.28942520265291, 'J_D_1KI': 20.11011437188866}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..016557b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 374, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.481700897216797, "TIME_S_1KI": 28.025938227852397, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1245.1822829723358, "W": 76.84, "J_1KI": 3329.3643929741597, "W_1KI": 205.45454545454547, "W_D": 40.990750000000006, "J_D": 664.2498134532572, "W_D_1KI": 109.60093582887701, "J_D_1KI": 293.0506305584947}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..9fc6dc1
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.8034374713897705}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       31,       56,  ..., 12499945,
+                            12499972, 12500000]),
+       col_indices=tensor([ 16534,  21956,  27589,  ..., 400032, 455487,
+                           480702]),
+       values=tensor([0.5221, 0.3710, 0.3411,  ..., 0.2701, 0.3669, 0.2928]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.5983, 0.4656, 0.7235,  ..., 0.5590, 0.7340, 0.5167])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 2.8034374713897705 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '374', '-ss', '500000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.481700897216797}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       20,       47,  ..., 12499941,
+                            12499971, 12500000]),
+       col_indices=tensor([ 37298,  48174,  79945,  ..., 425979, 429124,
+                           477898]),
+       values=tensor([0.8892, 0.8073, 0.3867,  ..., 0.6750, 0.3130, 0.8587]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.8772, 0.7830, 0.9014,  ..., 0.3941, 0.0151, 0.6871])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.481700897216797 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       20,       47,  ..., 12499941,
+                            12499971, 12500000]),
+       col_indices=tensor([ 37298,  48174,  79945,  ..., 425979, 429124,
+                           477898]),
+       values=tensor([0.8892, 0.8073, 0.3867,  ..., 0.6750, 0.3130, 0.8587]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.8772, 0.7830, 0.9014,  ..., 0.3941, 0.0151, 0.6871])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.481700897216797 seconds
+
+[39.77, 39.61, 39.09, 39.46, 39.43, 39.2, 39.07, 38.94, 44.28, 39.72]
+[76.84]
+16.20487093925476
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 374, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.481700897216797, 'TIME_S_1KI': 28.025938227852397, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1245.1822829723358, 'W': 76.84}
+[39.77, 39.61, 39.09, 39.46, 39.43, 39.2, 39.07, 38.94, 44.28, 39.72, 40.11, 44.49, 39.49, 38.95, 39.21, 39.06, 39.21, 38.96, 39.02, 39.43]
+716.9849999999999
+35.84925
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 374, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.481700897216797, 'TIME_S_1KI': 28.025938227852397, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1245.1822829723358, 'W': 76.84, 'J_1KI': 3329.3643929741597, 'W_1KI': 205.45454545454547, 'W_D': 40.990750000000006, 'J_D': 664.2498134532572, 'W_D_1KI': 109.60093582887701, 'J_D_1KI': 293.0506305584947}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..e80dbeb
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 15655, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.832781791687012, "TIME_S_1KI": 0.691969453317599, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 866.9169475746155, "W": 65.84, "J_1KI": 55.37636202967841, "W_1KI": 4.205685084637497, "W_D": 30.269999999999996, "J_D": 398.5658566689491, "W_D_1KI": 1.933567550303417, "J_D_1KI": 0.12351118175045782}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..da8990f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.08056378364562988}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      7,  ..., 249990, 249996,
+                            250000]),
+       col_indices=tensor([28795, 30379, 41102,  ...,  5633,  6424, 22447]),
+       values=tensor([0.9841, 0.4564, 0.4138,  ..., 0.4352, 0.7831, 0.6427]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6096, 0.2856, 0.5951,  ..., 0.5564, 0.0665, 0.9869])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.08056378364562988 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '13033', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.741129398345947}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     13,  ..., 249987, 249991,
+                            250000]),
+       col_indices=tensor([  370,  1086,  2786,  ..., 43615, 44396, 45243]),
+       values=tensor([0.9664, 0.8693, 0.7422,  ..., 0.8293, 0.8225, 0.1476]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.9116, 0.5607, 0.8635,  ..., 0.8139, 0.6651, 0.4589])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 8.741129398345947 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15655', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.832781791687012}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,     10,  ..., 249991, 249998,
+                            250000]),
+       col_indices=tensor([12466, 31687, 41380,  ..., 43099, 30794, 44210]),
+       values=tensor([0.5535, 0.2801, 0.3869,  ..., 0.8607, 0.0342, 0.7001]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.1141, 0.0022, 0.7559,  ..., 0.9683, 0.9705, 0.8203])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.832781791687012 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,     10,  ..., 249991, 249998,
+                            250000]),
+       col_indices=tensor([12466, 31687, 41380,  ..., 43099, 30794, 44210]),
+       values=tensor([0.5535, 0.2801, 0.3869,  ..., 0.8607, 0.0342, 0.7001]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.1141, 0.0022, 0.7559,  ..., 0.9683, 0.9705, 0.8203])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.832781791687012 seconds
+
+[39.71, 38.91, 39.08, 39.03, 39.04, 38.89, 39.49, 39.37, 39.35, 39.31]
+[65.84]
+13.167025327682495
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15655, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.832781791687012, 'TIME_S_1KI': 0.691969453317599, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 866.9169475746155, 'W': 65.84}
+[39.71, 38.91, 39.08, 39.03, 39.04, 38.89, 39.49, 39.37, 39.35, 39.31, 40.75, 39.44, 39.59, 39.09, 39.39, 39.11, 38.98, 38.78, 44.38, 39.19]
+711.4000000000001
+35.57000000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 15655, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.832781791687012, 'TIME_S_1KI': 0.691969453317599, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 866.9169475746155, 'W': 65.84, 'J_1KI': 55.37636202967841, 'W_1KI': 4.205685084637497, 'W_D': 30.269999999999996, 'J_D': 398.5658566689491, 'W_D_1KI': 1.933567550303417, 'J_D_1KI': 0.12351118175045782}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..4e67596
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3401, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.135668277740479, "TIME_S_1KI": 2.9802023751074618, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 929.8197621202468, "W": 70.57, "J_1KI": 273.39599003829665, "W_1KI": 20.74977947662452, "W_D": 35.09774999999999, "J_D": 462.44270307433595, "W_D_1KI": 10.319832402234633, "J_D_1KI": 3.0343523676079487}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..73dd96f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.3087193965911865}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      47,     100,  ..., 2499908,
+                            2499955, 2500000]),
+       col_indices=tensor([ 1811,  3820,  5210,  ..., 47398, 47518, 48036]),
+       values=tensor([0.8154, 0.8090, 0.3024,  ..., 0.4722, 0.9116, 0.7561]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4260, 0.8795, 0.3202,  ..., 0.3159, 0.0406, 0.9752])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.3087193965911865 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3401', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.135668277740479}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      41,      88,  ..., 2499883,
+                            2499951, 2500000]),
+       col_indices=tensor([  638,  2365,  2400,  ..., 44467, 46636, 49496]),
+       values=tensor([0.8518, 0.8769, 0.3572,  ..., 0.1360, 0.1673, 0.1097]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0408, 0.6133, 0.1624,  ..., 0.7272, 0.2583, 0.9038])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.135668277740479 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      41,      88,  ..., 2499883,
+                            2499951, 2500000]),
+       col_indices=tensor([  638,  2365,  2400,  ..., 44467, 46636, 49496]),
+       values=tensor([0.8518, 0.8769, 0.3572,  ..., 0.1360, 0.1673, 0.1097]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0408, 0.6133, 0.1624,  ..., 0.7272, 0.2583, 0.9038])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.135668277740479 seconds
+
+[40.86, 44.19, 38.96, 38.91, 39.18, 38.84, 39.26, 38.86, 39.41, 39.32]
+[70.57]
+13.17585039138794
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.135668277740479, 'TIME_S_1KI': 2.9802023751074618, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 929.8197621202468, 'W': 70.57}
+[40.86, 44.19, 38.96, 38.91, 39.18, 38.84, 39.26, 38.86, 39.41, 39.32, 40.03, 39.02, 38.84, 38.98, 38.82, 40.3, 38.82, 38.8, 38.79, 38.72]
+709.445
+35.47225
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3401, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.135668277740479, 'TIME_S_1KI': 2.9802023751074618, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 929.8197621202468, 'W': 70.57, 'J_1KI': 273.39599003829665, 'W_1KI': 20.74977947662452, 'W_D': 35.09774999999999, 'J_D': 462.44270307433595, 'W_D_1KI': 10.319832402234633, 'J_D_1KI': 3.0343523676079487}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..05ef34d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 277, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.601917266845703, "TIME_S_1KI": 38.27406955539965, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1502.4378191399574, "W": 76.47, "J_1KI": 5423.963245992626, "W_1KI": 276.0649819494585, "W_D": 40.946749999999994, "J_D": 804.497786986649, "W_D_1KI": 147.82220216606495, "J_D_1KI": 533.6541594442779}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..e414a83
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.789712905883789}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      551,     1060,  ..., 24998953,
+                            24999494, 25000000]),
+       col_indices=tensor([   18,    53,    90,  ..., 49926, 49944, 49970]),
+       values=tensor([0.6546, 0.1735, 0.7966,  ..., 0.3203, 0.8871, 0.0598]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.4855, 0.9619, 0.0930,  ..., 0.6959, 0.6112, 0.3764])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 3.789712905883789 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '277', '-ss', '50000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.601917266845703}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      523,     1012,  ..., 24998990,
+                            24999509, 25000000]),
+       col_indices=tensor([  171,   246,   332,  ..., 49640, 49825, 49863]),
+       values=tensor([0.1620, 0.2511, 0.7784,  ..., 0.0916, 0.2856, 0.2435]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.9840, 0.8151, 0.6106,  ..., 0.4542, 0.6992, 0.9833])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.601917266845703 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      523,     1012,  ..., 24998990,
+                            24999509, 25000000]),
+       col_indices=tensor([  171,   246,   332,  ..., 49640, 49825, 49863]),
+       values=tensor([0.1620, 0.2511, 0.7784,  ..., 0.0916, 0.2856, 0.2435]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.9840, 0.8151, 0.6106,  ..., 0.4542, 0.6992, 0.9833])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.601917266845703 seconds
+
+[39.75, 38.88, 39.04, 38.94, 38.9, 44.6, 39.87, 39.01, 39.47, 38.87]
+[76.47]
+19.647414922714233
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.601917266845703, 'TIME_S_1KI': 38.27406955539965, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.4378191399574, 'W': 76.47}
+[39.75, 38.88, 39.04, 38.94, 38.9, 44.6, 39.87, 39.01, 39.47, 38.87, 39.65, 39.46, 38.97, 39.37, 39.36, 38.88, 39.18, 38.94, 39.03, 38.86]
+710.465
+35.523250000000004
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.601917266845703, 'TIME_S_1KI': 38.27406955539965, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.4378191399574, 'W': 76.47, 'J_1KI': 5423.963245992626, 'W_1KI': 276.0649819494585, 'W_D': 40.946749999999994, 'J_D': 804.497786986649, 'W_D_1KI': 147.82220216606495, 'J_D_1KI': 533.6541594442779}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..41355a8
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 35925, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.468754291534424, "TIME_S_1KI": 0.29140582579079816, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 855.3263425445557, "W": 65.12, "J_1KI": 23.808666459138642, "W_1KI": 1.8126652748782186, "W_D": 29.625000000000007, "J_D": 389.1130666136743, "W_D_1KI": 0.8246346555323593, "J_D_1KI": 0.022954339750378826}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..77e4f27
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.04216480255126953}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 24999, 25000]),
+       col_indices=tensor([ 8605,  8537, 29290,  ...,  9179, 13978,  1469]),
+       values=tensor([0.2780, 0.8342, 0.8502,  ..., 0.9082, 0.5496, 0.9536]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7685, 0.9033, 0.7153,  ..., 0.8654, 0.8274, 0.9503])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.04216480255126953 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24902', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.278246164321899}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([37031, 32096, 18727,  ..., 44552, 41451,  6296]),
+       values=tensor([0.0751, 0.3287, 0.1662,  ..., 0.5788, 0.0483, 0.4147]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5341, 0.4675, 0.3327,  ..., 0.1193, 0.3106, 0.6128])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 7.278246164321899 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35925', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.468754291534424}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([44391, 10770, 45928,  ...,  5594,  4079, 17032]),
+       values=tensor([0.6882, 0.4791, 0.4331,  ..., 0.9470, 0.6037, 0.4941]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1889, 0.2559, 0.4696,  ..., 0.5278, 0.4768, 0.4458])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.468754291534424 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24998, 24998, 25000]),
+       col_indices=tensor([44391, 10770, 45928,  ...,  5594,  4079, 17032]),
+       values=tensor([0.6882, 0.4791, 0.4331,  ..., 0.9470, 0.6037, 0.4941]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1889, 0.2559, 0.4696,  ..., 0.5278, 0.4768, 0.4458])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.468754291534424 seconds
+
+[39.59, 39.87, 39.3, 39.3, 39.78, 38.89, 39.35, 40.52, 39.13, 38.83]
+[65.12]
+13.134618282318115
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35925, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.468754291534424, 'TIME_S_1KI': 0.29140582579079816, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3263425445557, 'W': 65.12}
+[39.59, 39.87, 39.3, 39.3, 39.78, 38.89, 39.35, 40.52, 39.13, 38.83, 39.59, 39.23, 39.11, 38.91, 39.77, 40.62, 39.49, 39.29, 38.91, 38.85]
+709.9
+35.495
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 35925, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.468754291534424, 'TIME_S_1KI': 0.29140582579079816, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.3263425445557, 'W': 65.12, 'J_1KI': 23.808666459138642, 'W_1KI': 1.8126652748782186, 'W_D': 29.625000000000007, 'J_D': 389.1130666136743, 'W_D_1KI': 0.8246346555323593, 'J_D_1KI': 0.022954339750378826}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..54cfc66
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 18331, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.45155644416809, "TIME_S_1KI": 0.570157462449844, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 852.8894222688675, "W": 65.62, "J_1KI": 46.52716285357414, "W_1KI": 3.5797283290600626, "W_D": 29.885000000000005, "J_D": 388.42731460690504, "W_D_1KI": 1.6302984016147513, "J_D_1KI": 0.08893668657545967}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..bef46d2
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.0693967342376709}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      5,  ..., 124992, 124996,
+                            125000]),
+       col_indices=tensor([17438, 29688, 13553,  ..., 36532, 44163, 44855]),
+       values=tensor([0.2242, 0.0224, 0.1461,  ..., 0.6740, 0.5355, 0.8238]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.2153, 0.3458, 0.3359,  ..., 0.7412, 0.9011, 0.6249])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.0693967342376709 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15130', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.666174411773682}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      4,  ..., 124994, 124997,
+                            125000]),
+       col_indices=tensor([17329, 36001, 38373,  ...,   475, 21379, 35295]),
+       values=tensor([0.0803, 0.2135, 0.1853,  ..., 0.6523, 0.5299, 0.1396]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.4435, 0.4362, 0.5554,  ..., 0.5263, 0.8506, 0.5178])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 8.666174411773682 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18331', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.45155644416809}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      2,  ..., 124997, 124997,
+                            125000]),
+       col_indices=tensor([14185, 16264,  7088,  ..., 33383, 46641, 46645]),
+       values=tensor([0.3059, 0.0880, 0.9320,  ..., 0.7602, 0.8512, 0.4645]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.5025, 0.7968, 0.2806,  ..., 0.1024, 0.8091, 0.6972])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.45155644416809 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      2,  ..., 124997, 124997,
+                            125000]),
+       col_indices=tensor([14185, 16264,  7088,  ..., 33383, 46641, 46645]),
+       values=tensor([0.3059, 0.0880, 0.9320,  ..., 0.7602, 0.8512, 0.4645]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.5025, 0.7968, 0.2806,  ..., 0.1024, 0.8091, 0.6972])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.45155644416809 seconds
+
+[39.86, 38.92, 44.85, 39.01, 39.6, 39.39, 38.97, 39.8, 41.08, 39.48]
+[65.62]
+12.997400522232056
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 18331, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.45155644416809, 'TIME_S_1KI': 0.570157462449844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 852.8894222688675, 'W': 65.62}
+[39.86, 38.92, 44.85, 39.01, 39.6, 39.39, 38.97, 39.8, 41.08, 39.48, 40.67, 39.02, 39.09, 39.05, 39.17, 38.87, 39.48, 39.3, 39.44, 39.31]
+714.7
+35.735
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 18331, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.45155644416809, 'TIME_S_1KI': 0.570157462449844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 852.8894222688675, 'W': 65.62, 'J_1KI': 46.52716285357414, 'W_1KI': 3.5797283290600626, 'W_D': 29.885000000000005, 'J_D': 388.42731460690504, 'W_D_1KI': 1.6302984016147513, 'J_D_1KI': 0.08893668657545967}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..2d402b4
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 461205, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.546576023101807, "TIME_S_1KI": 0.022867436439548153, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 854.1005449390411, "W": 65.22, "J_1KI": 1.8518891706270337, "W_1KI": 0.1414121702930367, "W_D": 29.99125, "J_D": 392.75594861090184, "W_D_1KI": 0.065028024414306, "J_D_1KI": 0.0001409959224516343}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..a788520
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.0131683349609375}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([1743,  190, 2771,  ..., 2075, 3388, 3957]),
+       values=tensor([0.1814, 0.3494, 0.7591,  ..., 0.1503, 0.3935, 0.5274]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3288, 0.8511, 0.6239,  ..., 0.9211, 0.6649, 0.5940])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.0131683349609375 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '79736', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.8153021335601807}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([3060, 1065, 3686,  ...,  959,  268, 4999]),
+       values=tensor([0.4441, 0.5663, 0.4237,  ..., 0.7927, 0.1815, 0.5098]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.5827, 0.3654, 0.8856,  ..., 0.0793, 0.3634, 0.4632])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 1.8153021335601807 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '461205', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.546576023101807}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([ 400,  983, 3289,  ..., 2520,  735,  710]),
+       values=tensor([0.5782, 0.5847, 0.2189,  ..., 0.6822, 0.4901, 0.5172]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.8041, 0.1477, 0.3593,  ..., 0.2372, 0.8803, 0.6540])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.546576023101807 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([ 400,  983, 3289,  ..., 2520,  735,  710]),
+       values=tensor([0.5782, 0.5847, 0.2189,  ..., 0.6822, 0.4901, 0.5172]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.8041, 0.1477, 0.3593,  ..., 0.2372, 0.8803, 0.6540])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.546576023101807 seconds
+
+[42.91, 38.91, 39.2, 38.78, 38.91, 38.88, 39.21, 39.32, 39.19, 38.86]
+[65.22]
+13.09568452835083
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 461205, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.546576023101807, 'TIME_S_1KI': 0.022867436439548153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 854.1005449390411, 'W': 65.22}
+[42.91, 38.91, 39.2, 38.78, 38.91, 38.88, 39.21, 39.32, 39.19, 38.86, 39.94, 38.72, 39.13, 38.84, 39.27, 38.74, 39.16, 38.84, 39.21, 38.82]
+704.5749999999999
+35.22875
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 461205, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.546576023101807, 'TIME_S_1KI': 0.022867436439548153, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 854.1005449390411, 'W': 65.22, 'J_1KI': 1.8518891706270337, 'W_1KI': 0.1414121702930367, 'W_D': 29.99125, 'J_D': 392.75594861090184, 'W_D_1KI': 0.065028024414306, 'J_D_1KI': 0.0001409959224516343}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..89f214d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 244335, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.417778253555298, "TIME_S_1KI": 0.04263727363478543, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 855.0210603523253, "W": 66.32, "J_1KI": 3.4993801966657467, "W_1KI": 0.27143061779933286, "W_D": 30.753, "J_D": 396.4786289055347, "W_D_1KI": 0.1258640800540242, "J_D_1KI": 0.0005151291466798624}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..b5c1026
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.01594710350036621}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     8,    12,  ..., 24992, 24999, 25000]),
+       col_indices=tensor([ 675, 1985, 2689,  ..., 3047, 3313, 3022]),
+       values=tensor([0.4390, 0.7079, 0.1777,  ..., 0.9145, 0.2520, 0.4929]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8163, 0.8334, 0.7201,  ..., 0.8897, 0.9022, 0.1765])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.01594710350036621 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '65842', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.8294758796691895}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     9,  ..., 24993, 24997, 25000]),
+       col_indices=tensor([  33, 1908, 3594,  ...,  299,  386, 4209]),
+       values=tensor([0.2193, 0.5619, 0.9883,  ..., 0.1847, 0.2793, 0.9697]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6444, 0.1854, 0.4415,  ..., 0.6088, 0.2901, 0.0299])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 2.8294758796691895 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '244335', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.417778253555298}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     8,  ..., 24990, 24997, 25000]),
+       col_indices=tensor([2557, 3235, 4228,  ...,  486, 3364, 4712]),
+       values=tensor([0.2079, 0.9240, 0.7430,  ..., 0.9071, 0.8940, 0.1396]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3103, 0.6909, 0.1567,  ..., 0.3064, 0.4398, 0.1480])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.417778253555298 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     8,  ..., 24990, 24997, 25000]),
+       col_indices=tensor([2557, 3235, 4228,  ...,  486, 3364, 4712]),
+       values=tensor([0.2079, 0.9240, 0.7430,  ..., 0.9071, 0.8940, 0.1396]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3103, 0.6909, 0.1567,  ..., 0.3064, 0.4398, 0.1480])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.417778253555298 seconds
+
+[45.51, 38.78, 39.51, 38.99, 39.48, 39.2, 39.32, 39.33, 39.41, 38.75]
+[66.32]
+12.892356157302856
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 244335, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.417778253555298, 'TIME_S_1KI': 0.04263727363478543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.0210603523253, 'W': 66.32}
+[45.51, 38.78, 39.51, 38.99, 39.48, 39.2, 39.32, 39.33, 39.41, 38.75, 39.41, 38.8, 39.14, 39.28, 40.7, 39.8, 39.35, 39.07, 39.32, 40.05]
+711.3399999999999
+35.56699999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 244335, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.417778253555298, 'TIME_S_1KI': 0.04263727363478543, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 855.0210603523253, 'W': 66.32, 'J_1KI': 3.4993801966657467, 'W_1KI': 0.27143061779933286, 'W_D': 30.753, 'J_D': 396.4786289055347, 'W_D_1KI': 0.1258640800540242, 'J_D_1KI': 0.0005151291466798624}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..d1e9eee
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 41575, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.472304821014404, "TIME_S_1KI": 0.2518894725439424, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 875.4065453624726, "W": 66.47, "J_1KI": 21.056080465723934, "W_1KI": 1.5987973541791942, "W_D": 30.839750000000002, "J_D": 406.1579510657788, "W_D_1KI": 0.7417859290438966, "J_D_1KI": 0.017842114949943394}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..9d6f728
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.03741025924682617}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     50,     95,  ..., 249917, 249960,
+                            250000]),
+       col_indices=tensor([  56,  131,  133,  ..., 4645, 4665, 4841]),
+       values=tensor([0.2594, 0.3669, 0.3309,  ..., 0.9204, 0.7750, 0.3008]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7286, 0.8651, 0.2961,  ..., 0.7120, 0.4132, 0.7079])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.03741025924682617 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28067', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.088342666625977}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     51,     92,  ..., 249888, 249951,
+                            250000]),
+       col_indices=tensor([ 195,  232,  275,  ..., 4637, 4801, 4910]),
+       values=tensor([0.9933, 0.3255, 0.9817,  ..., 0.2679, 0.2640, 0.0554]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.2297, 0.2173, 0.6945,  ..., 0.5761, 0.5521, 0.3650])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 7.088342666625977 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '41575', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.472304821014404}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     48,     88,  ..., 249902, 249950,
+                            250000]),
+       col_indices=tensor([  75,   83,  302,  ..., 4746, 4941, 4952]),
+       values=tensor([0.9930, 0.3893, 0.6584,  ..., 0.0382, 0.8338, 0.2904]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5807, 0.7893, 0.2250,  ..., 0.2178, 0.8594, 0.2155])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.472304821014404 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     48,     88,  ..., 249902, 249950,
+                            250000]),
+       col_indices=tensor([  75,   83,  302,  ..., 4746, 4941, 4952]),
+       values=tensor([0.9930, 0.3893, 0.6584,  ..., 0.0382, 0.8338, 0.2904]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5807, 0.7893, 0.2250,  ..., 0.2178, 0.8594, 0.2155])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.472304821014404 seconds
+
+[39.83, 38.85, 39.09, 38.79, 39.53, 38.8, 39.37, 39.26, 39.29, 38.81]
+[66.47]
+13.169949531555176
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 41575, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.472304821014404, 'TIME_S_1KI': 0.2518894725439424, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 875.4065453624726, 'W': 66.47}
+[39.83, 38.85, 39.09, 38.79, 39.53, 38.8, 39.37, 39.26, 39.29, 38.81, 39.98, 38.99, 38.83, 39.07, 38.9, 38.8, 38.86, 39.29, 47.91, 39.33]
+712.6049999999999
+35.63025
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 41575, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.472304821014404, 'TIME_S_1KI': 0.2518894725439424, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 875.4065453624726, 'W': 66.47, 'J_1KI': 21.056080465723934, 'W_1KI': 1.5987973541791942, 'W_D': 30.839750000000002, 'J_D': 406.1579510657788, 'W_D_1KI': 0.7417859290438966, 'J_D_1KI': 0.017842114949943394}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..e407283
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 8178, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.511467218399048, "TIME_S_1KI": 1.2853347051111579, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 880.7755374526977, "W": 66.16, "J_1KI": 107.70060374818999, "W_1KI": 8.089997554414282, "W_D": 30.69249999999999, "J_D": 408.60343384623513, "W_D_1KI": 3.7530569821472226, "J_D_1KI": 0.4589211276775767}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..b9e58d8
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.14169001579284668}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     264,     521,  ..., 1249500,
+                            1249758, 1250000]),
+       col_indices=tensor([  22,   35,   54,  ..., 4954, 4963, 4982]),
+       values=tensor([0.3715, 0.6699, 0.1465,  ..., 0.9132, 0.2376, 0.1878]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.3853, 0.7833, 0.5244,  ..., 0.5756, 0.3818, 0.8103])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 0.14169001579284668 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '7410', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.513462543487549}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     245,     477,  ..., 1249530,
+                            1249759, 1250000]),
+       col_indices=tensor([  15,   78,  164,  ..., 4968, 4978, 4992]),
+       values=tensor([0.5947, 0.7215, 0.2123,  ..., 0.7493, 0.6227, 0.0355]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.4245, 0.9857, 0.9704,  ..., 0.1643, 0.0459, 0.0545])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 9.513462543487549 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '8178', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.511467218399048}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     243,     531,  ..., 1249480,
+                            1249728, 1250000]),
+       col_indices=tensor([  10,   29,   31,  ..., 4961, 4980, 4981]),
+       values=tensor([0.2878, 0.6436, 0.4714,  ..., 0.0074, 0.1096, 0.3758]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.4226, 0.4894, 0.7354,  ..., 0.5546, 0.7888, 0.4627])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.511467218399048 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     243,     531,  ..., 1249480,
+                            1249728, 1250000]),
+       col_indices=tensor([  10,   29,   31,  ..., 4961, 4980, 4981]),
+       values=tensor([0.2878, 0.6436, 0.4714,  ..., 0.0074, 0.1096, 0.3758]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.4226, 0.4894, 0.7354,  ..., 0.5546, 0.7888, 0.4627])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.511467218399048 seconds
+
+[40.21, 39.14, 39.56, 39.02, 39.43, 41.29, 39.49, 39.07, 39.13, 39.19]
+[66.16]
+13.31281042098999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 8178, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.511467218399048, 'TIME_S_1KI': 1.2853347051111579, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 880.7755374526977, 'W': 66.16}
+[40.21, 39.14, 39.56, 39.02, 39.43, 41.29, 39.49, 39.07, 39.13, 39.19, 40.14, 39.04, 40.64, 38.87, 39.1, 38.93, 39.37, 39.03, 38.8, 39.34]
+709.3500000000001
+35.46750000000001
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 8178, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.511467218399048, 'TIME_S_1KI': 1.2853347051111579, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 880.7755374526977, 'W': 66.16, 'J_1KI': 107.70060374818999, 'W_1KI': 8.089997554414282, 'W_D': 30.69249999999999, 'J_D': 408.60343384623513, 'W_D_1KI': 3.7530569821472226, 'J_D_1KI': 0.4589211276775767}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..cc784c7
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 3463, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.092161417007446, "TIME_S_1KI": 2.9142828232767677, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 888.6800115585327, "W": 67.72, "J_1KI": 256.6214298465298, "W_1KI": 19.555298873808837, "W_D": 32.353, "J_D": 424.56385726451873, "W_D_1KI": 9.34247762056021, "J_D_1KI": 2.697798908622642}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..8f92a77
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.30318737030029297}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     515,    1018,  ..., 2498999,
+                            2499493, 2500000]),
+       col_indices=tensor([   4,   21,   24,  ..., 4955, 4957, 4967]),
+       values=tensor([0.5325, 0.9134, 0.0336,  ..., 0.9679, 0.3618, 0.7033]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8211, 0.4248, 0.8998,  ..., 0.9656, 0.3613, 0.2992])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 0.30318737030029297 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3463', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.092161417007446}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     498,    1000,  ..., 2499016,
+                            2499523, 2500000]),
+       col_indices=tensor([   2,   43,   50,  ..., 4978, 4986, 4997]),
+       values=tensor([0.9352, 0.1649, 0.8628,  ..., 0.6965, 0.7003, 0.5455]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0499, 0.6757, 0.4614,  ..., 0.0542, 0.7848, 0.0169])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.092161417007446 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     498,    1000,  ..., 2499016,
+                            2499523, 2500000]),
+       col_indices=tensor([   2,   43,   50,  ..., 4978, 4986, 4997]),
+       values=tensor([0.9352, 0.1649, 0.8628,  ..., 0.6965, 0.7003, 0.5455]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0499, 0.6757, 0.4614,  ..., 0.0542, 0.7848, 0.0169])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.092161417007446 seconds
+
+[40.21, 38.8, 38.93, 38.71, 39.03, 38.9, 38.74, 39.19, 39.25, 38.74]
+[67.72]
+13.122859001159668
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3463, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.092161417007446, 'TIME_S_1KI': 2.9142828232767677, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.6800115585327, 'W': 67.72}
+[40.21, 38.8, 38.93, 38.71, 39.03, 38.9, 38.74, 39.19, 39.25, 38.74, 40.21, 38.79, 39.15, 38.78, 38.85, 38.73, 44.44, 39.11, 38.88, 38.96]
+707.3399999999999
+35.367
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 3463, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.092161417007446, 'TIME_S_1KI': 2.9142828232767677, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 888.6800115585327, 'W': 67.72, 'J_1KI': 256.6214298465298, 'W_1KI': 19.555298873808837, 'W_D': 32.353, 'J_D': 424.56385726451873, 'W_D_1KI': 9.34247762056021, 'J_D_1KI': 2.697798908622642}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..b65b863
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1741, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.298704862594604, "TIME_S_1KI": 5.915396245028492, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1027.8759766101837, "W": 74.46, "J_1KI": 590.3940129868946, "W_1KI": 42.76852383687535, "W_D": 39.113499999999995, "J_D": 539.9385846245289, "W_D_1KI": 22.466111430212518, "J_D_1KI": 12.904142119593635}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..987a685
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.6030943393707275}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1017,    2015,  ..., 4997969,
+                            4998981, 5000000]),
+       col_indices=tensor([   0,   31,   34,  ..., 4984, 4989, 4995]),
+       values=tensor([0.9331, 0.7841, 0.5601,  ..., 0.4749, 0.8668, 0.1575]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.1492, 0.3030, 0.4894,  ..., 0.2440, 0.4033, 0.0238])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 0.6030943393707275 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1741', '-ss', '5000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.298704862594604}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     961,    1935,  ..., 4998003,
+                            4999013, 5000000]),
+       col_indices=tensor([   0,    3,    5,  ..., 4968, 4970, 4989]),
+       values=tensor([0.3710, 0.5603, 0.2176,  ..., 0.5072, 0.2694, 0.8987]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6029, 0.2962, 0.8345,  ..., 0.0907, 0.1121, 0.2666])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.298704862594604 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     961,    1935,  ..., 4998003,
+                            4999013, 5000000]),
+       col_indices=tensor([   0,    3,    5,  ..., 4968, 4970, 4989]),
+       values=tensor([0.3710, 0.5603, 0.2176,  ..., 0.5072, 0.2694, 0.8987]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6029, 0.2962, 0.8345,  ..., 0.0907, 0.1121, 0.2666])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.298704862594604 seconds
+
+[39.56, 39.38, 38.97, 38.88, 38.82, 39.21, 39.23, 39.16, 39.31, 39.25]
+[74.46]
+13.804404735565186
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1741, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.298704862594604, 'TIME_S_1KI': 5.915396245028492, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1027.8759766101837, 'W': 74.46}
+[39.56, 39.38, 38.97, 38.88, 38.82, 39.21, 39.23, 39.16, 39.31, 39.25, 40.48, 40.05, 39.23, 38.85, 39.62, 39.32, 38.87, 39.04, 39.64, 39.41]
+706.93
+35.3465
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1741, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.298704862594604, 'TIME_S_1KI': 5.915396245028492, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1027.8759766101837, 'W': 74.46, 'J_1KI': 590.3940129868946, 'W_1KI': 42.76852383687535, 'W_D': 39.113499999999995, 'J_D': 539.9385846245289, 'W_D_1KI': 22.466111430212518, 'J_D_1KI': 12.904142119593635}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..20f95b6
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 1166, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.334495306015015, "TIME_S_1KI": 8.8632035214537, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1095.1619911956786, "W": 75.64, "J_1KI": 939.2469907338582, "W_1KI": 64.8713550600343, "W_D": 40.43425, "J_D": 585.4316993985176, "W_D_1KI": 34.67774442538593, "J_D_1KI": 29.74077566499651}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..be3ca1f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.9000704288482666}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1542,    2985,  ..., 7497037,
+                            7498525, 7500000]),
+       col_indices=tensor([   0,    1,    6,  ..., 4988, 4994, 4997]),
+       values=tensor([0.4057, 0.2930, 0.9549,  ..., 0.4574, 0.5414, 0.6416]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.4314, 0.0098, 0.8060,  ..., 0.6655, 0.0522, 0.0757])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 0.9000704288482666 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1166', '-ss', '5000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.334495306015015}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1525,    3027,  ..., 7497015,
+                            7498498, 7500000]),
+       col_indices=tensor([  12,   15,   18,  ..., 4991, 4994, 4995]),
+       values=tensor([0.8648, 0.2387, 0.0206,  ..., 0.7504, 0.0755, 0.9898]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.0126, 0.2581, 0.5840,  ..., 0.5862, 0.5778, 0.3525])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.334495306015015 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1525,    3027,  ..., 7497015,
+                            7498498, 7500000]),
+       col_indices=tensor([  12,   15,   18,  ..., 4991, 4994, 4995]),
+       values=tensor([0.8648, 0.2387, 0.0206,  ..., 0.7504, 0.0755, 0.9898]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.0126, 0.2581, 0.5840,  ..., 0.5862, 0.5778, 0.3525])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.334495306015015 seconds
+
+[41.12, 38.83, 38.85, 38.96, 39.38, 38.75, 39.14, 38.88, 38.85, 38.77]
+[75.64]
+14.478609085083008
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1166, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.334495306015015, 'TIME_S_1KI': 8.8632035214537, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1095.1619911956786, 'W': 75.64}
+[41.12, 38.83, 38.85, 38.96, 39.38, 38.75, 39.14, 38.88, 38.85, 38.77, 40.18, 38.79, 39.32, 38.91, 39.22, 38.92, 39.3, 39.28, 39.3, 38.8]
+704.115
+35.20575
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 1166, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.334495306015015, 'TIME_S_1KI': 8.8632035214537, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1095.1619911956786, 'W': 75.64, 'J_1KI': 939.2469907338582, 'W_1KI': 64.8713550600343, 'W_D': 40.43425, 'J_D': 585.4316993985176, 'W_D_1KI': 34.67774442538593, 'J_D_1KI': 29.74077566499651}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..973144c
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 704, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.429930925369263, "TIME_S_1KI": 14.815242791717703, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1157.2951932907106, "W": 76.7, "J_1KI": 1643.8852177424865, "W_1KI": 108.94886363636364, "W_D": 41.2785, "J_D": 622.8345454530717, "W_D_1KI": 58.63423295454546, "J_D_1KI": 83.2872627195248}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..0f653eb
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 1.4912726879119873}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1951,     3944,  ...,  9995965,
+                             9997988, 10000000]),
+       col_indices=tensor([   6,   14,   15,  ..., 4996, 4997, 4998]),
+       values=tensor([0.7071, 0.0905, 0.6037,  ..., 0.4689, 0.3914, 0.3516]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7487, 0.9804, 0.4485,  ..., 0.3781, 0.3818, 0.0598])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 1.4912726879119873 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '704', '-ss', '5000', '-sd', '0.4', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.429930925369263}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1962,     3997,  ...,  9995983,
+                             9998031, 10000000]),
+       col_indices=tensor([   2,    3,    4,  ..., 4989, 4991, 4999]),
+       values=tensor([0.3156, 0.5704, 0.4238,  ..., 0.9413, 0.7746, 0.6098]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3215, 0.3275, 0.6323,  ..., 0.0024, 0.8893, 0.0654])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.429930925369263 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1962,     3997,  ...,  9995983,
+                             9998031, 10000000]),
+       col_indices=tensor([   2,    3,    4,  ..., 4989, 4991, 4999]),
+       values=tensor([0.3156, 0.5704, 0.4238,  ..., 0.9413, 0.7746, 0.6098]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3215, 0.3275, 0.6323,  ..., 0.0024, 0.8893, 0.0654])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.429930925369263 seconds
+
+[40.15, 39.37, 39.68, 38.87, 39.22, 39.13, 41.47, 38.91, 39.0, 39.01]
+[76.7]
+15.088594436645508
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 704, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.429930925369263, 'TIME_S_1KI': 14.815242791717703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1157.2951932907106, 'W': 76.7}
+[40.15, 39.37, 39.68, 38.87, 39.22, 39.13, 41.47, 38.91, 39.0, 39.01, 40.04, 39.56, 38.96, 39.33, 38.96, 38.95, 39.21, 38.91, 38.9, 40.8]
+708.4300000000001
+35.4215
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 704, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.429930925369263, 'TIME_S_1KI': 14.815242791717703, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1157.2951932907106, 'W': 76.7, 'J_1KI': 1643.8852177424865, 'W_1KI': 108.94886363636364, 'W_D': 41.2785, 'J_D': 622.8345454530717, 'W_D_1KI': 58.63423295454546, 'J_D_1KI': 83.2872627195248}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..70ebf3a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 569, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.483191013336182, "TIME_S_1KI": 18.42388578793705, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1197.8375442028046, "W": 76.6, "J_1KI": 2105.162643590166, "W_1KI": 134.62214411247803, "W_D": 41.0435, "J_D": 641.8204340142012, "W_D_1KI": 72.13268892794376, "J_D_1KI": 126.77098229867093}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..d4faa36
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 1.8439099788665771}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2531,     5020,  ..., 12494917,
+                            12497450, 12500000]),
+       col_indices=tensor([   0,    1,    9,  ..., 4992, 4994, 4999]),
+       values=tensor([0.6676, 0.2754, 0.2712,  ..., 0.4447, 0.2547, 0.8500]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9338, 0.0413, 0.5968,  ..., 0.6366, 0.2029, 0.7249])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 1.8439099788665771 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '569', '-ss', '5000', '-sd', '0.5', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.483191013336182}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2491,     4957,  ..., 12495037,
+                            12497545, 12500000]),
+       col_indices=tensor([   0,    2,    5,  ..., 4995, 4998, 4999]),
+       values=tensor([0.5758, 0.7291, 0.3910,  ..., 0.8483, 0.9816, 0.9388]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0835, 0.8623, 0.0534,  ..., 0.1116, 0.3605, 0.8512])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.483191013336182 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2491,     4957,  ..., 12495037,
+                            12497545, 12500000]),
+       col_indices=tensor([   0,    2,    5,  ..., 4995, 4998, 4999]),
+       values=tensor([0.5758, 0.7291, 0.3910,  ..., 0.8483, 0.9816, 0.9388]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0835, 0.8623, 0.0534,  ..., 0.1116, 0.3605, 0.8512])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.483191013336182 seconds
+
+[39.92, 38.82, 39.41, 39.19, 39.02, 39.24, 39.27, 38.82, 38.86, 38.89]
+[76.6]
+15.637565851211548
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 569, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.483191013336182, 'TIME_S_1KI': 18.42388578793705, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1197.8375442028046, 'W': 76.6}
+[39.92, 38.82, 39.41, 39.19, 39.02, 39.24, 39.27, 38.82, 38.86, 38.89, 40.74, 39.81, 39.32, 39.32, 39.28, 44.23, 39.36, 38.92, 39.11, 38.75]
+711.1299999999999
+35.55649999999999
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 569, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.483191013336182, 'TIME_S_1KI': 18.42388578793705, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1197.8375442028046, 'W': 76.6, 'J_1KI': 2105.162643590166, 'W_1KI': 134.62214411247803, 'W_D': 41.0435, 'J_D': 641.8204340142012, 'W_D_1KI': 72.13268892794376, 'J_D_1KI': 126.77098229867093}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..c3ed634
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 569391, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.146198511123657, "TIME_S_1KI": 0.017819386873209546, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 837.5037681818008, "W": 65.05, "J_1KI": 1.4708763717406856, "W_1KI": 0.11424486864035434, "W_D": 29.76475, "J_D": 383.2143010605574, "W_D_1KI": 0.05227471105092985, "J_D_1KI": 9.180810910416543e-05}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..6482c19
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,329 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.025912761688232422}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([2955, 4682, 3449, 1969, 3854,  251, 2319,  668,  498,
+                           3572, 3854, 2322, 4794,  541, 1204, 3498,  557, 2286,
+                           3599, 1633,  263,  572,  401, 2359, 4315,  911, 3580,
+                            647, 4452, 4876, 2180, 2875, 4244, 3344, 2209,  559,
+                           1303, 3655, 2944, 2633, 4973, 1628, 4101,  143, 4999,
+                           4517, 2519, 3173, 1189, 2853, 1263, 1640, 2218, 2187,
+                            500,  639, 1141, 4234,  755,  459,  531, 3206, 4041,
+                           4859, 2211, 4404, 1761, 2224, 3743, 1719, 4799, 2055,
+                           4619, 4316, 4729, 1778, 2613, 3964, 2169,  739, 4154,
+                           1973, 1793, 4308,  801, 3310,  342, 3095,  520, 3764,
+                           4097, 3032, 1897, 3411, 2128, 1565, 3684, 2810, 2598,
+                           1157, 2540, 3590, 4555,  145, 3631, 3914,  308,   84,
+                           3223, 2928, 1083, 1157, 2416, 2092, 1789, 4234, 2339,
+                            866, 1332,  582,  480,  407, 2460,  594, 1184, 1302,
+                           4554, 3210,  212, 2368, 3876, 3742, 2272, 1393,  406,
+                             80, 3371, 4169, 2118,  452, 2986,  932,   49, 3160,
+                           2327,  713, 2877, 3484, 2257,  182, 3066, 1789,  610,
+                           3257, 2119, 4757,  577,  310, 2782,  116, 2689, 2876,
+                           2666,  144, 4643, 3955,  517, 1439, 3012, 4289, 3484,
+                           1701, 3447, 1824, 1951, 3681, 4272, 2740, 4790, 1804,
+                           3833, 1146,  672, 4424, 3920, 2173, 2155, 2090, 4174,
+                           4084,  560, 3425, 3143, 3470, 1712, 3489, 1811, 1766,
+                           4872, 3288,  771, 4485, 3269, 2695, 4584,  535,  200,
+                           2606, 3547, 3737,  102,  696,  271, 2048, 2470, 1740,
+                           2219,  829, 4785, 1264, 2283, 4517, 1344, 1726,  844,
+                           2384, 4310,   42, 3459, 2017, 2199, 3093, 4339,  313,
+                           1332,  303, 3733, 2074, 4094, 4006,  429, 1830, 3447,
+                           3735, 3446, 1158, 2156, 2171, 2458,  989]),
+       values=tensor([0.1546, 0.0338, 0.3832, 0.6845, 0.5359, 0.2115, 0.5134,
+                      0.5236, 0.8368, 0.0286, 0.8691, 0.3848, 0.7412, 0.7376,
+                      0.0492, 0.2561, 0.4285, 0.6403, 0.4264, 0.9823, 0.2432,
+                      0.9496, 0.9648, 0.1994, 0.5806, 0.9987, 0.8946, 0.3796,
+                      0.9742, 0.5421, 0.7745, 0.0707, 0.4271, 0.7771, 0.6813,
+                      0.4916, 0.5119, 0.7062, 0.2752, 0.9391, 0.5947, 0.6220,
+                      0.1073, 0.5082, 0.8956, 0.6658, 0.1040, 0.0454, 0.1560,
+                      0.7150, 0.4739, 0.5494, 0.1706, 0.6723, 0.3127, 0.2460,
+                      0.1585, 0.6938, 0.6937, 0.2200, 0.6734, 0.5021, 0.7544,
+                      0.1720, 0.3861, 0.8179, 0.3810, 0.0801, 0.6567, 0.1205,
+                      0.2925, 0.1255, 0.7882, 0.3753, 0.2290, 0.4877, 0.9183,
+                      0.7718, 0.3047, 0.6194, 0.2851, 0.3419, 0.7256, 0.2356,
+                      0.5102, 0.4050, 0.2487, 0.5983, 0.2252, 0.4330, 0.1968,
+                      0.1306, 0.4006, 0.3092, 0.2645, 0.4635, 0.3587, 0.7536,
+                      0.7019, 0.0792, 0.1678, 0.7485, 0.9817, 0.9065, 0.9994,
+                      0.3911, 0.8833, 0.5411, 0.3679, 0.9711, 0.9853, 0.6437,
+                      0.7861, 0.5048, 0.2591, 0.7726, 0.6174, 0.1004, 0.1489,
+                      0.6017, 0.8836, 0.5571, 0.7423, 0.4796, 0.5887, 0.7010,
+                      0.6198, 0.7601, 0.9790, 0.0717, 0.8254, 0.1983, 0.6497,
+                      0.9092, 0.9977, 0.7279, 0.3767, 0.5564, 0.8901, 0.2896,
+                      0.4914, 0.7288, 0.7282, 0.1569, 0.7491, 0.2024, 0.5103,
+                      0.2006, 0.2616, 0.3680, 0.8784, 0.5095, 0.3100, 0.5468,
+                      0.7473, 0.8067, 0.1566, 0.7922, 0.7836, 0.0734, 0.2895,
+                      0.0658, 0.3948, 0.1442, 0.9166, 0.4276, 0.4709, 0.0305,
+                      0.5373, 0.3415, 0.0906, 0.7116, 0.3303, 0.4381, 0.5263,
+                      0.3654, 0.9325, 0.0370, 0.6385, 0.6709, 0.3766, 0.7549,
+                      0.4298, 0.9883, 0.6034, 0.5195, 0.8067, 0.8173, 0.5194,
+                      0.5329, 0.4999, 0.5357, 0.5843, 0.4652, 0.6589, 0.6127,
+                      0.9625, 0.8533, 0.0618, 0.2250, 0.1341, 0.3851, 0.9176,
+                      0.6106, 0.0281, 0.2538, 0.5580, 0.0137, 0.4927, 0.5743,
+                      0.6268, 0.5818, 0.7719, 0.1711, 0.1084, 0.6064, 0.1367,
+                      0.6312, 0.8778, 0.2960, 0.3372, 0.8224, 0.9699, 0.6070,
+                      0.2907, 0.4693, 0.5694, 0.7710, 0.6091, 0.5452, 0.3569,
+                      0.0226, 0.4986, 0.6727, 0.5738, 0.8629, 0.9155, 0.9081,
+                      0.9105, 0.9222, 0.7776, 0.3699, 0.9402, 0.5035, 0.4769,
+                      0.4797, 0.1466, 0.6411, 0.6861, 0.6601]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.1758, 0.5990, 0.4260,  ..., 0.6457, 0.1523, 0.4408])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.025912761688232422 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '40520', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.7472190856933594}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([4979, 4303, 4566, 4762, 4549,  190, 2004, 2509,  751,
+                            928, 1907, 2345, 3485,  628, 2908, 2256, 1744, 3960,
+                            753,  741, 1691, 3236,  822,  110, 1716, 2413, 2905,
+                           1494, 3365, 3162, 4499, 3337, 4292, 2749, 1497, 1083,
+                            571, 2576, 1389, 2374, 4678, 2621, 3191, 2850, 4442,
+                           3038, 2838, 3147, 1692, 3297, 1369,  316,   44, 3096,
+                           1795, 1502, 4078, 1530,  382, 1493, 2347,  369, 4086,
+                           3463, 1734,  324, 4159,  222, 2163, 3883, 2624, 3179,
+                           1955, 2358, 4353, 3311, 2924,  323,  448,   10, 1850,
+                           3616, 4600, 4760,  641, 4296, 2902, 1005, 4495, 3758,
+                           2177, 1780, 1919,  576, 3690, 2414, 4915,  960, 2888,
+                           4117, 1616, 3504, 3045, 3420, 3788, 3250, 4185,  565,
+                           1378, 1958, 1079, 3142,  700, 3775, 2755, 2658, 4044,
+                           3937, 2159, 2450,  691, 2810, 2854, 3907,   15, 3162,
+                           4347, 4859,  392, 4662, 1450, 2377, 2601, 2733, 2929,
+                           1056, 3918, 1597,  324, 1218, 4516, 2665, 4847, 4488,
+                           1656, 2761,  652, 3181,  916,   67, 4641, 3028,  215,
+                           2829,  990, 4480, 3427, 3715, 1658, 1471, 2613,  915,
+                           4016, 2089,    4, 4230, 2407, 4012,  246, 2239,  359,
+                           1734, 4596, 4776, 1554, 2314, 1733, 3775, 4475, 4112,
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+                            660, 3425, 4720, 1730, 3977,  302, 2922, 4097, 2384,
+                           2222, 3777, 3527,  253, 2444, 2627,  880]),
+       values=tensor([0.6428, 0.9934, 0.2143, 0.2775, 0.8801, 0.9209, 0.7937,
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+                      0.8062, 0.8168, 0.8982, 0.6078, 0.4816, 0.3037, 0.1198,
+                      0.4605, 0.7585, 0.3262, 0.3531, 0.6379]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.7653, 0.7067, 0.4116,  ..., 0.4300, 0.1070, 0.3611])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.7472190856933594 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '569391', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.146198511123657}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   1,   1,  ..., 250, 250, 250]),
+       col_indices=tensor([ 989, 4634,  172,  296,  437,  988, 4701,   40, 3459,
+                           2902,  284, 2223, 3489,  379, 2336, 3854, 3801, 4913,
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+                           3459, 2009, 1681,    9, 2459, 3710, 4173, 1493, 3773,
+                           2982, 4418, 4646, 1091,  541, 4902, 4735, 4604, 3735,
+                           3670,  955,  687, 2373, 4360, 1850, 1893]),
+       values=tensor([0.2345, 0.0289, 0.8583, 0.9123, 0.0874, 0.7501, 0.2033,
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+                      0.6766, 0.7905, 0.8999, 0.3805, 0.8437]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.6631, 0.6256, 0.9086,  ..., 0.3830, 0.1647, 0.1472])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.146198511123657 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   1,   1,  ..., 250, 250, 250]),
+       col_indices=tensor([ 989, 4634,  172,  296,  437,  988, 4701,   40, 3459,
+                           2902,  284, 2223, 3489,  379, 2336, 3854, 3801, 4913,
+                           1784,  189, 1121, 2886, 4344, 1044, 1507, 1629, 4228,
+                            950, 3157,  372, 4392,  249, 3355, 4991,   61, 3311,
+                            365, 3749, 2426, 4689,  420, 1130, 2303, 3276, 2058,
+                           3417, 2635, 1997, 4469,  149, 3640, 2817,  310, 1358,
+                           4005,  314, 2266,  836, 2231, 2153, 4590, 1083, 2752,
+                           2577, 2539, 3832,  170, 4839, 1662,  908, 3409,  160,
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+                           2982, 4418, 4646, 1091,  541, 4902, 4735, 4604, 3735,
+                           3670,  955,  687, 2373, 4360, 1850, 1893]),
+       values=tensor([0.2345, 0.0289, 0.8583, 0.9123, 0.0874, 0.7501, 0.2033,
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+                      0.9748, 0.7021, 0.7026, 0.1970, 0.7386, 0.9856, 0.8826,
+                      0.6766, 0.7905, 0.8999, 0.3805, 0.8437]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.6631, 0.6256, 0.9086,  ..., 0.3830, 0.1647, 0.1472])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.146198511123657 seconds
+
+[39.27, 38.69, 38.63, 39.39, 38.61, 38.66, 38.59, 38.96, 38.73, 38.93]
+[65.05]
+12.874769687652588
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 569391, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.146198511123657, 'TIME_S_1KI': 0.017819386873209546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 837.5037681818008, 'W': 65.05}
+[39.27, 38.69, 38.63, 39.39, 38.61, 38.66, 38.59, 38.96, 38.73, 38.93, 39.57, 38.76, 39.0, 38.65, 39.12, 39.14, 39.15, 44.31, 38.75, 39.36]
+705.7049999999999
+35.28525
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 569391, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.146198511123657, 'TIME_S_1KI': 0.017819386873209546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 837.5037681818008, 'W': 65.05, 'J_1KI': 1.4708763717406856, 'W_1KI': 0.11424486864035434, 'W_D': 29.76475, 'J_D': 383.2143010605574, 'W_D_1KI': 0.05227471105092985, 'J_D_1KI': 9.180810910416543e-05}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..1f26acc
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 1, "ITERATIONS": 520646, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.21049165725708, "TIME_S_1KI": 0.01961119773753583, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 853.8924551653862, "W": 65.17, "J_1KI": 1.6400634119255428, "W_1KI": 0.1251714216569416, "W_D": 29.995000000000005, "J_D": 393.0106520283223, "W_D_1KI": 0.05761112156820566, "J_D_1KI": 0.0001106531531370752}
diff --git a/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..65d6ab4
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/epyc_7313p_1_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.012744903564453125}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([1621, 4974, 1997,  ..., 3786, 4849,  461]),
+       values=tensor([0.2109, 0.3256, 0.0266,  ..., 0.3581, 0.6264, 0.0778]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.8357, 0.0383, 0.1188,  ..., 0.4462, 0.9461, 0.1099])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.012744903564453125 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '82385', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.6614789962768555}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([1213, 1571, 1960,  ..., 2843, 4867, 4843]),
+       values=tensor([0.3029, 0.3061, 0.3000,  ..., 0.6016, 0.9759, 0.4960]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.1072, 0.2899, 0.3055,  ..., 0.7146, 0.5978, 0.9959])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 1.6614789962768555 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '520646', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.21049165725708}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([ 472, 3691, 4268,  ..., 1601, 3041,  533]),
+       values=tensor([0.9317, 0.8516, 0.8376,  ..., 0.6191, 0.8435, 0.3776]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.6520, 0.6755, 0.7512,  ..., 0.2262, 0.3599, 0.0025])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.21049165725708 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([ 472, 3691, 4268,  ..., 1601, 3041,  533]),
+       values=tensor([0.9317, 0.8516, 0.8376,  ..., 0.6191, 0.8435, 0.3776]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.6520, 0.6755, 0.7512,  ..., 0.2262, 0.3599, 0.0025])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.21049165725708 seconds
+
+[39.48, 39.4, 39.17, 39.09, 40.79, 38.97, 38.82, 38.72, 38.81, 38.71]
+[65.17]
+13.102538824081421
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 520646, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.21049165725708, 'TIME_S_1KI': 0.01961119773753583, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 853.8924551653862, 'W': 65.17}
+[39.48, 39.4, 39.17, 39.09, 40.79, 38.97, 38.82, 38.72, 38.81, 38.71, 39.49, 39.17, 38.98, 38.86, 39.13, 38.88, 38.85, 38.81, 38.77, 38.88]
+703.5
+35.175
+{'CPU': 'Epyc 7313P', 'CORES': 1, 'ITERATIONS': 520646, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.21049165725708, 'TIME_S_1KI': 0.01961119773753583, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 853.8924551653862, 'W': 65.17, 'J_1KI': 1.6400634119255428, 'W_1KI': 0.1251714216569416, 'W_D': 29.995000000000005, 'J_D': 393.0106520283223, 'W_D_1KI': 0.05761112156820566, 'J_D_1KI': 0.0001106531531370752}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..3a94ff3
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 3659, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.513276815414429, "TIME_S_1KI": 2.8732650493070317, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 699.5738222312928, "W": 48.46, "J_1KI": 191.19262701046534, "W_1KI": 13.244055752937962, "W_D": 31.944000000000003, "J_D": 461.147052772522, "W_D_1KI": 8.73025416780541, "J_D_1KI": 2.3859672500151436}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..3030a8d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.3051443099975586}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      20,  ...,  999978,
+                             999988, 1000000]),
+       col_indices=tensor([10874, 16180, 25759,  ..., 85120, 90595, 97571]),
+       values=tensor([0.6980, 0.1450, 0.2222,  ..., 0.0442, 0.2876, 0.2305]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9393, 0.9771, 0.4381,  ..., 0.2097, 0.8256, 0.0395])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.3051443099975586 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '3440', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.870911598205566}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      16,      29,  ...,  999980,
+                             999991, 1000000]),
+       col_indices=tensor([ 5523, 13716, 16446,  ..., 89337, 96388, 97674]),
+       values=tensor([0.9883, 0.0360, 0.0063,  ..., 0.9506, 0.8956, 0.5971]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2735, 0.3671, 0.2374,  ..., 0.9952, 0.6404, 0.3809])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 9.870911598205566 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '3659', '-ss', '100000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.513276815414429}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      15,      23,  ...,  999980,
+                             999992, 1000000]),
+       col_indices=tensor([ 3420, 12508, 17596,  ..., 74140, 75972, 84324]),
+       values=tensor([0.2457, 0.5369, 0.4041,  ..., 0.2835, 0.7746, 0.0854]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1485, 0.5045, 0.7097,  ..., 0.5250, 0.4505, 0.4467])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.513276815414429 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      15,      23,  ...,  999980,
+                             999992, 1000000]),
+       col_indices=tensor([ 3420, 12508, 17596,  ..., 74140, 75972, 84324]),
+       values=tensor([0.2457, 0.5369, 0.4041,  ..., 0.2835, 0.7746, 0.0854]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.1485, 0.5045, 0.7097,  ..., 0.5250, 0.4505, 0.4467])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.513276815414429 seconds
+
+[19.03, 17.89, 18.11, 18.19, 17.92, 19.96, 20.76, 18.13, 18.16, 18.25]
+[48.46]
+14.436108589172363
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3659, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.513276815414429, 'TIME_S_1KI': 2.8732650493070317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 699.5738222312928, 'W': 48.46}
+[19.03, 17.89, 18.11, 18.19, 17.92, 19.96, 20.76, 18.13, 18.16, 18.25, 18.14, 18.17, 18.05, 17.87, 18.21, 18.32, 17.88, 17.89, 18.02, 18.16]
+330.31999999999994
+16.516
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 3659, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.513276815414429, 'TIME_S_1KI': 2.8732650493070317, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 699.5738222312928, 'W': 48.46, 'J_1KI': 191.19262701046534, 'W_1KI': 13.244055752937962, 'W_D': 31.944000000000003, 'J_D': 461.147052772522, 'W_D_1KI': 8.73025416780541, 'J_D_1KI': 2.3859672500151436}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..253d722
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 376, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.408750772476196, "TIME_S_1KI": 27.68284779913882, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 864.391910111904, "W": 48.41, "J_1KI": 2298.914654552936, "W_1KI": 128.75, "W_D": 32.1215, "J_D": 573.5501908832788, "W_D_1KI": 85.42952127659574, "J_D_1KI": 227.20617360796737}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..8c93db0
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.7904272079467773}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       83,      173,  ...,  9999788,
+                             9999895, 10000000]),
+       col_indices=tensor([  687,  1990,  2832,  ..., 93491, 98909, 99713]),
+       values=tensor([0.2182, 0.4312, 0.1873,  ..., 0.5994, 0.5663, 0.3895]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6218, 0.2935, 0.0099,  ..., 0.4944, 0.2399, 0.4191])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 2.7904272079467773 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '376', '-ss', '100000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.408750772476196}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      101,      179,  ...,  9999785,
+                             9999885, 10000000]),
+       col_indices=tensor([  974,  1017,  1175,  ..., 97865, 98322, 99037]),
+       values=tensor([0.8598, 0.2680, 0.8943,  ..., 0.1763, 0.5676, 0.4916]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4049, 0.5573, 0.2557,  ..., 0.0615, 0.7671, 0.4849])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.408750772476196 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      101,      179,  ...,  9999785,
+                             9999885, 10000000]),
+       col_indices=tensor([  974,  1017,  1175,  ..., 97865, 98322, 99037]),
+       values=tensor([0.8598, 0.2680, 0.8943,  ..., 0.1763, 0.5676, 0.4916]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4049, 0.5573, 0.2557,  ..., 0.0615, 0.7671, 0.4849])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.408750772476196 seconds
+
+[18.19, 17.86, 18.15, 17.76, 18.57, 17.97, 17.88, 17.91, 18.5, 18.33]
+[48.41]
+17.855647802352905
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 376, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.408750772476196, 'TIME_S_1KI': 27.68284779913882, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 864.391910111904, 'W': 48.41}
+[18.19, 17.86, 18.15, 17.76, 18.57, 17.97, 17.88, 17.91, 18.5, 18.33, 18.68, 17.76, 17.89, 18.01, 18.9, 17.73, 18.37, 17.97, 18.0, 17.88]
+325.77
+16.2885
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 376, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.408750772476196, 'TIME_S_1KI': 27.68284779913882, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 864.391910111904, 'W': 48.41, 'J_1KI': 2298.914654552936, 'W_1KI': 128.75, 'W_D': 32.1215, 'J_D': 573.5501908832788, 'W_D_1KI': 85.42952127659574, 'J_D_1KI': 227.20617360796737}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..9c4b03d
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 8087, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.519959688186646, "TIME_S_1KI": 1.300848236451916, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 670.7503997087479, "W": 46.91, "J_1KI": 82.94180780372794, "W_1KI": 5.800667738345492, "W_D": 30.164499999999997, "J_D": 431.3120961844921, "W_D_1KI": 3.729998763447508, "J_D_1KI": 0.46123392648046346}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..cc342ed
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.14809489250183105}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      3,  ...,  99993,  99994,
+                            100000]),
+       col_indices=tensor([11566,  2001, 14819,  ..., 49184, 52555, 95716]),
+       values=tensor([0.6903, 0.1382, 0.4591,  ..., 0.3067, 0.8088, 0.6364]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.9563, 0.6034, 0.0890,  ..., 0.8548, 0.6115, 0.7911])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.14809489250183105 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7090', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.205029487609863}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      3,  ...,  99999,  99999,
+                            100000]),
+       col_indices=tensor([ 7711, 16815, 22150,  ..., 77554, 50594, 27282]),
+       values=tensor([0.3735, 0.5582, 0.2278,  ..., 0.2317, 0.9623, 0.5188]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8847, 0.2063, 0.0570,  ..., 0.4149, 0.1346, 0.4208])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 9.205029487609863 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8087', '-ss', '100000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.519959688186646}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      3,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([95443, 50058, 77222,  ..., 43317,  3451, 10339]),
+       values=tensor([0.1078, 0.8522, 0.7935,  ..., 0.8133, 0.3945, 0.6126]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8231, 0.3876, 0.1205,  ..., 0.1479, 0.8608, 0.1605])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.519959688186646 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      3,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([95443, 50058, 77222,  ..., 43317,  3451, 10339]),
+       values=tensor([0.1078, 0.8522, 0.7935,  ..., 0.8133, 0.3945, 0.6126]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8231, 0.3876, 0.1205,  ..., 0.1479, 0.8608, 0.1605])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.519959688186646 seconds
+
+[18.45, 18.35, 18.06, 17.99, 17.93, 18.06, 18.08, 17.93, 22.44, 18.22]
+[46.91]
+14.298665523529053
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8087, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.519959688186646, 'TIME_S_1KI': 1.300848236451916, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 670.7503997087479, 'W': 46.91}
+[18.45, 18.35, 18.06, 17.99, 17.93, 18.06, 18.08, 17.93, 22.44, 18.22, 19.6, 17.81, 18.45, 17.9, 22.14, 18.02, 18.39, 17.96, 18.34, 17.85]
+334.90999999999997
+16.7455
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 8087, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.519959688186646, 'TIME_S_1KI': 1.300848236451916, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 670.7503997087479, 'W': 46.91, 'J_1KI': 82.94180780372794, 'W_1KI': 5.800667738345492, 'W_D': 30.164499999999997, 'J_D': 431.3120961844921, 'W_D_1KI': 3.729998763447508, 'J_D_1KI': 0.46123392648046346}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..0e52b1a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 4760, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.509625434875488, "TIME_S_1KI": 2.2079045031251026, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 685.1951848602295, "W": 48.04, "J_1KI": 143.94856824794735, "W_1KI": 10.092436974789916, "W_D": 31.590249999999997, "J_D": 450.57217295026777, "W_D_1KI": 6.636607142857142, "J_D_1KI": 1.3942451980792314}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..e5c487f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.2381742000579834}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,     10,  ..., 499988, 499996,
+                            500000]),
+       col_indices=tensor([ 7829, 21471, 22951,  ..., 29509, 41224, 66852]),
+       values=tensor([0.9739, 0.6225, 0.8607,  ..., 0.0619, 0.3093, 0.0510]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.6694, 0.1094, 0.7903,  ..., 0.4860, 0.7386, 0.2172])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 0.2381742000579834 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4408', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.722268104553223}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,      8,  ..., 499991, 499997,
+                            500000]),
+       col_indices=tensor([ 8468, 11831, 46487,  ..., 65418, 70471, 71020]),
+       values=tensor([0.5611, 0.2625, 0.0139,  ..., 0.7643, 0.0263, 0.5630]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.6021, 0.3259, 0.4454,  ..., 0.1291, 0.3066, 0.3093])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 9.722268104553223 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4760', '-ss', '100000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.509625434875488}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      7,  ..., 499986, 499991,
+                            500000]),
+       col_indices=tensor([37454, 51813, 86506,  ..., 62954, 73906, 92773]),
+       values=tensor([0.8256, 0.7091, 0.8154,  ..., 0.4160, 0.7952, 0.1689]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.1880, 0.2475, 0.0895,  ..., 0.2917, 0.5906, 0.9519])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.509625434875488 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      7,  ..., 499986, 499991,
+                            500000]),
+       col_indices=tensor([37454, 51813, 86506,  ..., 62954, 73906, 92773]),
+       values=tensor([0.8256, 0.7091, 0.8154,  ..., 0.4160, 0.7952, 0.1689]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.1880, 0.2475, 0.0895,  ..., 0.2917, 0.5906, 0.9519])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.509625434875488 seconds
+
+[18.07, 18.03, 18.14, 17.98, 17.8, 18.31, 18.47, 18.23, 18.16, 17.92]
+[48.04]
+14.26301383972168
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4760, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.509625434875488, 'TIME_S_1KI': 2.2079045031251026, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 685.1951848602295, 'W': 48.04}
+[18.07, 18.03, 18.14, 17.98, 17.8, 18.31, 18.47, 18.23, 18.16, 17.92, 18.33, 17.81, 18.24, 18.05, 18.09, 17.75, 17.78, 21.35, 18.67, 17.95]
+328.995
+16.44975
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4760, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.509625434875488, 'TIME_S_1KI': 2.2079045031251026, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 685.1951848602295, 'W': 48.04, 'J_1KI': 143.94856824794735, 'W_1KI': 10.092436974789916, 'W_D': 31.590249999999997, 'J_D': 450.57217295026777, 'W_D_1KI': 6.636607142857142, 'J_D_1KI': 1.3942451980792314}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..20b7d93
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 84718, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.541555881500244, "TIME_S_1KI": 0.12443112303761, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 661.3758532905579, "W": 46.44, "J_1KI": 7.80679257407585, "W_1KI": 0.5481715810099388, "W_D": 30.123999999999995, "J_D": 429.01133084678645, "W_D_1KI": 0.3555796879057579, "J_D_1KI": 0.004197215325028422}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..79fd885
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.028100967407226562}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([1696, 2591, 3015,  ..., 1730, 8585, 3790]),
+       values=tensor([0.6837, 0.7697, 0.7550,  ..., 0.1323, 0.4514, 0.4553]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1557, 0.9230, 0.6401,  ..., 0.3725, 0.8926, 0.6402])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.028100967407226562 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '37365', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 4.631014823913574}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     4,  ...,  9998,  9998, 10000]),
+       col_indices=tensor([2386, 8388, 9261,  ..., 1344, 2569, 4425]),
+       values=tensor([0.6000, 0.2415, 0.7139,  ..., 0.1197, 0.4001, 0.0791]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.9194, 0.6903, 0.0708,  ..., 0.1917, 0.6424, 0.6800])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 4.631014823913574 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '84718', '-ss', '10000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.541555881500244}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([6630, 3375,  966,  ..., 5451,   76,  624]),
+       values=tensor([0.0314, 0.6841, 0.2123,  ..., 0.3011, 0.8872, 0.9156]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.4846, 0.1841, 0.4323,  ..., 0.0718, 0.1957, 0.5902])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.541555881500244 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([6630, 3375,  966,  ..., 5451,   76,  624]),
+       values=tensor([0.0314, 0.6841, 0.2123,  ..., 0.3011, 0.8872, 0.9156]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.4846, 0.1841, 0.4323,  ..., 0.0718, 0.1957, 0.5902])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.541555881500244 seconds
+
+[18.42, 18.02, 18.15, 18.67, 18.24, 18.06, 18.05, 17.99, 18.11, 18.07]
+[46.44]
+14.241512775421143
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 84718, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.541555881500244, 'TIME_S_1KI': 0.12443112303761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.3758532905579, 'W': 46.44}
+[18.42, 18.02, 18.15, 18.67, 18.24, 18.06, 18.05, 17.99, 18.11, 18.07, 18.36, 18.1, 17.99, 18.16, 18.02, 18.13, 18.12, 18.03, 18.02, 18.07]
+326.32000000000005
+16.316000000000003
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 84718, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.541555881500244, 'TIME_S_1KI': 0.12443112303761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.3758532905579, 'W': 46.44, 'J_1KI': 7.80679257407585, 'W_1KI': 0.5481715810099388, 'W_D': 30.123999999999995, 'J_D': 429.01133084678645, 'W_D_1KI': 0.3555796879057579, 'J_D_1KI': 0.004197215325028422}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..e07fffc
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 34651, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.471668004989624, "TIME_S_1KI": 0.30220391922281103, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 677.0323536157608, "W": 47.45, "J_1KI": 19.53860937969354, "W_1KI": 1.369368849383856, "W_D": 30.828750000000007, "J_D": 439.8748402851821, "W_D_1KI": 0.8896929381547433, "J_D_1KI": 0.025675822866720825}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..ff06711
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.04611039161682129}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     22,  ...,  99983,  99992,
+                            100000]),
+       col_indices=tensor([ 845, 1153, 1508,  ..., 8313, 9367, 9854]),
+       values=tensor([0.8746, 0.4039, 0.9243,  ..., 0.5657, 0.2713, 0.6449]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.3540, 0.2628, 0.4314,  ..., 0.0912, 0.3507, 0.8651])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.04611039161682129 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22771', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 6.900022268295288}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     19,  ...,  99978,  99991,
+                            100000]),
+       col_indices=tensor([ 917, 1959, 2965,  ..., 8075, 8263, 9058]),
+       values=tensor([0.2271, 0.8712, 0.9636,  ..., 0.2167, 0.1262, 0.7253]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8029, 0.1325, 0.2655,  ..., 0.5832, 0.4718, 0.3144])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 6.900022268295288 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '34651', '-ss', '10000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.471668004989624}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     14,  ...,  99994,  99997,
+                            100000]),
+       col_indices=tensor([  24, 1396, 2236,  ..., 5590, 6310, 9874]),
+       values=tensor([0.4982, 0.6812, 0.1465,  ..., 0.3747, 0.0311, 0.9162]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7533, 0.0703, 0.6276,  ..., 0.6008, 0.2603, 0.3256])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.471668004989624 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     14,  ...,  99994,  99997,
+                            100000]),
+       col_indices=tensor([  24, 1396, 2236,  ..., 5590, 6310, 9874]),
+       values=tensor([0.4982, 0.6812, 0.1465,  ..., 0.3747, 0.0311, 0.9162]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7533, 0.0703, 0.6276,  ..., 0.6008, 0.2603, 0.3256])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.471668004989624 seconds
+
+[18.34, 18.19, 18.22, 18.02, 22.16, 18.64, 18.12, 17.87, 17.95, 18.81]
+[47.45]
+14.26833200454712
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 34651, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.471668004989624, 'TIME_S_1KI': 0.30220391922281103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 677.0323536157608, 'W': 47.45}
+[18.34, 18.19, 18.22, 18.02, 22.16, 18.64, 18.12, 17.87, 17.95, 18.81, 22.38, 17.93, 18.41, 17.77, 18.07, 17.88, 18.52, 17.88, 18.1, 17.86]
+332.42499999999995
+16.621249999999996
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 34651, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.471668004989624, 'TIME_S_1KI': 0.30220391922281103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 677.0323536157608, 'W': 47.45, 'J_1KI': 19.53860937969354, 'W_1KI': 1.369368849383856, 'W_D': 30.828750000000007, 'J_D': 439.8748402851821, 'W_D_1KI': 0.8896929381547433, 'J_D_1KI': 0.025675822866720825}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..89c422b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 5583, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.513006925582886, "TIME_S_1KI": 1.8830390337780558, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 692.6418607521057, "W": 47.919999999999995, "J_1KI": 124.06266536845884, "W_1KI": 8.58319899695504, "W_D": 31.175249999999995, "J_D": 450.611084503591, "W_D_1KI": 5.583960236432024, "J_D_1KI": 1.0001719929127753}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..d152642
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.2040116786956787}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     106,     193,  ...,  999811,
+                             999905, 1000000]),
+       col_indices=tensor([ 107,  139,  344,  ..., 9485, 9560, 9767]),
+       values=tensor([0.2657, 0.7219, 0.2773,  ..., 0.6022, 0.5377, 0.2291]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8604, 0.1859, 0.3719,  ..., 0.6286, 0.9460, 0.3185])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.2040116786956787 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5146', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.677676439285278}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      90,     186,  ...,  999811,
+                             999907, 1000000]),
+       col_indices=tensor([  74,  208,  311,  ..., 9654, 9863, 9976]),
+       values=tensor([0.0395, 0.4059, 0.0831,  ..., 0.6188, 0.9591, 0.8953]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6360, 0.9265, 0.4313,  ..., 0.6926, 0.7242, 0.0651])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 9.677676439285278 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5583', '-ss', '10000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.513006925582886}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     104,     207,  ...,  999792,
+                             999898, 1000000]),
+       col_indices=tensor([ 168,  206,  240,  ..., 9827, 9842, 9996]),
+       values=tensor([0.8276, 0.5768, 0.6424,  ..., 0.0752, 0.7475, 0.3129]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7571, 0.4178, 0.1860,  ..., 0.0563, 0.6255, 0.7203])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.513006925582886 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     104,     207,  ...,  999792,
+                             999898, 1000000]),
+       col_indices=tensor([ 168,  206,  240,  ..., 9827, 9842, 9996]),
+       values=tensor([0.8276, 0.5768, 0.6424,  ..., 0.0752, 0.7475, 0.3129]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.7571, 0.4178, 0.1860,  ..., 0.0563, 0.6255, 0.7203])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.513006925582886 seconds
+
+[18.38, 17.86, 18.24, 17.87, 19.06, 17.88, 18.29, 21.74, 18.54, 18.08]
+[47.92]
+14.454128980636597
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5583, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.513006925582886, 'TIME_S_1KI': 1.8830390337780558, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.6418607521057, 'W': 47.919999999999995}
+[18.38, 17.86, 18.24, 17.87, 19.06, 17.88, 18.29, 21.74, 18.54, 18.08, 18.58, 17.99, 17.96, 22.09, 18.22, 17.97, 17.93, 18.49, 18.13, 18.23]
+334.895
+16.74475
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 5583, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.513006925582886, 'TIME_S_1KI': 1.8830390337780558, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 692.6418607521057, 'W': 47.919999999999995, 'J_1KI': 124.06266536845884, 'W_1KI': 8.58319899695504, 'W_D': 31.175249999999995, 'J_D': 450.611084503591, 'W_D_1KI': 5.583960236432024, 'J_D_1KI': 1.0001719929127753}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..20cc2bc
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 959, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.294347047805786, "TIME_S_1KI": 10.73445990386422, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 792.9360737204552, "W": 48.35, "J_1KI": 826.8363646720074, "W_1KI": 50.417101147028156, "W_D": 31.9585, "J_D": 524.1168047982454, "W_D_1KI": 33.324817518248175, "J_D_1KI": 34.74954902841311}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..643494f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 1.0941581726074219}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     514,    1009,  ..., 4998988,
+                            4999478, 5000000]),
+       col_indices=tensor([   8,   23,   83,  ..., 9969, 9982, 9990]),
+       values=tensor([0.1393, 0.4453, 0.1108,  ..., 0.3215, 0.7885, 0.8444]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.3317, 0.0622, 0.5595,  ..., 0.2290, 0.2268, 0.9236])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 1.0941581726074219 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '959', '-ss', '10000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.294347047805786}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     514,     994,  ..., 4999000,
+                            4999500, 5000000]),
+       col_indices=tensor([  13,   16,   23,  ..., 9955, 9988, 9993]),
+       values=tensor([0.2414, 0.9977, 0.9772,  ..., 0.9200, 0.6029, 0.8714]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.0530, 0.1017, 0.7510,  ..., 0.2543, 0.0728, 0.9686])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.294347047805786 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     514,     994,  ..., 4999000,
+                            4999500, 5000000]),
+       col_indices=tensor([  13,   16,   23,  ..., 9955, 9988, 9993]),
+       values=tensor([0.2414, 0.9977, 0.9772,  ..., 0.9200, 0.6029, 0.8714]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.0530, 0.1017, 0.7510,  ..., 0.2543, 0.0728, 0.9686])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.294347047805786 seconds
+
+[18.47, 18.05, 17.89, 17.81, 18.54, 18.75, 17.99, 17.87, 18.49, 18.81]
+[48.35]
+16.399918794631958
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 959, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.294347047805786, 'TIME_S_1KI': 10.73445990386422, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 792.9360737204552, 'W': 48.35}
+[18.47, 18.05, 17.89, 17.81, 18.54, 18.75, 17.99, 17.87, 18.49, 18.81, 18.47, 17.8, 17.9, 18.12, 18.16, 18.55, 18.0, 18.63, 18.45, 17.91]
+327.83
+16.3915
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 959, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.294347047805786, 'TIME_S_1KI': 10.73445990386422, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 792.9360737204552, 'W': 48.35, 'J_1KI': 826.8363646720074, 'W_1KI': 50.417101147028156, 'W_D': 31.9585, 'J_D': 524.1168047982454, 'W_D_1KI': 33.324817518248175, 'J_D_1KI': 34.74954902841311}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..a3bba53
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 389, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.411779165267944, "TIME_S_1KI": 26.765499139506282, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 876.1174001097679, "W": 47.85, "J_1KI": 2252.2298203335936, "W_1KI": 123.00771208226222, "W_D": 31.457250000000002, "J_D": 575.971663210094, "W_D_1KI": 80.86696658097686, "J_D_1KI": 207.88423285598165}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..086f941
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 2.693603992462158}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1004,     2049,  ...,  9997955,
+                             9998949, 10000000]),
+       col_indices=tensor([   1,    3,   35,  ..., 9984, 9987, 9993]),
+       values=tensor([0.3631, 0.8073, 0.7190,  ..., 0.1286, 0.7057, 0.1104]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7885, 0.1169, 0.1101,  ..., 0.4416, 0.5822, 0.3212])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 2.693603992462158 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '389', '-ss', '10000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.411779165267944}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      966,     1941,  ...,  9997989,
+                             9998973, 10000000]),
+       col_indices=tensor([   2,   11,   26,  ..., 9956, 9965, 9978]),
+       values=tensor([0.0342, 0.9218, 0.6993,  ..., 0.4506, 0.1146, 0.2093]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6037, 0.3505, 0.1319,  ..., 0.1315, 0.2126, 0.8791])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.411779165267944 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      966,     1941,  ...,  9997989,
+                             9998973, 10000000]),
+       col_indices=tensor([   2,   11,   26,  ..., 9956, 9965, 9978]),
+       values=tensor([0.0342, 0.9218, 0.6993,  ..., 0.4506, 0.1146, 0.2093]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6037, 0.3505, 0.1319,  ..., 0.1315, 0.2126, 0.8791])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.411779165267944 seconds
+
+[18.87, 18.17, 18.37, 17.93, 17.97, 18.29, 18.55, 17.92, 18.13, 17.84]
+[47.85]
+18.30966353416443
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 389, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.411779165267944, 'TIME_S_1KI': 26.765499139506282, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 876.1174001097679, 'W': 47.85}
+[18.87, 18.17, 18.37, 17.93, 17.97, 18.29, 18.55, 17.92, 18.13, 17.84, 19.36, 18.07, 17.99, 17.91, 18.82, 18.49, 18.1, 18.04, 18.09, 17.96]
+327.855
+16.39275
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 389, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.411779165267944, 'TIME_S_1KI': 26.765499139506282, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 876.1174001097679, 'W': 47.85, 'J_1KI': 2252.2298203335936, 'W_1KI': 123.00771208226222, 'W_D': 31.457250000000002, 'J_D': 575.971663210094, 'W_D_1KI': 80.86696658097686, 'J_D_1KI': 207.88423285598165}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..9e5a000
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 193, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.419133424758911, "TIME_S_1KI": 53.98514727854358, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1480.8600453448294, "W": 45.41, "J_1KI": 7672.849975879945, "W_1KI": 235.28497409326422, "W_D": 29.142749999999996, "J_D": 950.3707132013438, "W_D_1KI": 150.9987046632124, "J_D_1KI": 782.3767080995461}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..f051b42
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 5.42013144493103}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2043,     4027,  ..., 19995921,
+                            19998006, 20000000]),
+       col_indices=tensor([   9,   15,   16,  ..., 9989, 9991, 9993]),
+       values=tensor([0.8685, 0.2737, 0.0800,  ..., 0.3440, 0.3550, 0.7008]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.2841, 0.6439, 0.8852,  ..., 0.0124, 0.9656, 0.3759])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 5.42013144493103 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '193', '-ss', '10000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.419133424758911}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1925,     3946,  ..., 19996050,
+                            19998038, 20000000]),
+       col_indices=tensor([   4,    9,   22,  ..., 9994, 9995, 9997]),
+       values=tensor([0.4143, 0.0158, 0.3991,  ..., 0.4975, 0.2189, 0.5132]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.4618, 0.1550, 0.6479,  ..., 0.8342, 0.8619, 0.7737])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.419133424758911 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1925,     3946,  ..., 19996050,
+                            19998038, 20000000]),
+       col_indices=tensor([   4,    9,   22,  ..., 9994, 9995, 9997]),
+       values=tensor([0.4143, 0.0158, 0.3991,  ..., 0.4975, 0.2189, 0.5132]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.4618, 0.1550, 0.6479,  ..., 0.8342, 0.8619, 0.7737])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.419133424758911 seconds
+
+[18.6, 18.13, 18.25, 17.88, 17.94, 17.94, 18.23, 18.23, 18.37, 17.79]
+[45.41]
+32.61087965965271
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.419133424758911, 'TIME_S_1KI': 53.98514727854358, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1480.8600453448294, 'W': 45.41}
+[18.6, 18.13, 18.25, 17.88, 17.94, 17.94, 18.23, 18.23, 18.37, 17.79, 18.29, 17.94, 17.98, 17.82, 18.04, 18.23, 17.96, 17.98, 18.11, 17.95]
+325.345
+16.26725
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 193, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.419133424758911, 'TIME_S_1KI': 53.98514727854358, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1480.8600453448294, 'W': 45.41, 'J_1KI': 7672.849975879945, 'W_1KI': 235.28497409326422, 'W_D': 29.142749999999996, 'J_D': 950.3707132013438, 'W_D_1KI': 150.9987046632124, 'J_D_1KI': 782.3767080995461}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..28c2d57
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.80802845954895, "TIME_S_1KI": 108.0802845954895, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3840.7128683352466, "W": 37.19, "J_1KI": 38407.128683352465, "W_1KI": 371.9, "W_D": 20.811749999999996, "J_D": 2149.286260757625, "W_D_1KI": 208.11749999999995, "J_D_1KI": 2081.1749999999997}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..5b24363
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.80802845954895}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2964,     5988,  ..., 29994017,
+                            29996992, 30000000]),
+       col_indices=tensor([   4,    9,   14,  ..., 9990, 9993, 9995]),
+       values=tensor([0.5510, 0.0884, 0.7125,  ..., 0.7844, 0.3492, 0.1801]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.7648, 0.2589, 0.8570,  ..., 0.0438, 0.7014, 0.5513])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.80802845954895 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2964,     5988,  ..., 29994017,
+                            29996992, 30000000]),
+       col_indices=tensor([   4,    9,   14,  ..., 9990, 9993, 9995]),
+       values=tensor([0.5510, 0.0884, 0.7125,  ..., 0.7844, 0.3492, 0.1801]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.7648, 0.2589, 0.8570,  ..., 0.0438, 0.7014, 0.5513])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.80802845954895 seconds
+
+[18.36, 18.76, 18.08, 17.8, 18.22, 18.13, 18.92, 17.85, 18.59, 18.05]
+[37.19]
+103.27273106575012
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.80802845954895, 'TIME_S_1KI': 108.0802845954895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3840.7128683352466, 'W': 37.19}
+[18.36, 18.76, 18.08, 17.8, 18.22, 18.13, 18.92, 17.85, 18.59, 18.05, 18.39, 18.01, 18.68, 17.8, 18.23, 17.94, 17.9, 17.92, 18.33, 18.01]
+327.56500000000005
+16.37825
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.80802845954895, 'TIME_S_1KI': 108.0802845954895, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3840.7128683352466, 'W': 37.19, 'J_1KI': 38407.128683352465, 'W_1KI': 371.9, 'W_D': 20.811749999999996, 'J_D': 2149.286260757625, 'W_D_1KI': 208.11749999999995, 'J_D_1KI': 2081.1749999999997}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..d3d85cb
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 223318, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.272708654403687, "TIME_S_1KI": 0.046000361163917314, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 661.7633163213729, "W": 46.54, "J_1KI": 2.963322778823798, "W_1KI": 0.2084023679237679, "W_D": 29.777749999999997, "J_D": 423.41690143078563, "W_D_1KI": 0.13334236380408207, "J_D_1KI": 0.0005970963549919042}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..404ad45
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1307 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.02031111717224121}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([4137, 7377, 7043, 6108, 9323,  897, 9654,  227, 2578,
+                           4698, 9915, 4015, 9949, 2349, 8008, 1966, 4875, 4040,
+                           6979, 2450, 7633, 6619, 9795, 2725, 5746, 5264, 4390,
+                           2139, 8315, 3676, 8258, 2550, 7703, 6861, 8246, 1325,
+                           6548, 4010, 3106, 8161, 3532, 2108, 7993,  386, 7921,
+                           2704, 9957, 7073, 3331, 9744, 4980, 6346, 1498, 1404,
+                           8824, 1275, 9681, 2862, 4082, 1465, 5274, 7840, 5924,
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+                           2000, 8258, 8990, 1304, 1955, 2898, 4931, 9660,  749,
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+                      6.2691e-01, 4.0234e-01, 8.7349e-01, 9.0944e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.3846, 0.0429, 0.8390,  ..., 0.4444, 0.4671, 0.5693])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.02031111717224121 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '51695', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.430596351623535}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      2.8594e-01, 9.8090e-01, 8.5909e-01, 7.8064e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8755, 0.4171, 0.4007,  ..., 0.3638, 0.0663, 0.0983])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 2.430596351623535 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '223318', '-ss', '10000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.272708654403687}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([1701, 5200,  151, 7106, 6780, 2055, 1375, 8242,  376,
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+                      0.5469, 0.0958, 0.2762, 0.2054, 0.3215, 0.7664]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5980, 0.9421, 0.9493,  ..., 0.6518, 0.5202, 0.4457])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.272708654403687 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.8056, 0.2886, 0.8492, 0.0497, 0.6903, 0.0986, 0.3481,
+                      0.9830, 0.1513, 0.7150, 0.5003, 0.6462, 0.3091, 0.5104,
+                      0.1090, 0.4875, 0.2487, 0.1973, 0.1005, 0.5965, 0.6503,
+                      0.6776, 0.1194, 0.6968, 0.4165, 0.3997, 0.2356, 0.9948,
+                      0.2469, 0.5798, 0.3444, 0.2015, 0.9011, 0.6742, 0.3975,
+                      0.8612, 0.7831, 0.0862, 0.4379, 0.0809, 0.7931, 0.0607,
+                      0.3830, 0.8447, 0.6462, 0.9840, 0.5433, 0.4101, 0.8260,
+                      0.5601, 0.6978, 0.1804, 0.4108, 0.0158, 0.7436, 0.8390,
+                      0.9475, 0.4933, 0.1487, 0.4644, 0.5877, 0.7798, 0.6315,
+                      0.5300, 0.7588, 0.5234, 0.0393, 0.3814, 0.5279, 0.6528,
+                      0.9869, 0.9540, 0.6270, 0.7013, 0.4305, 0.2085, 0.9008,
+                      0.4866, 0.4271, 0.1271, 0.3216, 0.3653, 0.9917, 0.2290,
+                      0.3330, 0.6020, 0.4543, 0.6559, 0.8582, 0.2937, 0.1913,
+                      0.3080, 0.3895, 0.3059, 0.0633, 0.6122, 0.1175, 0.6572,
+                      0.2524, 0.7704, 0.8210, 0.4794, 0.7538, 0.4031, 0.0275,
+                      0.5311, 0.0815, 0.4034, 0.6148, 0.1185, 0.7439, 0.4016,
+                      0.5608, 0.0706, 0.4835, 0.2505, 0.9363, 0.3741, 0.3124,
+                      0.4634, 0.9844, 0.4189, 0.3458, 0.6504, 0.0883, 0.9154,
+                      0.1987, 0.8102, 0.2403, 0.6352, 0.9134, 0.0740, 0.6217,
+                      0.7461, 0.9307, 0.2340, 0.9526, 0.2394, 0.1661, 0.5903,
+                      0.3531, 0.3579, 0.3335, 0.4941, 0.0937, 0.0190, 0.7400,
+                      0.6048, 0.0736, 0.8480, 0.7056, 0.9624, 0.8952, 0.2590,
+                      0.2918, 0.9369, 0.3316, 0.8127, 0.4530, 0.8583, 0.8824,
+                      0.6823, 0.0554, 0.6007, 0.8647, 0.0307, 0.2993, 0.2476,
+                      0.7318, 0.8917, 0.9643, 0.6157, 0.2184, 0.8408, 0.3345,
+                      0.0712, 0.8159, 0.2459, 0.0991, 0.7444, 0.2222, 0.0014,
+                      0.1305, 0.8914, 0.0089, 0.5321, 0.7917, 0.7163, 0.9580,
+                      0.3624, 0.0142, 0.8937, 0.5115, 0.5049, 0.8434, 0.7234,
+                      0.7161, 0.2634, 0.8592, 0.3961, 0.5586, 0.2620, 0.0375,
+                      0.1665, 0.2915, 0.9139, 0.7009, 0.5095, 0.4519, 0.1213,
+                      0.3561, 0.0066, 0.4379, 0.3522, 0.6225, 0.6900, 0.8216,
+                      0.8841, 0.6553, 0.8193, 0.7688, 0.5104, 0.3926, 0.7388,
+                      0.4735, 0.1897, 0.7788, 0.8825, 0.9103, 0.2988, 0.1239,
+                      0.1792, 0.1266, 0.4818, 0.8893, 0.6604, 0.1883, 0.9700,
+                      0.5469, 0.0958, 0.2762, 0.2054, 0.3215, 0.7664]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5980, 0.9421, 0.9493,  ..., 0.6518, 0.5202, 0.4457])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.272708654403687 seconds
+
+[18.39, 17.85, 18.29, 17.9, 18.11, 17.8, 18.16, 22.08, 18.79, 18.14]
+[46.54]
+14.219237565994263
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 223318, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.272708654403687, 'TIME_S_1KI': 0.046000361163917314, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.7633163213729, 'W': 46.54}
+[18.39, 17.85, 18.29, 17.9, 18.11, 17.8, 18.16, 22.08, 18.79, 18.14, 20.06, 18.33, 18.25, 21.91, 18.14, 18.0, 18.29, 18.13, 17.88, 18.08]
+335.245
+16.76225
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 223318, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.272708654403687, 'TIME_S_1KI': 0.046000361163917314, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 661.7633163213729, 'W': 46.54, 'J_1KI': 2.963322778823798, 'W_1KI': 0.2084023679237679, 'W_D': 29.777749999999997, 'J_D': 423.41690143078563, 'W_D_1KI': 0.13334236380408207, 'J_D_1KI': 0.0005970963549919042}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..d34e261
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 115566, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.362370014190674, "TIME_S_1KI": 0.08966625144238508, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 657.2445391845703, "W": 46.58, "J_1KI": 5.687179094063741, "W_1KI": 0.40305972344807295, "W_D": 30.2825, "J_D": 427.28655555725095, "W_D_1KI": 0.2620364120935223, "J_D_1KI": 0.002267417857272228}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..30badbd
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.024602413177490234}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    2,    3,  ..., 4997, 4999, 5000]),
+       col_indices=tensor([5115, 9337, 5262,  ..., 1244, 4227, 2124]),
+       values=tensor([0.7036, 0.8839, 0.8989,  ..., 0.3409, 0.8377, 0.4572]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.4155, 0.9580, 0.1653,  ..., 0.8843, 0.0512, 0.6581])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.024602413177490234 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '42678', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.8775720596313477}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4998, 4999, 5000]),
+       col_indices=tensor([ 717, 6679, 2344,  ..., 3928, 4219, 6236]),
+       values=tensor([0.9595, 0.5891, 0.3421,  ..., 0.3760, 0.2961, 0.2336]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.6496, 0.5288, 0.3835,  ..., 0.2038, 0.3313, 0.3083])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 3.8775720596313477 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '115566', '-ss', '10000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.362370014190674}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([6593, 2332, 3653,  ..., 6447, 6839, 4175]),
+       values=tensor([0.4277, 0.1691, 0.2657,  ..., 0.2731, 0.4419, 0.1553]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.3155, 0.6085, 0.7514,  ..., 0.0185, 0.1956, 0.3828])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.362370014190674 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([6593, 2332, 3653,  ..., 6447, 6839, 4175]),
+       values=tensor([0.4277, 0.1691, 0.2657,  ..., 0.2731, 0.4419, 0.1553]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.3155, 0.6085, 0.7514,  ..., 0.0185, 0.1956, 0.3828])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.362370014190674 seconds
+
+[18.44, 17.79, 18.13, 18.16, 18.19, 17.86, 18.01, 17.82, 18.05, 18.06]
+[46.58]
+14.110015869140625
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 115566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.362370014190674, 'TIME_S_1KI': 0.08966625144238508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 657.2445391845703, 'W': 46.58}
+[18.44, 17.79, 18.13, 18.16, 18.19, 17.86, 18.01, 17.82, 18.05, 18.06, 18.35, 17.92, 18.08, 18.77, 17.97, 18.23, 18.59, 17.91, 18.14, 17.81]
+325.95
+16.2975
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 115566, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.362370014190674, 'TIME_S_1KI': 0.08966625144238508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 657.2445391845703, 'W': 46.58, 'J_1KI': 5.687179094063741, 'W_1KI': 0.40305972344807295, 'W_D': 30.2825, 'J_D': 427.28655555725095, 'W_D_1KI': 0.2620364120935223, 'J_D_1KI': 0.002267417857272228}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..5b0e976
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.447505474090576, "TIME_S_1KI": 154.47505474090576, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2621.8117760252953, "W": 44.61, "J_1KI": 26218.117760252953, "W_1KI": 446.1, "W_D": 28.050250000000002, "J_D": 1648.56480095166, "W_D_1KI": 280.50250000000005, "J_D_1KI": 2805.0250000000005}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..55f3dfa
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.447505474090576}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       57,      102,  ..., 24999899,
+                            24999940, 25000000]),
+       col_indices=tensor([  5577,  28835,  47310,  ..., 481805, 486701,
+                           494412]),
+       values=tensor([0.4828, 0.8396, 0.7554,  ..., 0.0896, 0.7495, 0.8303]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.9266, 0.8773, 0.4193,  ..., 0.0231, 0.6267, 0.1934])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 15.447505474090576 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       57,      102,  ..., 24999899,
+                            24999940, 25000000]),
+       col_indices=tensor([  5577,  28835,  47310,  ..., 481805, 486701,
+                           494412]),
+       values=tensor([0.4828, 0.8396, 0.7554,  ..., 0.0896, 0.7495, 0.8303]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.9266, 0.8773, 0.4193,  ..., 0.0231, 0.6267, 0.1934])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 15.447505474090576 seconds
+
+[18.81, 17.9, 18.4, 17.9, 18.2, 17.84, 18.01, 22.17, 18.54, 18.17]
+[44.61]
+58.77183985710144
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.447505474090576, 'TIME_S_1KI': 154.47505474090576, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2621.8117760252953, 'W': 44.61}
+[18.81, 17.9, 18.4, 17.9, 18.2, 17.84, 18.01, 22.17, 18.54, 18.17, 18.44, 18.17, 17.99, 18.08, 18.12, 18.6, 18.11, 18.22, 18.18, 18.11]
+331.19499999999994
+16.559749999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.447505474090576, 'TIME_S_1KI': 154.47505474090576, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2621.8117760252953, 'W': 44.61, 'J_1KI': 26218.117760252953, 'W_1KI': 446.1, 'W_D': 28.050250000000002, 'J_D': 1648.56480095166, 'W_D_1KI': 280.50250000000005, 'J_D_1KI': 2805.0250000000005}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..8a43ad7
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 751, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.154199123382568, "TIME_S_1KI": 13.520904292120598, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 705.406801700592, "W": 48.84, "J_1KI": 939.290015580016, "W_1KI": 65.03328894806924, "W_D": 32.38000000000001, "J_D": 467.67142176628124, "W_D_1KI": 43.115845539280976, "J_D_1KI": 57.41124572474165}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..9480055
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.3980276584625244}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       7,      14,  ..., 2499987,
+                            2499996, 2500000]),
+       col_indices=tensor([ 56026, 195485, 327540,  ...,  74351, 467081,
+                           495492]),
+       values=tensor([0.8691, 0.8234, 0.1160,  ..., 0.0380, 0.6115, 0.8262]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8652, 0.4148, 0.1413,  ..., 0.7873, 0.1950, 0.8001])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 1.3980276584625244 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '751', '-ss', '500000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.154199123382568}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       3,      11,  ..., 2499987,
+                            2499993, 2500000]),
+       col_indices=tensor([159259, 352180, 455738,  ..., 361655, 368506,
+                           421546]),
+       values=tensor([0.7015, 0.3878, 0.3559,  ..., 0.2417, 0.3895, 0.7278]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4879, 0.7909, 0.7587,  ..., 0.1983, 0.9582, 0.5253])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.154199123382568 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       3,      11,  ..., 2499987,
+                            2499993, 2500000]),
+       col_indices=tensor([159259, 352180, 455738,  ..., 361655, 368506,
+                           421546]),
+       values=tensor([0.7015, 0.3878, 0.3559,  ..., 0.2417, 0.3895, 0.7278]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4879, 0.7909, 0.7587,  ..., 0.1983, 0.9582, 0.5253])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.154199123382568 seconds
+
+[18.79, 17.69, 18.39, 21.58, 18.07, 18.02, 18.17, 17.87, 17.87, 18.03]
+[48.84]
+14.44321870803833
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 751, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.154199123382568, 'TIME_S_1KI': 13.520904292120598, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 705.406801700592, 'W': 48.84}
+[18.79, 17.69, 18.39, 21.58, 18.07, 18.02, 18.17, 17.87, 17.87, 18.03, 18.42, 17.82, 17.95, 18.61, 17.96, 18.22, 17.97, 18.42, 18.04, 17.86]
+329.19999999999993
+16.459999999999997
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 751, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.154199123382568, 'TIME_S_1KI': 13.520904292120598, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 705.406801700592, 'W': 48.84, 'J_1KI': 939.290015580016, 'W_1KI': 65.03328894806924, 'W_D': 32.38000000000001, 'J_D': 467.67142176628124, 'W_D_1KI': 43.115845539280976, 'J_D_1KI': 57.41124572474165}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..e9ce521
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 147, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.563251495361328, "TIME_S_1KI": 71.85885370994102, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 973.9529043245316, "W": 47.49, "J_1KI": 6625.529961391371, "W_1KI": 323.0612244897959, "W_D": 30.996250000000003, "J_D": 635.6893600898982, "W_D_1KI": 210.858843537415, "J_D_1KI": 1434.4139016150682}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..b78ba4c
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.09878396987915}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       30,       58,  ..., 12499941,
+                            12499962, 12500000]),
+       col_indices=tensor([  1470,   2567,   5271,  ..., 471166, 490246,
+                           499700]),
+       values=tensor([0.1668, 0.7788, 0.4321,  ..., 0.7966, 0.9450, 0.5105]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9128, 0.0898, 0.7303,  ..., 0.7724, 0.8343, 0.8680])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 7.09878396987915 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '147', '-ss', '500000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.563251495361328}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       16,       39,  ..., 12499961,
+                            12499982, 12500000]),
+       col_indices=tensor([ 42415,  50722,  59820,  ..., 419133, 436999,
+                           480407]),
+       values=tensor([0.4848, 0.7890, 0.2846,  ..., 0.4428, 0.7066, 0.1150]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.7955, 0.8202, 0.0668,  ..., 0.2866, 0.4586, 0.4680])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.563251495361328 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       16,       39,  ..., 12499961,
+                            12499982, 12500000]),
+       col_indices=tensor([ 42415,  50722,  59820,  ..., 419133, 436999,
+                           480407]),
+       values=tensor([0.4848, 0.7890, 0.2846,  ..., 0.4428, 0.7066, 0.1150]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.7955, 0.8202, 0.0668,  ..., 0.2866, 0.4586, 0.4680])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.563251495361328 seconds
+
+[18.64, 21.68, 17.93, 17.89, 17.96, 18.08, 18.05, 17.94, 17.92, 17.72]
+[47.49]
+20.508589267730713
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.563251495361328, 'TIME_S_1KI': 71.85885370994102, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 973.9529043245316, 'W': 47.49}
+[18.64, 21.68, 17.93, 17.89, 17.96, 18.08, 18.05, 17.94, 17.92, 17.72, 18.36, 17.99, 18.51, 17.9, 18.05, 18.98, 18.56, 18.01, 18.0, 18.13]
+329.875
+16.49375
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.563251495361328, 'TIME_S_1KI': 71.85885370994102, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 973.9529043245316, 'W': 47.49, 'J_1KI': 6625.529961391371, 'W_1KI': 323.0612244897959, 'W_D': 30.996250000000003, 'J_D': 635.6893600898982, 'W_D_1KI': 210.858843537415, 'J_D_1KI': 1434.4139016150682}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..4d243f2
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 9147, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.50059986114502, "TIME_S_1KI": 1.1479829300475588, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 681.5257016324997, "W": 47.63, "J_1KI": 74.50811212774677, "W_1KI": 5.207171750300645, "W_D": 31.135, "J_D": 445.50289146184923, "W_D_1KI": 3.4038482562588825, "J_D_1KI": 0.37212728285327235}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..2f48a00
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.13143563270568848}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,     12,  ..., 249990, 249995,
+                            250000]),
+       col_indices=tensor([33764,   781,  3609,  ..., 16676, 21435, 31146]),
+       values=tensor([0.7781, 0.6572, 0.9120,  ..., 0.8330, 0.7571, 0.7121]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7934, 0.6307, 0.2590,  ..., 0.3547, 0.1547, 0.8460])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.13143563270568848 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7988', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 9.168656826019287}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     10,     18,  ..., 249992, 249995,
+                            250000]),
+       col_indices=tensor([ 6728,  8437,  8523,  ..., 40465, 44043, 46138]),
+       values=tensor([0.4640, 0.7108, 0.7346,  ..., 0.1761, 0.2770, 0.7056]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0571, 0.2747, 0.4590,  ..., 0.7273, 0.2570, 0.3128])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 9.168656826019287 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9147', '-ss', '50000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.50059986114502}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     10,  ..., 249987, 249996,
+                            250000]),
+       col_indices=tensor([ 4811,  5188, 33226,  ..., 17568, 20020, 26384]),
+       values=tensor([0.5580, 0.6578, 0.5141,  ..., 0.8482, 0.1339, 0.2046]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.2505, 0.9140, 0.1873,  ..., 0.2385, 0.4644, 0.1302])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.50059986114502 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     10,  ..., 249987, 249996,
+                            250000]),
+       col_indices=tensor([ 4811,  5188, 33226,  ..., 17568, 20020, 26384]),
+       values=tensor([0.5580, 0.6578, 0.5141,  ..., 0.8482, 0.1339, 0.2046]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.2505, 0.9140, 0.1873,  ..., 0.2385, 0.4644, 0.1302])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.50059986114502 seconds
+
+[18.33, 18.14, 21.59, 19.0, 18.11, 18.16, 17.98, 18.16, 18.03, 18.31]
+[47.63]
+14.308748722076416
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 9147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.50059986114502, 'TIME_S_1KI': 1.1479829300475588, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 681.5257016324997, 'W': 47.63}
+[18.33, 18.14, 21.59, 19.0, 18.11, 18.16, 17.98, 18.16, 18.03, 18.31, 18.43, 17.74, 17.82, 18.07, 18.59, 18.18, 17.93, 18.01, 17.93, 17.85]
+329.90000000000003
+16.495
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 9147, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.50059986114502, 'TIME_S_1KI': 1.1479829300475588, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 681.5257016324997, 'W': 47.63, 'J_1KI': 74.50811212774677, 'W_1KI': 5.207171750300645, 'W_D': 31.135, 'J_D': 445.50289146184923, 'W_D_1KI': 3.4038482562588825, 'J_D_1KI': 0.37212728285327235}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..db8a72e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 1997, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.44909930229187, "TIME_S_1KI": 5.232398248518714, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 733.6282246422767, "W": 48.73, "J_1KI": 367.3651600612302, "W_1KI": 24.40160240360541, "W_D": 32.179249999999996, "J_D": 484.4573373244404, "W_D_1KI": 16.113795693540307, "J_D_1KI": 8.069001348793345}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..a863f3a
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.5546464920043945}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      55,     117,  ..., 2499903,
+                            2499953, 2500000]),
+       col_indices=tensor([  566,  1603,  2858,  ..., 47622, 48780, 49985]),
+       values=tensor([0.9915, 0.7849, 0.7900,  ..., 0.9170, 0.4625, 0.4875]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6866, 0.0421, 0.1446,  ..., 0.6566, 0.6603, 0.7026])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.5546464920043945 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1893', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.948294878005981}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      33,      68,  ..., 2499891,
+                            2499938, 2500000]),
+       col_indices=tensor([ 3534,  3824,  4376,  ..., 49368, 49484, 49571]),
+       values=tensor([0.2824, 0.5783, 0.2215,  ..., 0.8826, 0.1249, 0.2741]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5004, 0.8496, 0.6985,  ..., 0.2602, 0.0299, 0.5346])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 9.948294878005981 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1997', '-ss', '50000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.44909930229187}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      60,     107,  ..., 2499904,
+                            2499948, 2500000]),
+       col_indices=tensor([ 1519,  4331,  6515,  ..., 42103, 42230, 49135]),
+       values=tensor([0.6099, 0.9393, 0.8647,  ..., 0.8575, 0.6331, 0.4704]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1131, 0.9150, 0.5556,  ..., 0.6033, 0.7715, 0.6124])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.44909930229187 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      60,     107,  ..., 2499904,
+                            2499948, 2500000]),
+       col_indices=tensor([ 1519,  4331,  6515,  ..., 42103, 42230, 49135]),
+       values=tensor([0.6099, 0.9393, 0.8647,  ..., 0.8575, 0.6331, 0.4704]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1131, 0.9150, 0.5556,  ..., 0.6033, 0.7715, 0.6124])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.44909930229187 seconds
+
+[18.64, 18.18, 18.44, 22.12, 18.9, 18.07, 17.86, 17.86, 18.53, 18.0]
+[48.73]
+15.054960489273071
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1997, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.44909930229187, 'TIME_S_1KI': 5.232398248518714, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 733.6282246422767, 'W': 48.73}
+[18.64, 18.18, 18.44, 22.12, 18.9, 18.07, 17.86, 17.86, 18.53, 18.0, 18.34, 18.09, 18.08, 17.94, 18.14, 18.44, 18.04, 17.91, 17.9, 18.05]
+331.015
+16.55075
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 1997, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.44909930229187, 'TIME_S_1KI': 5.232398248518714, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 733.6282246422767, 'W': 48.73, 'J_1KI': 367.3651600612302, 'W_1KI': 24.40160240360541, 'W_D': 32.179249999999996, 'J_D': 484.4573373244404, 'W_D_1KI': 16.113795693540307, 'J_D_1KI': 8.069001348793345}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..981b4ab
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 134, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.514585256576538, "TIME_S_1KI": 78.46705415355626, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2494.187549471855, "W": 43.5, "J_1KI": 18613.339921431754, "W_1KI": 324.6268656716418, "W_D": 26.962, "J_D": 1545.9375795140265, "W_D_1KI": 201.2089552238806, "J_D_1KI": 1501.5593673423925}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..7744b5c
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.827495813369751}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      509,     1029,  ..., 24999021,
+                            24999525, 25000000]),
+       col_indices=tensor([   77,   168,   174,  ..., 49716, 49743, 49917]),
+       values=tensor([0.0871, 0.3865, 0.3717,  ..., 0.4376, 0.0483, 0.0994]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.1988, 0.8388, 0.2584,  ..., 0.5965, 0.5005, 0.7795])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 7.827495813369751 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '134', '-ss', '50000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.514585256576538}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      525,     1042,  ..., 24999041,
+                            24999512, 25000000]),
+       col_indices=tensor([  163,   320,   387,  ..., 49821, 49828, 49920]),
+       values=tensor([0.8765, 0.3303, 0.5777,  ..., 0.2129, 0.9852, 0.0873]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.2121, 0.5034, 0.7106,  ..., 0.6677, 0.7232, 0.0645])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.514585256576538 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      525,     1042,  ..., 24999041,
+                            24999512, 25000000]),
+       col_indices=tensor([  163,   320,   387,  ..., 49821, 49828, 49920]),
+       values=tensor([0.8765, 0.3303, 0.5777,  ..., 0.2129, 0.9852, 0.0873]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.2121, 0.5034, 0.7106,  ..., 0.6677, 0.7232, 0.0645])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.514585256576538 seconds
+
+[18.5, 18.14, 19.17, 17.93, 18.04, 17.95, 18.11, 18.93, 18.07, 18.37]
+[43.5]
+57.337644815444946
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 134, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.514585256576538, 'TIME_S_1KI': 78.46705415355626, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2494.187549471855, 'W': 43.5}
+[18.5, 18.14, 19.17, 17.93, 18.04, 17.95, 18.11, 18.93, 18.07, 18.37, 22.44, 18.33, 18.23, 18.16, 18.38, 17.89, 18.61, 17.96, 18.23, 17.95]
+330.76
+16.538
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 134, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.514585256576538, 'TIME_S_1KI': 78.46705415355626, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2494.187549471855, 'W': 43.5, 'J_1KI': 18613.339921431754, 'W_1KI': 324.6268656716418, 'W_D': 26.962, 'J_D': 1545.9375795140265, 'W_D_1KI': 201.2089552238806, 'J_D_1KI': 1501.5593673423925}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..01c9d2b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 22242, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.517975091934204, "TIME_S_1KI": 0.472888008809199, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 668.1220734119415, "W": 47.0, "J_1KI": 30.03875880819807, "W_1KI": 2.113119323801816, "W_D": 30.704, "J_D": 436.4685136604309, "W_D_1KI": 1.3804513982555526, "J_D_1KI": 0.062065075004745646}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..f23093c
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.06578350067138672}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([16918,  1143,  1724,  ..., 48553, 41363, 39308]),
+       values=tensor([0.9238, 0.1195, 0.2813,  ..., 0.9276, 0.6113, 0.0798]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0373, 0.8428, 0.6841,  ..., 0.3333, 0.7324, 0.6824])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.06578350067138672 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '15961', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 7.534844875335693}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([29190, 43986, 25006,  ..., 44362, 15421,  8070]),
+       values=tensor([0.3395, 0.8970, 0.1159,  ..., 0.8275, 0.4942, 0.3559]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7532, 0.7737, 0.3401,  ..., 0.4031, 0.1788, 0.5939])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 7.534844875335693 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22242', '-ss', '50000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.517975091934204}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([22981, 23025, 47875,  ..., 28752, 43497,  8642]),
+       values=tensor([0.4359, 0.4232, 0.6349,  ..., 0.3036, 0.5759, 0.5327]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8882, 0.3621, 0.9798,  ..., 0.1733, 0.2748, 0.2728])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.517975091934204 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([22981, 23025, 47875,  ..., 28752, 43497,  8642]),
+       values=tensor([0.4359, 0.4232, 0.6349,  ..., 0.3036, 0.5759, 0.5327]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8882, 0.3621, 0.9798,  ..., 0.1733, 0.2748, 0.2728])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.517975091934204 seconds
+
+[18.31, 17.89, 18.04, 17.91, 18.19, 18.28, 17.95, 17.98, 18.92, 18.1]
+[47.0]
+14.215363264083862
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 22242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.517975091934204, 'TIME_S_1KI': 0.472888008809199, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 668.1220734119415, 'W': 47.0}
+[18.31, 17.89, 18.04, 17.91, 18.19, 18.28, 17.95, 17.98, 18.92, 18.1, 18.24, 18.64, 18.01, 17.83, 18.02, 18.06, 18.04, 17.88, 17.99, 17.93]
+325.92
+16.296
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 22242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.517975091934204, 'TIME_S_1KI': 0.472888008809199, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 668.1220734119415, 'W': 47.0, 'J_1KI': 30.03875880819807, 'W_1KI': 2.113119323801816, 'W_D': 30.704, 'J_D': 436.4685136604309, 'W_D_1KI': 1.3804513982555526, 'J_D_1KI': 0.062065075004745646}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..7de68b9
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 11256, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.481152534484863, "TIME_S_1KI": 0.9311613836607022, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 671.3460792446136, "W": 47.13, "J_1KI": 59.64339723210853, "W_1KI": 4.187100213219616, "W_D": 30.78675, "J_D": 438.54368566060066, "W_D_1KI": 2.735141257995736, "J_D_1KI": 0.24299407053977753}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..d1f10c6
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.1096796989440918}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      2,  ..., 124996, 124997,
+                            125000]),
+       col_indices=tensor([38708, 28625, 11454,  ...,   884, 22723, 30800]),
+       values=tensor([0.0038, 0.3289, 0.1581,  ..., 0.9719, 0.8303, 0.9998]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.0207, 0.8237, 0.8176,  ..., 0.3561, 0.4550, 0.3366])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.1096796989440918 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9573', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 8.929835081100464}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      4,  ..., 124997, 124998,
+                            125000]),
+       col_indices=tensor([ 5176, 42593, 37500,  ...,  3219, 12793, 38658]),
+       values=tensor([0.6803, 0.7664, 0.9859,  ..., 0.5422, 0.9603, 0.0980]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.2299, 0.8541, 0.6200,  ..., 0.4981, 0.6521, 0.8502])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 8.929835081100464 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '11256', '-ss', '50000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.481152534484863}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 124996, 124999,
+                            125000]),
+       col_indices=tensor([ 3517, 32781, 39284,  ..., 16837, 28625, 12663]),
+       values=tensor([0.4051, 0.3118, 0.0683,  ..., 0.4752, 0.1421, 0.6822]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.2287, 0.1097, 0.9835,  ..., 0.8729, 0.0701, 0.5217])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.481152534484863 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 124996, 124999,
+                            125000]),
+       col_indices=tensor([ 3517, 32781, 39284,  ..., 16837, 28625, 12663]),
+       values=tensor([0.4051, 0.3118, 0.0683,  ..., 0.4752, 0.1421, 0.6822]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.2287, 0.1097, 0.9835,  ..., 0.8729, 0.0701, 0.5217])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.481152534484863 seconds
+
+[18.46, 17.73, 18.38, 18.1, 18.29, 18.18, 18.04, 18.01, 18.16, 17.9]
+[47.13]
+14.244559288024902
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 11256, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.481152534484863, 'TIME_S_1KI': 0.9311613836607022, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 671.3460792446136, 'W': 47.13}
+[18.46, 17.73, 18.38, 18.1, 18.29, 18.18, 18.04, 18.01, 18.16, 17.9, 18.94, 17.91, 17.96, 17.99, 18.41, 18.07, 18.2, 17.95, 18.46, 18.75]
+326.865
+16.34325
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 11256, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.481152534484863, 'TIME_S_1KI': 0.9311613836607022, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 671.3460792446136, 'W': 47.13, 'J_1KI': 59.64339723210853, 'W_1KI': 4.187100213219616, 'W_D': 30.78675, 'J_D': 438.54368566060066, 'W_D_1KI': 2.735141257995736, 'J_D_1KI': 0.24299407053977753}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..b6b292e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 220904, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.611992359161377, "TIME_S_1KI": 0.04803893256419701, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 660.6772035217285, "W": 46.78, "J_1KI": 2.990788774860249, "W_1KI": 0.211766197081085, "W_D": 30.2935, "J_D": 427.83721387100223, "W_D_1KI": 0.13713423025386592, "J_D_1KI": 0.0006207865419090009}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..bcbbe28
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.021222829818725586}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([2865, 4172, 3505,  ..., 1471, 1829, 2284]),
+       values=tensor([0.9472, 0.7106, 0.6508,  ..., 0.6327, 0.4564, 0.0632]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.0811, 0.3767, 0.7595,  ..., 0.7571, 0.6856, 0.3676])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.021222829818725586 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '49475', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.351640462875366}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([1365, 3411,  751,  ..., 1715, 4182, 3544]),
+       values=tensor([0.2168, 0.4073, 0.8209,  ..., 0.1504, 0.5765, 0.8829]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.3766, 0.6993, 0.2098,  ..., 0.7754, 0.7068, 0.6832])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 2.351640462875366 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '220904', '-ss', '5000', '-sd', '0.0001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.611992359161377}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([ 469, 3066, 4238,  ..., 2570, 4418, 4413]),
+       values=tensor([0.6778, 0.7938, 0.7053,  ..., 0.2703, 0.2957, 0.5133]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.1744, 0.7493, 0.4982,  ..., 0.1073, 0.6650, 0.9357])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.611992359161377 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([ 469, 3066, 4238,  ..., 2570, 4418, 4413]),
+       values=tensor([0.6778, 0.7938, 0.7053,  ..., 0.2703, 0.2957, 0.5133]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.1744, 0.7493, 0.4982,  ..., 0.1073, 0.6650, 0.9357])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.611992359161377 seconds
+
+[18.28, 17.78, 18.16, 18.31, 18.21, 17.89, 17.98, 18.38, 18.01, 17.96]
+[46.78]
+14.123069763183594
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 220904, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.611992359161377, 'TIME_S_1KI': 0.04803893256419701, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 660.6772035217285, 'W': 46.78}
+[18.28, 17.78, 18.16, 18.31, 18.21, 17.89, 17.98, 18.38, 18.01, 17.96, 18.63, 18.15, 17.86, 18.23, 18.11, 18.07, 18.06, 17.82, 21.74, 19.07]
+329.73
+16.4865
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 220904, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.611992359161377, 'TIME_S_1KI': 0.04803893256419701, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 660.6772035217285, 'W': 46.78, 'J_1KI': 2.990788774860249, 'W_1KI': 0.211766197081085, 'W_D': 30.2935, 'J_D': 427.83721387100223, 'W_D_1KI': 0.13713423025386592, 'J_D_1KI': 0.0006207865419090009}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..96eeba5
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 111257, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.7375807762146, "TIME_S_1KI": 0.09651150737674573, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 673.9428442955017, "W": 47.23, "J_1KI": 6.057532059065961, "W_1KI": 0.42451261493658826, "W_D": 30.839999999999996, "J_D": 440.0676967620849, "W_D_1KI": 0.2771960415973826, "J_D_1KI": 0.0024914930440096588}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..1589984
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.025800704956054688}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,    11,  ..., 24988, 24993, 25000]),
+       col_indices=tensor([  36,  564, 3279,  ..., 4511, 4767, 4922]),
+       values=tensor([0.2797, 0.2996, 0.9239,  ..., 0.1899, 0.3417, 0.3512]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3254, 0.3602, 0.2662,  ..., 0.3074, 0.8226, 0.4658])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.025800704956054688 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '40696', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.8407018184661865}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     7,    11,  ..., 24987, 24992, 25000]),
+       col_indices=tensor([ 298,  713, 1200,  ..., 1957, 3799, 4153]),
+       values=tensor([0.8486, 0.9770, 0.8154,  ..., 0.4467, 0.7513, 0.9966]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8767, 0.4273, 0.1763,  ..., 0.9403, 0.3580, 0.5902])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 3.8407018184661865 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '111257', '-ss', '5000', '-sd', '0.001', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.7375807762146}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     3,  ..., 24991, 24996, 25000]),
+       col_indices=tensor([ 575, 1907, 4405,  ..., 1224, 3086, 3740]),
+       values=tensor([0.1597, 0.6483, 0.2533,  ..., 0.7760, 0.1307, 0.6720]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0745, 0.7131, 0.6004,  ..., 0.2535, 0.5073, 0.4932])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.7375807762146 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     3,  ..., 24991, 24996, 25000]),
+       col_indices=tensor([ 575, 1907, 4405,  ..., 1224, 3086, 3740]),
+       values=tensor([0.1597, 0.6483, 0.2533,  ..., 0.7760, 0.1307, 0.6720]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0745, 0.7131, 0.6004,  ..., 0.2535, 0.5073, 0.4932])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.7375807762146 seconds
+
+[18.26, 17.96, 17.92, 18.07, 18.39, 18.16, 18.05, 18.66, 19.34, 18.38]
+[47.23]
+14.269380569458008
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 111257, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.7375807762146, 'TIME_S_1KI': 0.09651150737674573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 673.9428442955017, 'W': 47.23}
+[18.26, 17.96, 17.92, 18.07, 18.39, 18.16, 18.05, 18.66, 19.34, 18.38, 18.78, 18.12, 17.96, 17.92, 17.93, 18.7, 18.05, 17.87, 17.97, 18.04]
+327.8
+16.39
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 111257, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.7375807762146, 'TIME_S_1KI': 0.09651150737674573, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 673.9428442955017, 'W': 47.23, 'J_1KI': 6.057532059065961, 'W_1KI': 0.42451261493658826, 'W_D': 30.839999999999996, 'J_D': 440.0676967620849, 'W_D_1KI': 0.2771960415973826, 'J_D_1KI': 0.0024914930440096588}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..a469b78
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 21150, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.43382978439331, "TIME_S_1KI": 0.4933252853141046, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 678.8009396362305, "W": 47.74, "J_1KI": 32.09460707499908, "W_1KI": 2.257210401891253, "W_D": 31.383250000000004, "J_D": 446.22914932632455, "W_D_1KI": 1.483841607565012, "J_D_1KI": 0.0701579956295514}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..c2c0006
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.06486701965332031}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     47,     88,  ..., 249915, 249957,
+                            250000]),
+       col_indices=tensor([  72,  118,  180,  ..., 4779, 4849, 4984]),
+       values=tensor([0.8923, 0.3860, 0.0290,  ..., 0.0532, 0.0516, 0.8464]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.1487, 0.9450, 0.3254,  ..., 0.6866, 0.3989, 0.7268])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.06486701965332031 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '16186', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 8.035360336303711}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     44,     82,  ..., 249908, 249962,
+                            250000]),
+       col_indices=tensor([  36,   43,   78,  ..., 4796, 4867, 4932]),
+       values=tensor([0.3758, 0.9832, 0.1983,  ..., 0.0743, 0.8633, 0.0592]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5985, 0.2492, 0.1240,  ..., 0.8930, 0.7764, 0.3200])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 8.035360336303711 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '21150', '-ss', '5000', '-sd', '0.01', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.43382978439331}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     47,     95,  ..., 249894, 249948,
+                            250000]),
+       col_indices=tensor([ 129,  143,  228,  ..., 4613, 4768, 4965]),
+       values=tensor([0.2601, 0.0327, 0.3118,  ..., 0.8257, 0.2689, 0.3965]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3768, 0.4725, 0.8369,  ..., 0.3357, 0.4139, 0.5546])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.43382978439331 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     47,     95,  ..., 249894, 249948,
+                            250000]),
+       col_indices=tensor([ 129,  143,  228,  ..., 4613, 4768, 4965]),
+       values=tensor([0.2601, 0.0327, 0.3118,  ..., 0.8257, 0.2689, 0.3965]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3768, 0.4725, 0.8369,  ..., 0.3357, 0.4139, 0.5546])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.43382978439331 seconds
+
+[18.56, 17.95, 18.07, 18.36, 18.1, 18.2, 18.17, 18.16, 18.01, 17.83]
+[47.74]
+14.218704223632812
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21150, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.43382978439331, 'TIME_S_1KI': 0.4933252853141046, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 678.8009396362305, 'W': 47.74}
+[18.56, 17.95, 18.07, 18.36, 18.1, 18.2, 18.17, 18.16, 18.01, 17.83, 19.27, 18.21, 17.98, 17.93, 18.07, 18.27, 18.29, 17.78, 18.3, 18.91]
+327.135
+16.356749999999998
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 21150, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.43382978439331, 'TIME_S_1KI': 0.4933252853141046, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 678.8009396362305, 'W': 47.74, 'J_1KI': 32.09460707499908, 'W_1KI': 2.257210401891253, 'W_D': 31.383250000000004, 'J_D': 446.22914932632455, 'W_D_1KI': 1.483841607565012, 'J_D_1KI': 0.0701579956295514}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..c9a64dd
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 4516, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.427689790725708, "TIME_S_1KI": 2.309054426644311, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 694.9637099742889, "W": 47.9, "J_1KI": 153.88921832911623, "W_1KI": 10.606731620903455, "W_D": 31.338749999999997, "J_D": 454.682546262145, "W_D_1KI": 6.939492914083259, "J_D_1KI": 1.5366459065729094}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..ce3acc5
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.2486708164215088}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     247,     481,  ..., 1249493,
+                            1249753, 1250000]),
+       col_indices=tensor([   6,   31,   64,  ..., 4955, 4959, 4978]),
+       values=tensor([0.8259, 0.7056, 0.5562,  ..., 0.4513, 0.5248, 0.2272]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.7144, 0.3655, 0.7208,  ..., 0.9456, 0.6678, 0.5049])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 0.2486708164215088 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4222', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.815936088562012}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     269,     519,  ..., 1249537,
+                            1249769, 1250000]),
+       col_indices=tensor([  42,   49,   74,  ..., 4955, 4973, 4990]),
+       values=tensor([0.0021, 0.9721, 0.1598,  ..., 0.6170, 0.8086, 0.1248]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.9473, 0.7157, 0.7204,  ..., 0.2702, 0.4361, 0.8753])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 9.815936088562012 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '4516', '-ss', '5000', '-sd', '0.05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.427689790725708}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     262,     521,  ..., 1249547,
+                            1249780, 1250000]),
+       col_indices=tensor([   5,   16,   32,  ..., 4965, 4966, 4994]),
+       values=tensor([0.6294, 0.1213, 0.8577,  ..., 0.8057, 0.3565, 0.7731]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.5561, 0.2020, 0.8277,  ..., 0.0800, 0.4571, 0.4718])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.427689790725708 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     262,     521,  ..., 1249547,
+                            1249780, 1250000]),
+       col_indices=tensor([   5,   16,   32,  ..., 4965, 4966, 4994]),
+       values=tensor([0.6294, 0.1213, 0.8577,  ..., 0.8057, 0.3565, 0.7731]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.5561, 0.2020, 0.8277,  ..., 0.0800, 0.4571, 0.4718])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.427689790725708 seconds
+
+[18.38, 21.41, 18.53, 17.98, 18.13, 17.94, 18.18, 18.19, 18.64, 17.87]
+[47.9]
+14.508636951446533
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4516, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.427689790725708, 'TIME_S_1KI': 2.309054426644311, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 694.9637099742889, 'W': 47.9}
+[18.38, 21.41, 18.53, 17.98, 18.13, 17.94, 18.18, 18.19, 18.64, 17.87, 18.41, 18.3, 18.02, 18.19, 18.18, 18.28, 18.35, 17.94, 18.16, 18.95]
+331.225
+16.56125
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 4516, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.427689790725708, 'TIME_S_1KI': 2.309054426644311, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 694.9637099742889, 'W': 47.9, 'J_1KI': 153.88921832911623, 'W_1KI': 10.606731620903455, 'W_D': 31.338749999999997, 'J_D': 454.682546262145, 'W_D_1KI': 6.939492914083259, 'J_D_1KI': 1.5366459065729094}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..6d226de
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 2077, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.487555503845215, "TIME_S_1KI": 5.04937674715706, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 724.931280415058, "W": 48.23, "J_1KI": 349.0280599013279, "W_1KI": 23.220991815117955, "W_D": 31.779249999999998, "J_D": 477.6647811140418, "W_D_1KI": 15.300553683196917, "J_D_1KI": 7.366660415597938}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..ec991c2
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.53643798828125}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     485,     953,  ..., 2499027,
+                            2499524, 2500000]),
+       col_indices=tensor([   4,    6,   18,  ..., 4982, 4984, 4999]),
+       values=tensor([0.6950, 0.7335, 0.8547,  ..., 0.7303, 0.2740, 0.2643]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4725, 0.9871, 0.6689,  ..., 0.5705, 0.1526, 0.5563])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 0.53643798828125 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1957', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.889901638031006}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     453,     921,  ..., 2498978,
+                            2499478, 2500000]),
+       col_indices=tensor([   4,   20,   34,  ..., 4974, 4986, 4991]),
+       values=tensor([0.7939, 0.2865, 0.3388,  ..., 0.3715, 0.9532, 0.1224]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8979, 0.8998, 0.6031,  ..., 0.0686, 0.1119, 0.6753])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 9.889901638031006 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2077', '-ss', '5000', '-sd', '0.1', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.487555503845215}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     534,    1011,  ..., 2499001,
+                            2499492, 2500000]),
+       col_indices=tensor([  12,   16,   26,  ..., 4984, 4992, 4995]),
+       values=tensor([0.3129, 0.5758, 0.2112,  ..., 0.6208, 0.5668, 0.8482]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7114, 0.1437, 0.5452,  ..., 0.6795, 0.1114, 0.8178])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.487555503845215 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     534,    1011,  ..., 2499001,
+                            2499492, 2500000]),
+       col_indices=tensor([  12,   16,   26,  ..., 4984, 4992, 4995]),
+       values=tensor([0.3129, 0.5758, 0.2112,  ..., 0.6208, 0.5668, 0.8482]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7114, 0.1437, 0.5452,  ..., 0.6795, 0.1114, 0.8178])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.487555503845215 seconds
+
+[18.45, 17.83, 21.85, 18.4, 18.31, 17.94, 17.97, 18.32, 18.18, 17.83]
+[48.23]
+15.030712842941284
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.487555503845215, 'TIME_S_1KI': 5.04937674715706, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 724.931280415058, 'W': 48.23}
+[18.45, 17.83, 21.85, 18.4, 18.31, 17.94, 17.97, 18.32, 18.18, 17.83, 18.49, 17.82, 18.18, 18.17, 18.15, 17.82, 18.07, 17.68, 17.99, 17.9]
+329.015
+16.45075
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 2077, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.487555503845215, 'TIME_S_1KI': 5.04937674715706, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 724.931280415058, 'W': 48.23, 'J_1KI': 349.0280599013279, 'W_1KI': 23.220991815117955, 'W_D': 31.779249999999998, 'J_D': 477.6647811140418, 'W_D_1KI': 15.300553683196917, 'J_D_1KI': 7.366660415597938}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..e6d6ed2
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 962, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.386102437973022, "TIME_S_1KI": 10.796364280637237, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 760.2223528313637, "W": 48.37, "J_1KI": 790.2519260201285, "W_1KI": 50.280665280665275, "W_D": 31.813249999999996, "J_D": 500.00297221857306, "W_D_1KI": 33.069906444906444, "J_D_1KI": 34.376202125682376}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..c8b98a6
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 1.090480089187622}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     979,    1989,  ..., 4997947,
+                            4998978, 5000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4989, 4991, 4992]),
+       values=tensor([0.4629, 0.8349, 0.1230,  ..., 0.3254, 0.2010, 0.4262]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.8820, 0.9283, 0.2134,  ..., 0.8569, 0.5183, 0.3465])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 1.090480089187622 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '962', '-ss', '5000', '-sd', '0.2', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.386102437973022}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1020,    2003,  ..., 4997983,
+                            4998972, 5000000]),
+       col_indices=tensor([   2,   12,   14,  ..., 4982, 4988, 4994]),
+       values=tensor([0.5268, 0.0953, 0.2601,  ..., 0.2366, 0.8226, 0.2641]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6710, 0.0398, 0.1150,  ..., 0.1943, 0.5070, 0.9802])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.386102437973022 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1020,    2003,  ..., 4997983,
+                            4998972, 5000000]),
+       col_indices=tensor([   2,   12,   14,  ..., 4982, 4988, 4994]),
+       values=tensor([0.5268, 0.0953, 0.2601,  ..., 0.2366, 0.8226, 0.2641]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6710, 0.0398, 0.1150,  ..., 0.1943, 0.5070, 0.9802])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.386102437973022 seconds
+
+[18.34, 18.12, 18.35, 17.91, 17.99, 18.03, 18.28, 18.28, 17.95, 18.09]
+[48.37]
+15.71681523323059
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 962, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.386102437973022, 'TIME_S_1KI': 10.796364280637237, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 760.2223528313637, 'W': 48.37}
+[18.34, 18.12, 18.35, 17.91, 17.99, 18.03, 18.28, 18.28, 17.95, 18.09, 18.27, 18.18, 18.12, 17.89, 18.15, 21.61, 18.11, 17.8, 18.1, 21.83]
+331.13500000000005
+16.55675
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 962, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.386102437973022, 'TIME_S_1KI': 10.796364280637237, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 760.2223528313637, 'W': 48.37, 'J_1KI': 790.2519260201285, 'W_1KI': 50.280665280665275, 'W_D': 31.813249999999996, 'J_D': 500.00297221857306, 'W_D_1KI': 33.069906444906444, 'J_D_1KI': 34.376202125682376}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..6efa366
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 640, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.40328574180603, "TIME_S_1KI": 16.255133971571922, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 792.4070657157898, "W": 48.54, "J_1KI": 1238.1360401809216, "W_1KI": 75.84375, "W_D": 32.294250000000005, "J_D": 527.1980198185445, "W_D_1KI": 50.45976562500001, "J_D_1KI": 78.84338378906251}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..e11bc1c
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.6401786804199219}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1479,    2972,  ..., 7496976,
+                            7498517, 7500000]),
+       col_indices=tensor([   4,   10,   12,  ..., 4987, 4989, 4997]),
+       values=tensor([0.8092, 0.2326, 0.1918,  ..., 0.7537, 0.2703, 0.9406]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.6079, 0.4244, 0.8803,  ..., 0.9929, 0.8834, 0.4182])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 1.6401786804199219 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '640', '-ss', '5000', '-sd', '0.3', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.40328574180603}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1469,    2947,  ..., 7496962,
+                            7498492, 7500000]),
+       col_indices=tensor([   5,    8,   10,  ..., 4979, 4981, 4995]),
+       values=tensor([0.5097, 0.4133, 0.1946,  ..., 0.6762, 0.7827, 0.5941]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.3256, 0.8526, 0.3288,  ..., 0.0837, 0.1622, 0.2040])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.40328574180603 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1469,    2947,  ..., 7496962,
+                            7498492, 7500000]),
+       col_indices=tensor([   5,    8,   10,  ..., 4979, 4981, 4995]),
+       values=tensor([0.5097, 0.4133, 0.1946,  ..., 0.6762, 0.7827, 0.5941]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.3256, 0.8526, 0.3288,  ..., 0.0837, 0.1622, 0.2040])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.40328574180603 seconds
+
+[18.61, 18.25, 18.17, 17.83, 17.98, 17.89, 17.97, 17.91, 17.89, 18.07]
+[48.54]
+16.32482624053955
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 640, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.40328574180603, 'TIME_S_1KI': 16.255133971571922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 792.4070657157898, 'W': 48.54}
+[18.61, 18.25, 18.17, 17.83, 17.98, 17.89, 17.97, 17.91, 17.89, 18.07, 18.46, 18.32, 17.92, 18.02, 17.98, 18.33, 17.98, 17.9, 18.02, 17.97]
+324.91499999999996
+16.245749999999997
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 640, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.40328574180603, 'TIME_S_1KI': 16.255133971571922, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 792.4070657157898, 'W': 48.54, 'J_1KI': 1238.1360401809216, 'W_1KI': 75.84375, 'W_D': 32.294250000000005, 'J_D': 527.1980198185445, 'W_D_1KI': 50.45976562500001, 'J_D_1KI': 78.84338378906251}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..679e37e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 393, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.361774921417236, "TIME_S_1KI": 26.365839494700346, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 830.257935385704, "W": 47.87, "J_1KI": 2112.615611668458, "W_1KI": 121.80661577608141, "W_D": 31.368499999999997, "J_D": 544.0556934645175, "W_D_1KI": 79.81806615776081, "J_D_1KI": 203.09940498157965}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..4eb279b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 2.6700339317321777}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1981,     4013,  ...,  9996053,
+                             9998014, 10000000]),
+       col_indices=tensor([   1,    2,    4,  ..., 4993, 4995, 4997]),
+       values=tensor([0.1269, 0.6137, 0.4927,  ..., 0.6127, 0.1027, 0.1107]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1274, 0.2875, 0.5158,  ..., 0.6638, 0.6368, 0.8182])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 2.6700339317321777 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '393', '-ss', '5000', '-sd', '0.4', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.361774921417236}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2035,     3994,  ...,  9995956,
+                             9997978, 10000000]),
+       col_indices=tensor([   2,    3,    4,  ..., 4989, 4993, 4999]),
+       values=tensor([0.0874, 0.4595, 0.0218,  ..., 0.9380, 0.2756, 0.2464]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1585, 0.7775, 0.6260,  ..., 0.0357, 0.4122, 0.0843])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.361774921417236 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2035,     3994,  ...,  9995956,
+                             9997978, 10000000]),
+       col_indices=tensor([   2,    3,    4,  ..., 4989, 4993, 4999]),
+       values=tensor([0.0874, 0.4595, 0.0218,  ..., 0.9380, 0.2756, 0.2464]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1585, 0.7775, 0.6260,  ..., 0.0357, 0.4122, 0.0843])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.361774921417236 seconds
+
+[18.09, 21.49, 19.13, 18.18, 17.87, 18.12, 18.49, 17.91, 18.18, 17.86]
+[47.87]
+17.344013690948486
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 393, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.361774921417236, 'TIME_S_1KI': 26.365839494700346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 830.257935385704, 'W': 47.87}
+[18.09, 21.49, 19.13, 18.18, 17.87, 18.12, 18.49, 17.91, 18.18, 17.86, 18.63, 18.02, 17.95, 17.88, 17.98, 18.08, 17.96, 17.78, 18.35, 18.74]
+330.03
+16.5015
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 393, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.361774921417236, 'TIME_S_1KI': 26.365839494700346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 830.257935385704, 'W': 47.87, 'J_1KI': 2112.615611668458, 'W_1KI': 121.80661577608141, 'W_D': 31.368499999999997, 'J_D': 544.0556934645175, 'W_D_1KI': 79.81806615776081, 'J_D_1KI': 203.09940498157965}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..afa179f
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 307, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.394981145858765, "TIME_S_1KI": 33.85987343927936, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 930.0089691495895, "W": 47.58, "J_1KI": 3029.3451763830276, "W_1KI": 154.98371335504885, "W_D": 31.056999999999995, "J_D": 607.0468380596636, "W_D_1KI": 101.16286644951138, "J_D_1KI": 329.5207376205583}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..91aa70e
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 3.415400505065918}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2477,     4964,  ..., 12494964,
+                            12497482, 12500000]),
+       col_indices=tensor([   1,    2,    4,  ..., 4993, 4994, 4997]),
+       values=tensor([0.5791, 0.4301, 0.3570,  ..., 0.1858, 0.4639, 0.9573]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0093, 0.1244, 0.8882,  ..., 0.7606, 0.5225, 0.2163])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 3.415400505065918 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '307', '-ss', '5000', '-sd', '0.5', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.394981145858765}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2499,     4994,  ..., 12495082,
+                            12497507, 12500000]),
+       col_indices=tensor([   0,    1,    6,  ..., 4995, 4997, 4999]),
+       values=tensor([0.6585, 0.9778, 0.9803,  ..., 0.8325, 0.0849, 0.1040]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.3486, 0.4233, 0.3644,  ..., 0.4149, 0.2376, 0.4812])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.394981145858765 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2499,     4994,  ..., 12495082,
+                            12497507, 12500000]),
+       col_indices=tensor([   0,    1,    6,  ..., 4995, 4997, 4999]),
+       values=tensor([0.6585, 0.9778, 0.9803,  ..., 0.8325, 0.0849, 0.1040]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.3486, 0.4233, 0.3644,  ..., 0.4149, 0.2376, 0.4812])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.394981145858765 seconds
+
+[18.48, 21.56, 18.9, 18.32, 18.22, 17.83, 18.09, 17.98, 18.55, 18.34]
+[47.58]
+19.546216249465942
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 307, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.394981145858765, 'TIME_S_1KI': 33.85987343927936, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 930.0089691495895, 'W': 47.58}
+[18.48, 21.56, 18.9, 18.32, 18.22, 17.83, 18.09, 17.98, 18.55, 18.34, 18.35, 17.93, 17.78, 17.82, 18.22, 18.23, 18.5, 17.76, 18.34, 17.69]
+330.46000000000004
+16.523000000000003
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 307, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.394981145858765, 'TIME_S_1KI': 33.85987343927936, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 930.0089691495895, 'W': 47.58, 'J_1KI': 3029.3451763830276, 'W_1KI': 154.98371335504885, 'W_D': 31.056999999999995, 'J_D': 607.0468380596636, 'W_D_1KI': 101.16286644951138, 'J_D_1KI': 329.5207376205583}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..ca05b98
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 355542, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.588425874710083, "TIME_S_1KI": 0.02978108317641821, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 655.0935684347153, "W": 46.57, "J_1KI": 1.8425209073322286, "W_1KI": 0.13098311873140162, "W_D": 30.1475, "J_D": 424.08059597134593, "W_D_1KI": 0.08479307648604104, "J_D_1KI": 0.00023848962003375422}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..a05082b
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,329 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.022435426712036133}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1292,  659, 4365, 3710, 1440, 2896,  123, 3649, 2612,
+                            927, 1659, 4214, 4385, 2636, 1869,  118, 1932, 2570,
+                           3752, 4154, 1992, 3194, 1579, 4685, 2991, 2783, 3487,
+                           3211, 2142, 3564, 4650, 3661, 3610, 3388, 1017, 3880,
+                           4125, 2970, 4559, 2499, 1693,   43, 2397, 1040, 4017,
+                           1828, 4674, 1496, 1775,  719, 2677,  389, 3154, 2997,
+                           3651,  773, 1883, 4184, 1034, 1972, 2681, 2277, 1527,
+                           4621,  238, 4365, 4905, 2103, 4738, 2026, 4073, 1797,
+                           3650, 3259, 4594, 2607, 1806, 3417, 2906,  213, 1348,
+                           1781, 4696, 1901,  709, 1550, 1968, 4490,  826, 4326,
+                            374, 3405, 4945,  642, 3326,  217,  437, 4704, 4681,
+                           3950, 1228, 3052, 3026, 3269, 1740, 2853, 2996, 1978,
+                            359, 3246, 3815, 3422, 1128,  733,  749, 4078, 2740,
+                            127, 2401,  240,  391, 4079,  575, 1175, 4439, 4510,
+                           2136, 1342,  880, 2183, 3085, 3808, 4189, 2436, 1877,
+                            140, 1033,  744,  134, 1457,  133,  407, 2079, 2372,
+                           2867, 1110,  602, 2915, 3299, 4776, 1097, 3465,  774,
+                           2845, 4963,  619, 3626, 3224, 3032, 4984, 3635, 1115,
+                           1431,  229,  362, 2520, 4880, 2306, 2092, 2949, 3111,
+                            141,  801, 4774, 1268, 4702, 3013, 4053, 4202, 2338,
+                           3307, 2339, 1627, 3991, 2211, 3208, 4859, 2254,  850,
+                           2555,  416, 3498, 2761, 1743, 3828, 3909, 4942, 4647,
+                           2857,  399, 2142, 1173, 2936, 2739, 3524, 2473, 2398,
+                           3617, 4358, 1503, 3513, 1560, 2497,  176, 1685,  851,
+                           2706, 2662, 1211,  466, 3647, 2835, 1798, 4560, 4189,
+                             74, 1919, 4892, 1659, 1504, 1873,  179, 4512, 1622,
+                            131,  802, 3776,  894,   98, 1072, 3715, 1448, 4255,
+                           4226,  676, 4655, 4974, 2293,  491, 1924]),
+       values=tensor([0.9106, 0.6103, 0.8018, 0.6726, 0.7831, 0.8787, 0.8641,
+                      0.0319, 0.4873, 0.6079, 0.6438, 0.5806, 0.1055, 0.2960,
+                      0.2595, 0.4592, 0.2559, 0.3932, 0.7042, 0.8694, 0.4660,
+                      0.4246, 0.4675, 0.7217, 0.9048, 0.6757, 0.7971, 0.3444,
+                      0.9040, 0.2589, 0.8383, 0.9787, 0.5364, 0.5478, 0.4280,
+                      0.9375, 0.9169, 0.6011, 0.6510, 0.3645, 0.9595, 0.3413,
+                      0.1561, 0.3706, 0.5420, 0.2194, 0.4928, 0.9365, 0.2372,
+                      0.4934, 0.8170, 0.4062, 0.4573, 0.8424, 0.2137, 0.2198,
+                      0.8285, 0.9490, 0.8645, 0.5816, 0.3427, 0.8902, 0.3651,
+                      0.7666, 0.8408, 0.8585, 0.8931, 0.5551, 0.8982, 0.6356,
+                      0.4250, 0.1088, 0.6737, 0.3958, 0.4828, 0.5186, 0.8805,
+                      0.2395, 0.2572, 0.2532, 0.6717, 0.2414, 0.7893, 0.8437,
+                      0.3171, 0.1858, 0.6604, 0.8284, 0.5385, 0.2314, 0.5114,
+                      0.2593, 0.8363, 0.9654, 0.7652, 0.9942, 0.9048, 0.6526,
+                      0.7743, 0.0670, 0.4879, 0.0500, 0.2026, 0.0553, 0.2990,
+                      0.2738, 0.8845, 0.6958, 0.2567, 0.3351, 0.1957, 0.2099,
+                      0.3337, 0.5048, 0.9817, 0.1630, 0.6715, 0.7671, 0.0645,
+                      0.2446, 0.2884, 0.8150, 0.9791, 0.9499, 0.4039, 0.4962,
+                      0.6049, 0.6707, 0.4315, 0.8269, 0.1062, 0.0634, 0.9597,
+                      0.8898, 0.1177, 0.9543, 0.8326, 0.6160, 0.9716, 0.8673,
+                      0.7943, 0.1918, 0.0735, 0.7498, 0.7051, 0.7537, 0.5409,
+                      0.9422, 0.7547, 0.3930, 0.7287, 0.3187, 0.8163, 0.1055,
+                      0.0953, 0.5157, 0.5484, 0.2625, 0.0877, 0.4823, 0.2711,
+                      0.4063, 0.7443, 0.2411, 0.7149, 0.2424, 0.1102, 0.7648,
+                      0.9164, 0.7435, 0.3343, 0.4014, 0.3868, 0.7585, 0.7825,
+                      0.9665, 0.6243, 0.8999, 0.5120, 0.3172, 0.9824, 0.0450,
+                      0.4103, 0.8334, 0.8361, 0.7898, 0.9067, 0.7235, 0.2233,
+                      0.3637, 0.9009, 0.8914, 0.3259, 0.8165, 0.9365, 0.9274,
+                      0.5741, 0.2639, 0.6520, 0.7150, 0.2093, 0.3816, 0.4707,
+                      0.4201, 0.9190, 0.5078, 0.8874, 0.9120, 0.2753, 0.7359,
+                      0.5812, 0.5682, 0.7646, 0.6267, 0.4102, 0.8266, 0.8853,
+                      0.7018, 0.9169, 0.0053, 0.7880, 0.6418, 0.6555, 0.9720,
+                      0.3526, 0.6341, 0.5088, 0.2195, 0.0203, 0.5525, 0.7633,
+                      0.6606, 0.4333, 0.8817, 0.0693, 0.9617, 0.5559, 0.9634,
+                      0.6048, 0.0232, 0.1068, 0.9352, 0.6002, 0.6363, 0.5154,
+                      0.1116, 0.5347, 0.0671, 0.7793, 0.1196]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.9677, 0.1967, 0.0087,  ..., 0.2565, 0.6584, 0.6200])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.022435426712036133 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '46800', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.382112741470337}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 249, 250]),
+       col_indices=tensor([4150, 2888, 4184, 1530,  678,  479, 1107,  471, 4285,
+                           3837, 1975, 2514,  511, 1660, 2068, 1448, 4627, 3695,
+                           4646, 2830, 2653, 1667, 2953, 3899, 1002, 4696, 4142,
+                           2682, 2648, 3794, 1459,  982,  980, 1746, 3339,  149,
+                           1718, 4640,  314,   53, 2403, 1141, 3357, 1085,  466,
+                           1619, 2477, 3859, 2168,  947, 4059, 1003, 2781, 2708,
+                           1717, 3009, 1864, 3007, 3896, 3680, 4372, 3309, 2254,
+                           2203, 4715, 1069, 4309, 2391, 3090, 3258,  952, 4475,
+                           4160, 3612, 4789, 3335,  819, 1827, 2260, 3171, 3323,
+                           4626, 3362,    7,  972, 4803,  364, 2649, 4177, 4599,
+                           2900, 3224, 1640, 4077, 3701, 2791, 1433,  655, 2314,
+                           3198,  317,  850, 1087,  611,  645,  558,  726, 1381,
+                             90, 1884, 2477,  176, 4078,  600, 1776, 1815, 4980,
+                           3290,  976, 3882, 4218, 3337, 4340, 4550, 1601,  376,
+                           2443, 2180, 1347, 4274, 3578, 2389, 1349, 3996, 4180,
+                           3976, 1026, 1825, 1698, 4427, 2513, 1604,  114, 2995,
+                           2989, 1072, 3384, 2975, 4300, 3198, 3255, 1005, 1851,
+                           4373, 2417, 1761, 1977, 1033,  304, 4563,  572, 4037,
+                           3427, 1513,   75,  468, 3187, 2009, 1764, 1805, 1467,
+                           3749, 4166, 2128, 4824, 3213, 2655, 2007, 1437, 1298,
+                            483,  971, 2056, 2156, 2263,  607, 4650,  771,  456,
+                           2047, 3920, 3689, 2454, 4552, 1948, 4918, 2583, 4601,
+                           1062, 3584, 2635, 2071, 2042, 2779, 1369, 1671, 4485,
+                           2542, 4111, 1550, 2280, 3307, 1653, 1055,  571, 3882,
+                           2132,  941, 2447, 3838,  493, 2724, 4427, 2495,  491,
+                            348, 2552, 3299,  317, 1166, 2830, 4896, 4608, 3014,
+                            670, 2086, 2508, 2837, 2920,  612, 4090, 2710, 1095,
+                           1628, 1220,  274, 3831, 1535, 1786, 4549]),
+       values=tensor([0.5255, 0.0182, 0.6174, 0.1076, 0.3535, 0.7090, 0.2797,
+                      0.4131, 0.9644, 0.6573, 0.3774, 0.2463, 0.8634, 0.5392,
+                      0.6180, 0.7460, 0.0840, 0.6919, 0.2395, 0.6380, 0.3064,
+                      0.4299, 0.2434, 0.7003, 0.1509, 0.6268, 0.3419, 0.0217,
+                      0.6724, 0.4826, 0.7793, 0.9245, 0.4498, 0.8997, 0.8789,
+                      0.2006, 0.9117, 0.6104, 0.9445, 0.6803, 0.5546, 0.0430,
+                      0.8599, 0.6166, 0.9366, 0.0741, 0.0108, 0.4102, 0.8063,
+                      0.1147, 0.2712, 0.9101, 0.6498, 0.4997, 0.5120, 0.1408,
+                      0.5873, 0.5440, 0.2130, 0.6524, 0.1914, 0.2027, 0.3598,
+                      0.1760, 0.9961, 0.4064, 0.1145, 0.4074, 0.8942, 0.9988,
+                      0.7396, 0.3520, 0.8007, 0.2689, 0.7383, 0.4192, 0.4738,
+                      0.5964, 0.1917, 0.1869, 0.3576, 0.9988, 0.6764, 0.1906,
+                      0.7629, 0.4501, 0.8709, 0.2468, 0.5177, 0.2466, 0.2197,
+                      0.1446, 0.0928, 0.2356, 0.4535, 0.4306, 0.8108, 0.4445,
+                      0.2001, 0.2909, 0.2893, 0.9446, 0.2722, 0.1526, 0.7522,
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+                      0.2685, 0.2403, 0.0997, 0.7290, 0.0363, 0.1796, 0.0573,
+                      0.1340, 0.1547, 0.5881, 0.5516, 0.6658, 0.9991, 0.5590,
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+                      0.2798, 0.7978, 0.1100, 0.0574, 0.5151, 0.8940, 0.9058,
+                      0.1406, 0.2261, 0.0686, 0.3738, 0.0528]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.9505, 0.7309, 0.0755,  ..., 0.0952, 0.7737, 0.4268])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 1.382112741470337 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '355542', '-ss', '5000', '-sd', '1e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.588425874710083}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([4976, 2999, 3531,  729, 4293, 4703,  527,  519, 1087,
+                           2588, 2503, 4621,  623, 2508, 4018, 2181, 4566,  129,
+                           1882,  141,  241, 4234, 3011, 3475, 1507, 1753, 1592,
+                             16, 1837, 2881, 4935, 1126, 1212, 1843, 3769, 1406,
+                           4716, 3662, 2501, 4960, 3438, 3187,  281, 2582, 4408,
+                           1833, 1310, 2092, 1021, 4933, 3776, 3565, 3765, 4067,
+                           3036, 4650, 3192, 1618, 3142,  599, 3093, 1420, 3804,
+                            496, 2492, 4465, 4733, 3170, 4826, 4434, 3018,  360,
+                           3823, 3346, 2952, 3585, 1580, 4171, 3383, 4158, 1729,
+                           4780,  335, 3992, 4542, 2789, 4029,  416, 4212,  913,
+                            792, 2949, 3671, 2278, 3431, 4080, 1970, 3530, 1625,
+                           1846,  123, 3801,  179, 2650, 2793, 3609, 4951, 1904,
+                           3327, 3068,  797, 2882, 3831, 1946, 4674, 2611, 3167,
+                           3799,  820, 4595, 2920, 4476,  339,   16, 1057, 3858,
+                           1996, 2480,  211,  316, 2763, 1963, 1855,  789, 4091,
+                           3327, 1560, 1579, 4983,  674, 1259, 1259, 1614, 4040,
+                           4463, 3393,  619, 1723, 4630,  933, 1161, 3845, 4268,
+                           4840, 4044, 3897, 1211, 4242,  540,  433, 3144, 2336,
+                           4751,  151, 2282, 2250, 2548, 3515,  851, 1606, 4107,
+                            209,  996, 4881, 3062, 1226,  876, 3179,  583, 2155,
+                            304, 1823,   35,  692, 3702, 4106, 1336, 1789, 1359,
+                           3986, 1095,  312, 3819,  955, 1411, 4676, 1229, 4251,
+                            263, 3639, 4785, 3043, 1158, 1138, 2259, 2515, 3611,
+                           4898, 3676,  269,  509, 3101, 1147,  409, 3191, 1984,
+                           2546, 1127, 3710, 4610, 4906,  997, 3399, 3628,  347,
+                            847, 2034,  588, 2961, 2738, 4337, 3827, 1890, 1227,
+                           1635, 1728, 2759, 1429, 4176, 4191, 4862, 4968, 1336,
+                            119, 3406,  554,  532, 1507, 1917, 2750]),
+       values=tensor([0.5199, 0.0559, 0.9006, 0.7777, 0.2843, 0.6207, 0.6975,
+                      0.8689, 0.5742, 0.4737, 0.6755, 0.5958, 0.4384, 0.4390,
+                      0.8599, 0.7635, 0.7028, 0.4511, 0.3241, 0.0806, 0.8242,
+                      0.9053, 0.3849, 0.6596, 0.0472, 0.0762, 0.5653, 0.2663,
+                      0.8433, 0.4091, 0.7837, 0.1469, 0.0020, 0.4944, 0.8738,
+                      0.1320, 0.2431, 0.2650, 0.7193, 0.2483, 0.4028, 0.5091,
+                      0.3427, 0.6757, 0.9747, 0.5603, 0.1148, 0.1932, 0.5560,
+                      0.7920, 0.7790, 0.7814, 0.5929, 0.3079, 0.4008, 0.4233,
+                      0.4893, 0.9629, 0.6575, 0.2210, 0.9465, 0.6464, 0.0795,
+                      0.6735, 0.0196, 0.9297, 0.4843, 0.7360, 0.6757, 0.8217,
+                      0.8318, 0.9782, 0.6754, 0.1098, 0.6940, 0.8548, 0.6163,
+                      0.6934, 0.6171, 0.1041, 0.7511, 0.4878, 0.3635, 0.8620,
+                      0.3905, 0.3542, 0.8549, 0.4722, 0.9206, 0.2789, 0.9568,
+                      0.1952, 0.7173, 0.8016, 0.5436, 0.6025, 0.2628, 0.4776,
+                      0.0648, 0.4119, 0.1873, 0.5768, 0.6229, 0.4612, 0.3781,
+                      0.9685, 0.2051, 0.1702, 0.4419, 0.4538, 0.4515, 0.1665,
+                      0.0292, 0.6419, 0.1217, 0.0198, 0.2563, 0.3495, 0.0936,
+                      0.2664, 0.5259, 0.3038, 0.7533, 0.2522, 0.7253, 0.1463,
+                      0.2087, 0.7896, 0.0364, 0.6152, 0.7986, 0.8112, 0.7556,
+                      0.0756, 0.4969, 0.3790, 0.1978, 0.1043, 0.8605, 0.5011,
+                      0.8762, 0.2381, 0.0660, 0.2738, 0.0392, 0.5623, 0.1390,
+                      0.4257, 0.6102, 0.9603, 0.2834, 0.0200, 0.2511, 0.8990,
+                      0.6324, 0.9167, 0.3730, 0.6817, 0.4652, 0.8996, 0.9136,
+                      0.8631, 0.9770, 0.2565, 0.6496, 0.1799, 0.4018, 0.4388,
+                      0.9314, 0.6833, 0.7914, 0.9455, 0.7655, 0.3170, 0.6697,
+                      0.9757, 0.4151, 0.9576, 0.1567, 0.8861, 0.0170, 0.7822,
+                      0.2221, 0.7184, 0.0581, 0.4824, 0.4815, 0.2522, 0.1559,
+                      0.1892, 0.1146, 0.0347, 0.4792, 0.0555, 0.6999, 0.5092,
+                      0.0919, 0.7115, 0.9111, 0.4498, 0.4923, 0.5300, 0.3126,
+                      0.8103, 0.3276, 0.8722, 0.4823, 0.2311, 0.5957, 0.0760,
+                      0.2892, 0.3555, 0.6604, 0.7686, 0.1412, 0.8595, 0.6702,
+                      0.1119, 0.1550, 0.8493, 0.8158, 0.3714, 0.8983, 0.3484,
+                      0.6611, 0.8110, 0.2241, 0.8267, 0.9605, 0.8151, 0.1779,
+                      0.2906, 0.1723, 0.3272, 0.4678, 0.1292, 0.9514, 0.6369,
+                      0.8054, 0.2983, 0.3742, 0.8673, 0.0274, 0.1851, 0.9052,
+                      0.8742, 0.4529, 0.2266, 0.4410, 0.5323]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4269, 0.0147, 0.1392,  ..., 0.1737, 0.9746, 0.9710])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.588425874710083 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([4976, 2999, 3531,  729, 4293, 4703,  527,  519, 1087,
+                           2588, 2503, 4621,  623, 2508, 4018, 2181, 4566,  129,
+                           1882,  141,  241, 4234, 3011, 3475, 1507, 1753, 1592,
+                             16, 1837, 2881, 4935, 1126, 1212, 1843, 3769, 1406,
+                           4716, 3662, 2501, 4960, 3438, 3187,  281, 2582, 4408,
+                           1833, 1310, 2092, 1021, 4933, 3776, 3565, 3765, 4067,
+                           3036, 4650, 3192, 1618, 3142,  599, 3093, 1420, 3804,
+                            496, 2492, 4465, 4733, 3170, 4826, 4434, 3018,  360,
+                           3823, 3346, 2952, 3585, 1580, 4171, 3383, 4158, 1729,
+                           4780,  335, 3992, 4542, 2789, 4029,  416, 4212,  913,
+                            792, 2949, 3671, 2278, 3431, 4080, 1970, 3530, 1625,
+                           1846,  123, 3801,  179, 2650, 2793, 3609, 4951, 1904,
+                           3327, 3068,  797, 2882, 3831, 1946, 4674, 2611, 3167,
+                           3799,  820, 4595, 2920, 4476,  339,   16, 1057, 3858,
+                           1996, 2480,  211,  316, 2763, 1963, 1855,  789, 4091,
+                           3327, 1560, 1579, 4983,  674, 1259, 1259, 1614, 4040,
+                           4463, 3393,  619, 1723, 4630,  933, 1161, 3845, 4268,
+                           4840, 4044, 3897, 1211, 4242,  540,  433, 3144, 2336,
+                           4751,  151, 2282, 2250, 2548, 3515,  851, 1606, 4107,
+                            209,  996, 4881, 3062, 1226,  876, 3179,  583, 2155,
+                            304, 1823,   35,  692, 3702, 4106, 1336, 1789, 1359,
+                           3986, 1095,  312, 3819,  955, 1411, 4676, 1229, 4251,
+                            263, 3639, 4785, 3043, 1158, 1138, 2259, 2515, 3611,
+                           4898, 3676,  269,  509, 3101, 1147,  409, 3191, 1984,
+                           2546, 1127, 3710, 4610, 4906,  997, 3399, 3628,  347,
+                            847, 2034,  588, 2961, 2738, 4337, 3827, 1890, 1227,
+                           1635, 1728, 2759, 1429, 4176, 4191, 4862, 4968, 1336,
+                            119, 3406,  554,  532, 1507, 1917, 2750]),
+       values=tensor([0.5199, 0.0559, 0.9006, 0.7777, 0.2843, 0.6207, 0.6975,
+                      0.8689, 0.5742, 0.4737, 0.6755, 0.5958, 0.4384, 0.4390,
+                      0.8599, 0.7635, 0.7028, 0.4511, 0.3241, 0.0806, 0.8242,
+                      0.9053, 0.3849, 0.6596, 0.0472, 0.0762, 0.5653, 0.2663,
+                      0.8433, 0.4091, 0.7837, 0.1469, 0.0020, 0.4944, 0.8738,
+                      0.1320, 0.2431, 0.2650, 0.7193, 0.2483, 0.4028, 0.5091,
+                      0.3427, 0.6757, 0.9747, 0.5603, 0.1148, 0.1932, 0.5560,
+                      0.7920, 0.7790, 0.7814, 0.5929, 0.3079, 0.4008, 0.4233,
+                      0.4893, 0.9629, 0.6575, 0.2210, 0.9465, 0.6464, 0.0795,
+                      0.6735, 0.0196, 0.9297, 0.4843, 0.7360, 0.6757, 0.8217,
+                      0.8318, 0.9782, 0.6754, 0.1098, 0.6940, 0.8548, 0.6163,
+                      0.6934, 0.6171, 0.1041, 0.7511, 0.4878, 0.3635, 0.8620,
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+                      0.9685, 0.2051, 0.1702, 0.4419, 0.4538, 0.4515, 0.1665,
+                      0.0292, 0.6419, 0.1217, 0.0198, 0.2563, 0.3495, 0.0936,
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+                      0.8103, 0.3276, 0.8722, 0.4823, 0.2311, 0.5957, 0.0760,
+                      0.2892, 0.3555, 0.6604, 0.7686, 0.1412, 0.8595, 0.6702,
+                      0.1119, 0.1550, 0.8493, 0.8158, 0.3714, 0.8983, 0.3484,
+                      0.6611, 0.8110, 0.2241, 0.8267, 0.9605, 0.8151, 0.1779,
+                      0.2906, 0.1723, 0.3272, 0.4678, 0.1292, 0.9514, 0.6369,
+                      0.8054, 0.2983, 0.3742, 0.8673, 0.0274, 0.1851, 0.9052,
+                      0.8742, 0.4529, 0.2266, 0.4410, 0.5323]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4269, 0.0147, 0.1392,  ..., 0.1737, 0.9746, 0.9710])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.588425874710083 seconds
+
+[18.34, 17.96, 17.95, 19.17, 18.44, 18.12, 18.19, 19.28, 18.14, 18.1]
+[46.57]
+14.066857814788818
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 355542, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.588425874710083, 'TIME_S_1KI': 0.02978108317641821, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 655.0935684347153, 'W': 46.57}
+[18.34, 17.96, 17.95, 19.17, 18.44, 18.12, 18.19, 19.28, 18.14, 18.1, 18.29, 18.16, 18.13, 17.89, 18.15, 18.08, 18.32, 17.91, 18.06, 18.27]
+328.45
+16.4225
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 355542, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.588425874710083, 'TIME_S_1KI': 0.02978108317641821, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 655.0935684347153, 'W': 46.57, 'J_1KI': 1.8425209073322286, 'W_1KI': 0.13098311873140162, 'W_D': 30.1475, 'J_D': 424.08059597134593, 'W_D_1KI': 0.08479307648604104, 'J_D_1KI': 0.00023848962003375422}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..0d245c9
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 1, "ITERATIONS": 303638, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.314902782440186, "TIME_S_1KI": 0.033971053631100805, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 652.3264142632485, "W": 46.59, "J_1KI": 2.148368828220606, "W_1KI": 0.1534392928421344, "W_D": 30.048000000000002, "J_D": 420.71483356475835, "W_D_1KI": 0.0989599457248434, "J_D_1KI": 0.0003259142324901475}
diff --git a/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..2579376
--- /dev/null
+++ b/pytorch/output_synthetic_1core_old2/xeon_4216_1_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.018550395965576172}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([2148, 2186, 2653,  ..., 4713,  108, 1050]),
+       values=tensor([0.2240, 0.7824, 0.4591,  ..., 0.0832, 0.2125, 0.9204]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.9989, 0.0104, 0.9920,  ..., 0.8791, 0.9452, 0.9082])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.018550395965576172 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '56602', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.9573328495025635}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    2,    2,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([ 143, 2910, 3407,  ...,  360, 1598, 1598]),
+       values=tensor([0.9185, 0.3997, 0.2489,  ..., 0.0567, 0.3179, 0.0072]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.5677, 0.2086, 0.3083,  ..., 0.5984, 0.8633, 0.3307])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 1.9573328495025635 seconds
+
+['apptainer', 'run', '--env', 'OMP_PROC_BIND=true', '--env', 'OMP_PLACES={0:1}', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '303638', '-ss', '5000', '-sd', '5e-05', '-c', '1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.314902782440186}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4078,   67, 3564,  ..., 1146, 2529, 4353]),
+       values=tensor([0.7614, 0.2386, 0.2238,  ..., 0.8351, 0.5866, 0.5164]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.2149, 0.3426, 0.8577,  ..., 0.2235, 0.1553, 0.5182])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.314902782440186 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4078,   67, 3564,  ..., 1146, 2529, 4353]),
+       values=tensor([0.7614, 0.2386, 0.2238,  ..., 0.8351, 0.5866, 0.5164]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.2149, 0.3426, 0.8577,  ..., 0.2235, 0.1553, 0.5182])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.314902782440186 seconds
+
+[18.14, 22.27, 18.32, 18.01, 17.87, 18.05, 18.11, 18.01, 18.55, 17.98]
+[46.59]
+14.001425504684448
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 303638, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.314902782440186, 'TIME_S_1KI': 0.033971053631100805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 652.3264142632485, 'W': 46.59}
+[18.14, 22.27, 18.32, 18.01, 17.87, 18.05, 18.11, 18.01, 18.55, 17.98, 18.58, 17.96, 18.17, 18.69, 18.11, 18.0, 18.22, 18.0, 18.06, 18.18]
+330.84000000000003
+16.542
+{'CPU': 'Xeon 4216', 'CORES': 1, 'ITERATIONS': 303638, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.314902782440186, 'TIME_S_1KI': 0.033971053631100805, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 652.3264142632485, 'W': 46.59, 'J_1KI': 2.148368828220606, 'W_1KI': 0.1534392928421344, 'W_D': 30.048000000000002, 'J_D': 420.71483356475835, 'W_D_1KI': 0.0989599457248434, 'J_D_1KI': 0.0003259142324901475}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..60ceca9
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 271, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 13.764680624008179, "TIME_S_1KI": 50.79217942438442, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 708.3959098911284, "W": 52.59794170063422, "J_1KI": 2614.0070475687394, "W_1KI": 194.08834575879789, "W_D": 33.72694170063423, "J_D": 454.2388310525417, "W_D_1KI": 124.45365941193442, "J_D_1KI": 459.2385956159942}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..3f5c857
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,74 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 100000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.8156983852386475}
+
+tensor(indices=tensor([[87675, 12759, 51005,  ...,  2425, 41762, 29389],
+                       [45918, 64834, 60882,  ...,  3878, 87168, 85462]]),
+       values=tensor([0.8155, 0.0162, 0.6356,  ..., 0.2182, 0.2053, 0.5055]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.1922, 0.3096, 0.1781,  ..., 0.3622, 0.7702, 0.3772])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.8156983852386475 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 128 -ss 100000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 4.945516109466553}
+
+tensor(indices=tensor([[48382, 90202, 56319,  ..., 51472, 45013, 24927],
+                       [ 9625, 87352, 54913,  ..., 87014, 15190, 70778]]),
+       values=tensor([2.9508e-04, 5.3837e-01, 2.3044e-01,  ...,
+                      7.8739e-01, 2.0008e-01, 6.1077e-01]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.1999, 0.8074, 0.7307,  ..., 0.2500, 0.5226, 0.2995])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 4.945516109466553 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 271 -ss 100000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 13.764680624008179}
+
+tensor(indices=tensor([[66316, 95038, 72364,  ..., 73501, 98405, 57084],
+                       [28561,  3708, 81350,  ..., 68253, 50641, 67993]]),
+       values=tensor([0.0455, 0.3371, 0.7609,  ..., 0.8030, 0.1462, 0.3187]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.3763, 0.9250, 0.8921,  ..., 0.1108, 0.7157, 0.5685])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 13.764680624008179 seconds
+
+tensor(indices=tensor([[66316, 95038, 72364,  ..., 73501, 98405, 57084],
+                       [28561,  3708, 81350,  ..., 68253, 50641, 67993]]),
+       values=tensor([0.0455, 0.3371, 0.7609,  ..., 0.8030, 0.1462, 0.3187]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.3763, 0.9250, 0.8921,  ..., 0.1108, 0.7157, 0.5685])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 13.764680624008179 seconds
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+[20.92, 20.8, 20.44, 20.52, 20.8, 20.96, 21.32, 21.28, 21.44, 21.12]
+[21.12, 20.92, 20.92, 22.0, 23.28, 35.6, 50.2, 69.52, 84.52, 96.84, 95.28, 92.36, 89.24]
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+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 271, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 13.764680624008179, 'TIME_S_1KI': 50.79217942438442, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 708.3959098911284, 'W': 52.59794170063422, 'J_1KI': 2614.0070475687394, 'W_1KI': 194.08834575879789, 'W_D': 33.72694170063423, 'J_D': 454.2388310525417, 'W_D_1KI': 124.45365941193442, 'J_D_1KI': 459.2385956159942}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..e22cb60
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 32, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.193521499633789, "TIME_S_1KI": 318.5475468635559, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 800.2320325660704, "W": 45.6654704340178, "J_1KI": 25007.2510176897, "W_1KI": 1427.0459510630562, "W_D": 26.934470434017797, "J_D": 471.9939555346964, "W_D_1KI": 841.7022010630561, "J_D_1KI": 26303.193783220504}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..6441d67
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 100000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.276198148727417}
+
+tensor(indices=tensor([[ 9271,  3520, 84372,  ..., 69208, 85337, 10954],
+                       [92692, 94391, 59106,  ..., 52928, 14707,  1304]]),
+       values=tensor([0.4952, 0.0287, 0.0135,  ..., 0.7782, 0.3099, 0.9948]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.6617, 0.1769, 0.5475,  ..., 0.2510, 0.2331, 0.9029])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 3.276198148727417 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 32 -ss 100000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.193521499633789}
+
+tensor(indices=tensor([[75264, 47872, 55415,  ..., 19097, 10593, 87775],
+                       [90533, 17061, 42308,  ..., 10695, 74233, 93542]]),
+       values=tensor([0.3597, 0.1443, 0.1824,  ..., 0.5042, 0.8186, 0.7909]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.9072, 0.8205, 0.6569,  ..., 0.2116, 0.6400, 0.9604])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.193521499633789 seconds
+
+tensor(indices=tensor([[75264, 47872, 55415,  ..., 19097, 10593, 87775],
+                       [90533, 17061, 42308,  ..., 10695, 74233, 93542]]),
+       values=tensor([0.3597, 0.1443, 0.1824,  ..., 0.5042, 0.8186, 0.7909]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.9072, 0.8205, 0.6569,  ..., 0.2116, 0.6400, 0.9604])
+Matrix Type: synthetic
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+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..30b9174
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 10, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 31.283674955368042, "TIME_S_1KI": 3128.367495536804, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2065.684582710266, "W": 29.080338518534504, "J_1KI": 206568.4582710266, "W_1KI": 2908.0338518534504, "W_D": 10.482338518534501, "J_D": 744.5994844484327, "W_D_1KI": 1048.2338518534502, "J_D_1KI": 104823.38518534502}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..c07cd28
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_0.01.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 100000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 31.283674955368042}
+
+tensor(indices=tensor([[39756, 56280,  8316,  ...,  2685, 54729, 61793],
+                       [13279, 85477, 14865,  ..., 46128, 20132,  4621]]),
+       values=tensor([0.9595, 0.6864, 0.2705,  ..., 0.5992, 0.1879, 0.1775]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_coo)
+tensor([0.2852, 0.8402, 0.2843,  ..., 0.8163, 0.4481, 0.7594])
+Matrix Type: synthetic
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+Shape: torch.Size([100000, 100000])
+Rows: 100000
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+NNZ: 100000000
+Density: 0.01
+Time: 31.283674955368042 seconds
+
+tensor(indices=tensor([[39756, 56280,  8316,  ...,  2685, 54729, 61793],
+                       [13279, 85477, 14865,  ..., 46128, 20132,  4621]]),
+       values=tensor([0.9595, 0.6864, 0.2705,  ..., 0.5992, 0.1879, 0.1775]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_coo)
+tensor([0.2852, 0.8402, 0.2843,  ..., 0.8163, 0.4481, 0.7594])
+Matrix Type: synthetic
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+Shape: torch.Size([100000, 100000])
+Rows: 100000
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+Time: 31.283674955368042 seconds
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..2455b73
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_1e-05.json
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..6325e2c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_1e-05.output
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..c96f39e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_5e-05.json
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..92e7e49
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_100000_5e-05.output
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..8cbcc03
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.0001.json
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..48cfbd1
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.0001.output
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.0001']
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..70aa208
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4771, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.456588745117188, "TIME_S_1KI": 2.1916974942605716, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 345.3197015190124, "W": 24.216936772507907, "J_1KI": 72.37889363215518, "W_1KI": 5.075861826138736, "W_D": 5.9229367725079065, "J_D": 84.45769907283783, "W_D_1KI": 1.2414455612047592, "J_D_1KI": 0.26020657329800023}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..d1d5c26
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.001.output
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.001']
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 2771 -ss 10000 -sd 0.001']
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..6d256d1
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.01.json
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+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 482, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.787339210510254, "TIME_S_1KI": 22.38037180603787, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.30498919487, "W": 22.873398379914686, "J_1KI": 674.9066165868672, "W_1KI": 47.45518336081885, "W_D": 4.648398379914685, "J_D": 66.10942369103431, "W_D_1KI": 9.643980041316773, "J_D_1KI": 20.008257347130236}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..b720bb4
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.01.output
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.01']
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+                       [6421, 9924, 1728,  ..., 2998, 4696, 8454]]),
+       values=tensor([0.3997, 0.7558, 0.1388,  ..., 0.9978, 0.9847, 0.4915]),
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+tensor([0.2367, 0.8301, 0.6234,  ..., 0.1068, 0.8744, 0.1789])
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 433 -ss 10000 -sd 0.01']
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..cdb5ffb
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 93, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.05737018585205, "TIME_S_1KI": 108.14376543926936, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 373.22606477737423, "W": 24.433023960718955, "J_1KI": 4013.183492229831, "W_1KI": 262.7206877496662, "W_D": 6.007023960718957, "J_D": 91.76014878416062, "W_D_1KI": 64.59165549160168, "J_D_1KI": 694.5339300172224}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..e80daea
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 1.1215338706970215}
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+       values=tensor([0.5595, 0.2399, 0.7407,  ..., 0.9554, 0.1670, 0.2830]),
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+tensor([0.8054, 0.5531, 0.8915,  ..., 0.9134, 0.0499, 0.3252])
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 93 -ss 10000 -sd 0.05']
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..573238c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 48, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.678550004959106, "TIME_S_1KI": 222.46979176998138, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 425.31584163665775, "W": 24.57520335684497, "J_1KI": 8860.746700763704, "W_1KI": 511.9834032676035, "W_D": 5.9762033568449695, "J_D": 103.42839990377426, "W_D_1KI": 124.50423660093686, "J_D_1KI": 2593.838262519518}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..987220e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.1.output
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 2.180788278579712}
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+                       [ 795,   78, 6603,  ..., 8745, 2373, 9995]]),
+       values=tensor([0.1634, 0.1032, 0.5646,  ..., 0.7997, 0.2164, 0.4007]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.8075, 0.9053, 0.9449,  ..., 0.1857, 0.8558, 0.5072])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
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+Time: 2.180788278579712 seconds
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 48 -ss 10000 -sd 0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.678550004959106}
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+                       [8948, 6866, 3950,  ..., 8270, 8493, 8265]]),
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..3af4a2d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 24, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.624644994735718, "TIME_S_1KI": 442.6935414473216, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 518.5183028793334, "W": 25.44127487819161, "J_1KI": 21604.929286638893, "W_1KI": 1060.0531199246504, "W_D": 6.432274878191613, "J_D": 131.0961132829189, "W_D_1KI": 268.0114532579839, "J_D_1KI": 11167.143885749327}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..c24e550
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 4.365953207015991}
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+                       [5551, 6794, 1853,  ..., 2090, 5489,  112]]),
+       values=tensor([0.0738, 0.6009, 0.0674,  ..., 0.2677, 0.6451, 0.5010]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_coo)
+tensor([0.7928, 0.9854, 0.6657,  ..., 0.9983, 0.2925, 0.1386])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 4.365953207015991 seconds
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 24 -ss 10000 -sd 0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.624644994735718}
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+                       [9639, 9310, 7087,  ..., 9751, 3863, 4367]]),
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+                       [9639, 9310, 7087,  ..., 9751, 3863, 4367]]),
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..05e572b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 16, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.421019077301025, "TIME_S_1KI": 651.3136923313141, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 587.9804890060425, "W": 26.20605496671889, "J_1KI": 36748.780562877655, "W_1KI": 1637.8784354199306, "W_D": 7.64405496671889, "J_D": 171.5082710094452, "W_D_1KI": 477.75343541993067, "J_D_1KI": 29859.589713745667}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..3df6353
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 6.48048210144043}
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+                       [9596, 8684, 4910,  ..., 4257, 2424, 1009]]),
+       values=tensor([0.9528, 0.4086, 0.1813,  ..., 0.8890, 0.1647, 0.4658]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_coo)
+tensor([0.0108, 0.4453, 0.6905,  ..., 0.6137, 0.6470, 0.8002])
+Matrix Type: synthetic
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 6.48048210144043 seconds
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 16 -ss 10000 -sd 0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.421019077301025}
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..b6f44f3
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 12, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.651156663894653, "TIME_S_1KI": 887.5963886578878, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 679.2371678161621, "W": 26.61789400659298, "J_1KI": 56603.09731801351, "W_1KI": 2218.1578338827485, "W_D": 7.888894006592977, "J_D": 201.30931548953052, "W_D_1KI": 657.4078338827482, "J_D_1KI": 54783.98615689568}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..700d83b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.4.output
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 10000 -sd 0.4']
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+        8.9305e-01])
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..2cdeddc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.5.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..fc040ba
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_0.5.output
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..5049a60
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 322612, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.590221643447876, "TIME_S_1KI": 0.0328264963592423, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 312.4309623336792, "W": 21.94978314858273, "J_1KI": 0.9684418506865188, "W_1KI": 0.06803771449475757, "W_D": 3.480783148582727, "J_D": 49.54511037421225, "W_D_1KI": 0.01078937903296445, "J_D_1KI": 3.344382426247149e-05}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..9cc8c51
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,855 @@
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..d4c2625
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_5e-05.json
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+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 90138, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.656882047653198, "TIME_S_1KI": 0.1182285168037143, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 319.2859945774078, "W": 22.370679958106543, "J_1KI": 3.542190802740329, "W_1KI": 0.24818256404742223, "W_D": 3.960679958106539, "J_D": 56.52888700723637, "W_D_1KI": 0.04394018014718031, "J_D_1KI": 0.0004874767594930031}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..4303f77
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_10000_5e-05.output
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+                       [3780, 8217, 6825,  ...,  118, 2662, 5701]]),
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+tensor([0.5584, 0.1963, 0.8382,  ..., 0.7245, 0.9482, 0.1967])
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+Shape: torch.Size([10000, 10000])
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+Size: 100000000
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..34d30a8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_0.0001.json
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..3b3c81a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_0.0001.output
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 500000 -sd 0.0001']
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+                       [165636, 118584, 230457,  ..., 297377,  39670, 364887]]),
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..2ec83c9
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_1e-05.json
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..7bcebef
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_1e-05.output
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 39 -ss 500000 -sd 1e-05']
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..3d4e91c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_5e-05.json
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+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 14, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.483234643936157, "TIME_S_1KI": 748.8024745668683, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 607.1887491989137, "W": 34.911636506462884, "J_1KI": 43370.62494277955, "W_1KI": 2493.688321890206, "W_D": 16.024636506462883, "J_D": 278.70303344059, "W_D_1KI": 1144.6168933187776, "J_D_1KI": 81758.34952276981}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..1329062
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_500000_5e-05.output
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..e169a34
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.0001.json
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..f9f7017
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.0001.output
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..f053fdb
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.001.json
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+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 111, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 14.219213724136353, "TIME_S_1KI": 128.10102454176894, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 433.3483148097992, "W": 38.30053612271195, "J_1KI": 3904.038872160353, "W_1KI": 345.04987497938686, "W_D": 19.167536122711947, "J_D": 216.86953548693657, "W_D_1KI": 172.68050561001755, "J_D_1KI": 1555.6802307208789}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..cbb8b97
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.001.output
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..5919928
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.01.json
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..e1aebfa
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_0.01.output
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 50000 -sd 0.01']
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 16 -ss 50000 -sd 0.01']
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..62236f4
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_1e-05.json
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..d4fd0f2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_1e-05.output
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..e3bfa50
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_5e-05.json
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+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 2771, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 16.318060398101807, "TIME_S_1KI": 5.888870587550273, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1258.8953846740724, "W": 64.44745463401114, "J_1KI": 454.31085697368184, "W_1KI": 23.257832780227766, "W_D": 45.63045463401114, "J_D": 891.3302948200704, "W_D_1KI": 16.467143498380057, "J_D_1KI": 5.942671778556499}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..a8463c6
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_50000_5e-05.output
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..4a68163
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.0001.json
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..c6c4b62
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.0001.output
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..ced88f1
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.001.json
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..1dfeeb8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.001.output
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.001']
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+tensor([0.1493, 0.7474, 0.2368,  ..., 0.4590, 0.8438, 0.4019])
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 9146 -ss 5000 -sd 0.001']
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..de325ee
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1914, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.46642279624939, "TIME_S_1KI": 5.468350468259869, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 320.75712821960445, "W": 22.459238592806493, "J_1KI": 167.58470648882155, "W_1KI": 11.734189442427635, "W_D": 4.083238592806495, "J_D": 58.3157741279602, "W_D_1KI": 2.133353496764104, "J_D_1KI": 1.1146047527503156}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..33a3190
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.01.output
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..12658d2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.05.json
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..13c9cf8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.05.output
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.05']
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 358 -ss 5000 -sd 0.05']
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..88a0c32
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 197, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.462316274642944, "TIME_S_1KI": 53.1082044398119, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 354.70012279510496, "W": 23.18372200056496, "J_1KI": 1800.508237538604, "W_1KI": 117.68386802317238, "W_D": 4.7517220005649605, "J_D": 72.69912816619873, "W_D_1KI": 24.12041624652264, "J_D_1KI": 122.43866114986113}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..7aa3065
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.5312061309814453}
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+tensor([0.4194, 0.3154, 0.0286,  ..., 0.7128, 0.7672, 0.7125])
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 197 -ss 5000 -sd 0.1']
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..fc28132
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 98, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.360224723815918, "TIME_S_1KI": 105.71657881444814, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 367.190686454773, "W": 23.931765947540203, "J_1KI": 3746.843739334419, "W_1KI": 244.20169334224698, "W_D": 5.381765947540202, "J_D": 82.57369460105903, "W_D_1KI": 54.91597905653268, "J_D_1KI": 560.3671332299252}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..e41dc4d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.2.output
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 1.0649404525756836}
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+       values=tensor([0.5014, 0.2122, 0.1157,  ..., 0.1611, 0.3118, 0.8655]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_coo)
+tensor([0.2317, 0.6495, 0.2388,  ..., 0.5848, 0.2642, 0.7839])
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+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 98 -ss 5000 -sd 0.2']
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..57a3699
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.3.json
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+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 64, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.220601797103882, "TIME_S_1KI": 159.69690307974815, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 403.9369924163819, "W": 24.765001026128928, "J_1KI": 6311.515506505967, "W_1KI": 386.9531410332645, "W_D": 6.221001026128928, "J_D": 101.46950697326663, "W_D_1KI": 97.2031410332645, "J_D_1KI": 1518.7990786447579}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..ec5f6f2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.3.output
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.6283369064331055}
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+                       [2791, 1620, 3013,  ..., 1069, 1185, 4438]]),
+       values=tensor([0.3064, 0.9507, 0.5509,  ..., 0.1641, 0.9585, 0.2381]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_coo)
+tensor([0.5387, 0.1602, 0.4905,  ..., 0.0478, 0.9961, 0.4036])
+Matrix Type: synthetic
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+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 1.6283369064331055 seconds
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 64 -ss 5000 -sd 0.3']
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..3c160b2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.4.json
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+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 50, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.600855827331543, "TIME_S_1KI": 212.01711654663086, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 415.4362985801697, "W": 24.028108964441873, "J_1KI": 8308.725971603395, "W_1KI": 480.5621792888375, "W_D": 5.398108964441871, "J_D": 93.33112359523771, "W_D_1KI": 107.96217928883742, "J_D_1KI": 2159.2435857767487}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..91b781e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 2.07926344871521}
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+       values=tensor([0.3210, 0.9373, 0.0094,  ..., 0.0958, 0.0204, 0.0169]),
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+tensor([0.4258, 0.7131, 0.4201,  ..., 0.1966, 0.1804, 0.5093])
+Matrix Type: synthetic
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+Shape: torch.Size([5000, 5000])
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+Time: 2.07926344871521 seconds
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 50 -ss 5000 -sd 0.4']
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..c516020
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 39, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.249986171722412, "TIME_S_1KI": 262.8201582492926, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 448.57725488662726, "W": 25.866452261197104, "J_1KI": 11501.980894528904, "W_1KI": 663.2423656717206, "W_D": 7.232452261197103, "J_D": 125.4255337634087, "W_D_1KI": 185.4474938768488, "J_D_1KI": 4755.063945560226}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..447c49c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_0.5.output
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+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 2.6484789848327637}
+
+tensor(indices=tensor([[2606, 4503,  568,  ..., 4002, 1170, 4255],
+                       [ 326,   86, 3578,  ..., 1350, 1054, 1712]]),
+       values=tensor([0.9165, 0.3495, 0.3325,  ..., 0.4870, 0.4561, 0.6575]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_coo)
+tensor([0.1516, 0.9580, 0.8544,  ..., 0.9817, 0.5121, 0.4328])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 2.6484789848327637 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 39 -ss 5000 -sd 0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.249986171722412}
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+tensor(indices=tensor([[3439,  237, 4370,  ..., 2920, 4672, 4476],
+                       [2193, 2292,  215,  ...,  299, 4749, 1213]]),
+       values=tensor([0.5546, 0.6374, 0.2752,  ..., 0.5437, 0.9306, 0.9193]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_coo)
+tensor([0.2963, 0.0823, 0.0479,  ..., 0.1943, 0.5608, 0.1183])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.249986171722412 seconds
+
+tensor(indices=tensor([[3439,  237, 4370,  ..., 2920, 4672, 4476],
+                       [2193, 2292,  215,  ...,  299, 4749, 1213]]),
+       values=tensor([0.5546, 0.6374, 0.2752,  ..., 0.5437, 0.9306, 0.9193]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_coo)
+tensor([0.2963, 0.0823, 0.0479,  ..., 0.1943, 0.5608, 0.1183])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.249986171722412 seconds
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diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..28eb4c8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 647809, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.034083366394043, "TIME_S_1KI": 0.015489262060876035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 321.12039955139164, "W": 22.569400922042004, "J_1KI": 0.49570228192475196, "W_1KI": 0.03483959148767925, "W_D": 4.131400922042001, "J_D": 58.78211474800112, "W_D_1KI": 0.006377498494219748, "J_D_1KI": 9.844720425649764e-06}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..aa624bc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,429 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.00798487663269043}
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+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 647809, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.034083366394043, 'TIME_S_1KI': 0.015489262060876035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 321.12039955139164, 'W': 22.569400922042004, 'J_1KI': 0.49570228192475196, 'W_1KI': 0.03483959148767925, 'W_D': 4.131400922042001, 'J_D': 58.78211474800112, 'W_D_1KI': 0.006377498494219748, 'J_D_1KI': 9.844720425649764e-06}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..569d057
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 289181, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.686526536941528, "TIME_S_1KI": 0.03695445598757016, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 324.68591009140016, "W": 22.814226029486633, "J_1KI": 1.1227774649489426, "W_1KI": 0.07889254836758512, "W_D": 4.440226029486631, "J_D": 63.1920989794731, "W_D_1KI": 0.015354487429971649, "J_D_1KI": 5.309646010620216e-05}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..0c12568
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_coo_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,73 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 10 -ss 5000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.0067293643951416016}
+
+tensor(indices=tensor([[4742,  824, 4219,  ..., 2208, 2692, 1106],
+                       [2227, 2640,  905,  ..., 2494, 4908, 3090]]),
+       values=tensor([0.5330, 0.4399, 0.1637,  ..., 0.1800, 0.7845, 0.3210]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.5676, 0.2274, 0.7445,  ..., 0.8344, 0.2887, 0.3063])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.0067293643951416016 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 15603 -ss 5000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.566535234451294}
+
+tensor(indices=tensor([[1815, 2596,  437,  ..., 1883, 1495, 3601],
+                       [ 662, 1366, 4123,  ..., 2867,   17, 4303]]),
+       values=tensor([0.2537, 0.9612, 0.4196,  ..., 0.7819, 0.3756, 0.7330]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.4565, 0.9887, 0.5185,  ..., 0.0050, 0.0496, 0.5640])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.566535234451294 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic coo 289181 -ss 5000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.686526536941528}
+
+tensor(indices=tensor([[3774, 4104, 1716,  ..., 2959, 3358,  810],
+                       [2642, 4754, 2109,  ...,  867, 2120, 3002]]),
+       values=tensor([0.8150, 0.9729, 0.8881,  ..., 0.8163, 0.5997, 0.7733]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.6444, 0.5658, 0.7442,  ..., 0.5847, 0.4527, 0.5143])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.686526536941528 seconds
+
+tensor(indices=tensor([[3774, 4104, 1716,  ..., 2959, 3358,  810],
+                       [2642, 4754, 2109,  ...,  867, 2120, 3002]]),
+       values=tensor([0.8150, 0.9729, 0.8881,  ..., 0.8163, 0.5997, 0.7733]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.6444, 0.5658, 0.7442,  ..., 0.5847, 0.4527, 0.5143])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.686526536941528 seconds
+
+[20.16, 20.28, 20.16, 20.28, 20.4, 20.24, 20.28, 20.52, 20.68, 20.8]
+[21.0, 21.0, 20.8, 23.84, 25.6, 27.36, 28.12, 28.64, 25.52, 23.88, 23.96, 24.08, 24.0, 23.76]
+14.231730222702026
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 289181, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.686526536941528, 'TIME_S_1KI': 0.03695445598757016, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.68591009140016, 'W': 22.814226029486633}
+[20.16, 20.28, 20.16, 20.28, 20.4, 20.24, 20.28, 20.52, 20.68, 20.8, 20.4, 20.4, 20.6, 20.6, 20.56, 20.4, 20.16, 20.32, 20.56, 20.72]
+367.48
+18.374000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 289181, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.686526536941528, 'TIME_S_1KI': 0.03695445598757016, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 324.68591009140016, 'W': 22.814226029486633, 'J_1KI': 1.1227774649489426, 'W_1KI': 0.07889254836758512, 'W_D': 4.440226029486631, 'J_D': 63.1920989794731, 'W_D_1KI': 0.015354487429971649, 'J_D_1KI': 5.309646010620216e-05}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.json
index b76a936..5a829d2 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 690, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.003246307373047, "TIME_S_1KI": 21.74383522807688, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 736.2026811027528, "W": 51.180846035126734, "J_1KI": 1066.960407395294, "W_1KI": 74.1751391813431, "W_D": 35.27984603512673, "J_D": 507.4772937932015, "W_D_1KI": 51.1302116451112, "J_D_1KI": 74.10175600740754}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 681, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 12.767336130142212, "TIME_S_1KI": 18.74792383280795, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1572.8213312911987, "W": 66.33395904463319, "J_1KI": 2309.576110559763, "W_1KI": 97.40669463235417, "W_D": 47.20295904463319, "J_D": 1119.2128730852603, "W_D_1KI": 69.3141836191383, "J_D_1KI": 101.78294217200926}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.output
index 175c45d..e2fad62 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.520033359527588}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10 -ss 100000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.1540968418121338}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       9,      21,  ...,  999972,
-                             999987, 1000000]),
-       col_indices=tensor([ 5448,  9227, 13530,  ..., 69235, 78462, 82074]),
-       values=tensor([0.3723, 0.4802, 0.5484,  ..., 0.8279, 0.7827, 0.0235]),
+tensor(crow_indices=tensor([      0,       9,      17,  ...,  999985,
+                             999991, 1000000]),
+       col_indices=tensor([17175, 31207, 43376,  ..., 77066, 82322, 87573]),
+       values=tensor([0.5674, 0.0287, 0.4379,  ..., 0.7433, 0.7100, 0.4913]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9343, 0.5585, 0.5996,  ..., 0.1233, 0.3217, 0.3044])
+tensor([0.0850, 0.1151, 0.7763,  ..., 0.6909, 0.3596, 0.4186])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 1.520033359527588 seconds
+Time: 0.1540968418121338 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 690 -ss 100000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.003246307373047}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 681 -ss 100000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 12.767336130142212}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      11,      20,  ...,  999982,
-                             999992, 1000000]),
-       col_indices=tensor([  122,  3109, 10697,  ..., 57820, 90383, 91253]),
-       values=tensor([0.9003, 0.9806, 0.3302,  ..., 0.9034, 0.0249, 0.7877]),
+tensor(crow_indices=tensor([      0,      13,      35,  ...,  999978,
+                             999995, 1000000]),
+       col_indices=tensor([11566, 25133, 27464,  ..., 53113, 69352, 69486]),
+       values=tensor([0.0574, 0.1837, 0.1783,  ..., 0.8977, 0.9517, 0.8812]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6863, 0.9695, 0.8316,  ..., 0.5738, 0.3295, 0.1413])
+tensor([0.1378, 0.0464, 0.2618,  ..., 0.0793, 0.9401, 0.6278])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 15.003246307373047 seconds
+Time: 12.767336130142212 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      11,      20,  ...,  999982,
-                             999992, 1000000]),
-       col_indices=tensor([  122,  3109, 10697,  ..., 57820, 90383, 91253]),
-       values=tensor([0.9003, 0.9806, 0.3302,  ..., 0.9034, 0.0249, 0.7877]),
+tensor(crow_indices=tensor([      0,      13,      35,  ...,  999978,
+                             999995, 1000000]),
+       col_indices=tensor([11566, 25133, 27464,  ..., 53113, 69352, 69486]),
+       values=tensor([0.0574, 0.1837, 0.1783,  ..., 0.8977, 0.9517, 0.8812]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6863, 0.9695, 0.8316,  ..., 0.5738, 0.3295, 0.1413])
+tensor([0.1378, 0.0464, 0.2618,  ..., 0.0793, 0.9401, 0.6278])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 15.003246307373047 seconds
+Time: 12.767336130142212 seconds
 
-[17.56, 17.56, 17.72, 17.72, 17.72, 17.92, 17.92, 17.64, 17.48, 17.56]
-[17.24, 17.28, 18.12, 18.12, 20.28, 29.36, 44.04, 63.04, 74.68, 91.04, 91.28, 91.4, 88.68, 90.76]
-14.384339809417725
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 690, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.003246307373047, 'TIME_S_1KI': 21.74383522807688, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 736.2026811027528, 'W': 51.180846035126734}
-[17.56, 17.56, 17.72, 17.72, 17.72, 17.92, 17.92, 17.64, 17.48, 17.56, 17.84, 17.84, 17.6, 17.92, 17.8, 17.8, 17.68, 17.52, 17.16, 17.08]
-318.02000000000004
-15.901000000000002
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 690, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.003246307373047, 'TIME_S_1KI': 21.74383522807688, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 736.2026811027528, 'W': 51.180846035126734, 'J_1KI': 1066.960407395294, 'W_1KI': 74.1751391813431, 'W_D': 35.27984603512673, 'J_D': 507.4772937932015, 'W_D_1KI': 51.1302116451112, 'J_D_1KI': 74.10175600740754}
+[21.08, 21.24, 21.48, 21.4, 21.28, 21.32, 21.36, 21.36, 21.08, 20.88]
+[20.96, 20.76, 20.76, 21.96, 23.16, 31.64, 49.56, 62.28, 78.52, 91.0, 89.12, 89.6, 88.56, 88.56, 88.4, 87.48, 87.84, 87.84, 88.64, 87.08, 87.56, 86.84, 86.88]
+23.71065068244934
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 681, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 12.767336130142212, 'TIME_S_1KI': 18.74792383280795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1572.8213312911987, 'W': 66.33395904463319}
+[21.08, 21.24, 21.48, 21.4, 21.28, 21.32, 21.36, 21.36, 21.08, 20.88, 21.6, 21.4, 21.16, 20.96, 20.84, 20.92, 21.2, 21.48, 21.64, 21.44]
+382.62
+19.131
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 681, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 12.767336130142212, 'TIME_S_1KI': 18.74792383280795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1572.8213312911987, 'W': 66.33395904463319, 'J_1KI': 2309.576110559763, 'W_1KI': 97.40669463235417, 'W_D': 47.20295904463319, 'J_D': 1119.2128730852603, 'W_D_1KI': 69.3141836191383, 'J_D_1KI': 101.78294217200926}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.json
index a46b44b..c18ffcf 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 14.453001976013184, "TIME_S_1KI": 144.53001976013184, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1049.84748960495, "W": 50.98554654857658, "J_1KI": 10498.4748960495, "W_1KI": 509.85546548576576, "W_D": 35.17954654857658, "J_D": 724.3848723731041, "W_D_1KI": 351.7954654857658, "J_D_1KI": 3517.954654857658}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 90, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 14.579551696777344, "TIME_S_1KI": 161.9950188530816, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1234.5135778713227, "W": 56.902850471974645, "J_1KI": 13716.817531903585, "W_1KI": 632.2538941330516, "W_D": 37.990850471974646, "J_D": 824.2156650066377, "W_D_1KI": 422.12056079971825, "J_D_1KI": 4690.228453330203}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.output
index f3d22b6..e9c004f 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 14.453001976013184}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10 -ss 100000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 1.1569969654083252}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      101,      216,  ...,  9999796,
-                             9999906, 10000000]),
-       col_indices=tensor([ 2079,  2370,  2404,  ..., 91560, 92604, 94393]),
-       values=tensor([0.9041, 0.3243, 0.4250,  ..., 0.8376, 0.1046, 0.5896]),
+tensor(crow_indices=tensor([       0,      107,      211,  ...,  9999805,
+                             9999901, 10000000]),
+       col_indices=tensor([  835,   870,  1184,  ..., 95475, 95735, 97798]),
+       values=tensor([0.3341, 0.5436, 0.0587,  ..., 0.4495, 0.2234, 0.1531]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3984, 0.6112, 0.9728,  ..., 0.2358, 0.4123, 0.2200])
+tensor([0.0457, 0.8936, 0.6535,  ..., 0.4171, 0.3043, 0.4939])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,16 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 14.453001976013184 seconds
+Time: 1.1569969654083252 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 90 -ss 100000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 14.579551696777344}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      101,      216,  ...,  9999796,
-                             9999906, 10000000]),
-       col_indices=tensor([ 2079,  2370,  2404,  ..., 91560, 92604, 94393]),
-       values=tensor([0.9041, 0.3243, 0.4250,  ..., 0.8376, 0.1046, 0.5896]),
+tensor(crow_indices=tensor([       0,       86,      187,  ...,  9999810,
+                             9999912, 10000000]),
+       col_indices=tensor([  586,   606,  2288,  ..., 97134, 98615, 99354]),
+       values=tensor([0.0352, 0.3503, 0.4758,  ..., 0.8606, 0.4637, 0.4341]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.3984, 0.6112, 0.9728,  ..., 0.2358, 0.4123, 0.2200])
+tensor([0.1404, 0.2606, 0.7055,  ..., 0.0689, 0.1209, 0.4700])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -33,13 +36,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 14.453001976013184 seconds
+Time: 14.579551696777344 seconds
 
-[17.32, 17.28, 17.28, 17.36, 17.64, 17.72, 17.68, 17.92, 17.88, 17.72]
-[17.76, 17.96, 18.52, 20.36, 21.52, 25.36, 25.36, 31.2, 31.76, 45.88, 58.28, 65.88, 77.32, 84.48, 83.76, 84.4, 84.16, 84.96, 85.8, 86.16]
-20.5910804271698
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 14.453001976013184, 'TIME_S_1KI': 144.53001976013184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.84748960495, 'W': 50.98554654857658}
-[17.32, 17.28, 17.28, 17.36, 17.64, 17.72, 17.68, 17.92, 17.88, 17.72, 17.16, 17.24, 17.32, 17.32, 17.44, 17.8, 17.76, 17.76, 17.72, 17.8]
-316.12
-15.806000000000001
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 14.453001976013184, 'TIME_S_1KI': 144.53001976013184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.84748960495, 'W': 50.98554654857658, 'J_1KI': 10498.4748960495, 'W_1KI': 509.85546548576576, 'W_D': 35.17954654857658, 'J_D': 724.3848723731041, 'W_D_1KI': 351.7954654857658, 'J_D_1KI': 3517.954654857658}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       86,      187,  ...,  9999810,
+                             9999912, 10000000]),
+       col_indices=tensor([  586,   606,  2288,  ..., 97134, 98615, 99354]),
+       values=tensor([0.0352, 0.3503, 0.4758,  ..., 0.8606, 0.4637, 0.4341]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1404, 0.2606, 0.7055,  ..., 0.0689, 0.1209, 0.4700])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 14.579551696777344 seconds
+
+[21.08, 21.12, 21.04, 21.04, 20.92, 20.92, 21.04, 20.8, 21.0, 21.24]
+[21.24, 21.04, 21.08, 24.52, 26.88, 28.96, 37.92, 39.28, 42.44, 52.92, 63.52, 70.12, 81.96, 84.36, 86.44, 88.32, 87.92, 87.92, 88.28, 87.76, 85.56]
+21.695109605789185
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 90, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 14.579551696777344, 'TIME_S_1KI': 161.9950188530816, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1234.5135778713227, 'W': 56.902850471974645}
+[21.08, 21.12, 21.04, 21.04, 20.92, 20.92, 21.04, 20.8, 21.0, 21.24, 20.92, 21.04, 20.88, 20.88, 20.84, 20.96, 20.92, 21.08, 21.36, 21.56]
+378.24
+18.912
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 90, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 14.579551696777344, 'TIME_S_1KI': 161.9950188530816, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1234.5135778713227, 'W': 56.902850471974645, 'J_1KI': 13716.817531903585, 'W_1KI': 632.2538941330516, 'W_D': 37.990850471974646, 'J_D': 824.2156650066377, 'W_D_1KI': 422.12056079971825, 'J_D_1KI': 4690.228453330203}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..89d2769
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 15, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.035902976989746, "TIME_S_1KI": 669.060198465983, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1645.6200160980222, "W": 31.002306455007126, "J_1KI": 109708.00107320149, "W_1KI": 2066.8204303338084, "W_D": 12.343306455007127, "J_D": 655.1897097291944, "W_D_1KI": 822.8870970004751, "J_D_1KI": 54859.13980003168}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..debf383
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10 -ss 100000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 6.8235437870025635}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      1028,      2081,  ...,
+                             99997896,  99998957, 100000000]),
+       col_indices=tensor([  124,   166,   183,  ..., 99846, 99876, 99948]),
+       values=tensor([0.8465, 0.2478, 0.2733,  ..., 0.4852, 0.2351, 0.0898]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.2002, 0.6847, 0.0655,  ..., 0.6857, 0.4421, 0.6041])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 6.8235437870025635 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 15 -ss 100000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.035902976989746}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      1018,      2029,  ...,
+                             99998012,  99999042, 100000000]),
+       col_indices=tensor([  135,   301,   599,  ..., 99852, 99911, 99921]),
+       values=tensor([0.1942, 0.0224, 0.2187,  ..., 0.1366, 0.5257, 0.1941]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.8770, 0.9277, 0.4956,  ..., 0.2234, 0.3583, 0.8131])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 10.035902976989746 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      1018,      2029,  ...,
+                             99998012,  99999042, 100000000]),
+       col_indices=tensor([  135,   301,   599,  ..., 99852, 99911, 99921]),
+       values=tensor([0.1942, 0.0224, 0.2187,  ..., 0.1366, 0.5257, 0.1941]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.8770, 0.9277, 0.4956,  ..., 0.2234, 0.3583, 0.8131])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 10.035902976989746 seconds
+
+[20.64, 20.72, 20.76, 20.84, 20.84, 20.64, 20.72, 20.72, 20.52, 20.36]
+[20.56, 20.72, 20.88, 23.44, 24.24, 25.24, 26.4, 26.8, 25.64, 25.92, 27.36, 27.36, 29.2, 31.08, 30.84, 30.0, 28.04, 26.32, 26.08, 26.72, 29.6, 42.32, 51.04, 50.16, 49.24, 44.16, 44.16, 28.4, 28.12, 28.12, 27.28, 27.0, 26.12, 25.4, 25.28, 25.32, 25.4, 25.56, 25.56, 25.84, 26.12, 26.16, 26.2, 30.2, 33.96, 41.44, 47.56, 51.68, 53.48, 55.56, 53.04, 57.84]
+53.080567359924316
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 15, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.035902976989746, 'TIME_S_1KI': 669.060198465983, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1645.6200160980222, 'W': 31.002306455007126}
+[20.64, 20.72, 20.76, 20.84, 20.84, 20.64, 20.72, 20.72, 20.52, 20.36, 20.8, 20.68, 20.68, 20.48, 20.52, 20.56, 20.64, 21.16, 21.16, 21.28]
+373.18
+18.659
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 15, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.035902976989746, 'TIME_S_1KI': 669.060198465983, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1645.6200160980222, 'W': 31.002306455007126, 'J_1KI': 109708.00107320149, 'W_1KI': 2066.8204303338084, 'W_D': 12.343306455007127, 'J_D': 655.1897097291944, 'W_D_1KI': 822.8870970004751, 'J_D_1KI': 54859.13980003168}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.json
index c8834ef..078e111 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4862, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 16.123133897781372, "TIME_S_1KI": 3.316152591069801, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1103.8810626983643, "W": 59.41987873604025, "J_1KI": 227.04258796757802, "W_1KI": 12.221283162492854, "W_D": 43.51287873604025, "J_D": 808.3665574879647, "W_D_1KI": 8.949584273146904, "J_D_1KI": 1.8407207472535796}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4377, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.716068029403687, "TIME_S_1KI": 2.6767347565464217, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 931.1554330062867, "W": 56.32022952696994, "J_1KI": 212.73827576108903, "W_1KI": 12.867313120166767, "W_D": 37.50822952696994, "J_D": 620.1322686328889, "W_D_1KI": 8.569392169744102, "J_D_1KI": 1.9578232053333566}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.output
index 7c14ca6..abd8fec 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.3613910675048828}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10 -ss 100000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.028092145919799805}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      2,  ...,  99999, 100000,
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99999,  99999,
                             100000]),
-       col_indices=tensor([27177, 91140, 35351,  ..., 36842, 63353, 40213]),
-       values=tensor([0.8085, 0.0220, 0.0238,  ..., 0.9528, 0.8072, 0.8356]),
+       col_indices=tensor([91606, 44856, 81980,  ..., 58749, 11667, 65996]),
+       values=tensor([0.6228, 0.5426, 0.7214,  ..., 0.2159, 0.4095, 0.9061]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.5173, 0.6337, 0.4770,  ..., 0.8095, 0.3804, 0.7829])
+tensor([0.3666, 0.3385, 0.5084,  ..., 0.0188, 0.4462, 0.5866])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 0.3613910675048828 seconds
+Time: 0.028092145919799805 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 2905 -ss 100000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.272582054138184}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3737 -ss 100000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.963433027267456}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      1,  ...,  99996,  99997,
+tensor(crow_indices=tensor([     0,      0,      3,  ...,  99998,  99999,
                             100000]),
-       col_indices=tensor([57428, 26674, 73957,  ..., 55311, 85675, 87326]),
-       values=tensor([0.1133, 0.1214, 0.0575,  ..., 0.3604, 0.8021, 0.3274]),
+       col_indices=tensor([12625, 71568, 82165,  ..., 56450, 55484, 67350]),
+       values=tensor([0.8626, 0.1693, 0.6451,  ..., 0.6303, 0.1406, 0.5137]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7862, 0.8717, 0.9240,  ..., 0.8758, 0.8236, 0.0748])
+tensor([0.7143, 0.3777, 0.4972,  ..., 0.3298, 0.4245, 0.8777])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 6.272582054138184 seconds
+Time: 8.963433027267456 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4862 -ss 100000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 16.123133897781372}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4377 -ss 100000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.716068029403687}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      3,  ...,  99999, 100000,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998,  99999,
                             100000]),
-       col_indices=tensor([36017, 48507, 97216,  ..., 44545, 10809,  6488]),
-       values=tensor([0.2196, 0.1379, 0.1607,  ..., 0.1720, 0.9833, 0.1649]),
+       col_indices=tensor([14042, 63263, 67415,  ...,  5690, 28440,  2947]),
+       values=tensor([0.6549, 0.8223, 0.8286,  ..., 0.0757, 0.1191, 0.8433]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6606, 0.9945, 0.6366,  ..., 0.7352, 0.9827, 0.2989])
+tensor([0.4605, 0.0591, 0.4857,  ..., 0.9698, 0.5094, 0.1166])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 16.123133897781372 seconds
+Time: 11.716068029403687 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      3,  ...,  99999, 100000,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99998,  99999,
                             100000]),
-       col_indices=tensor([36017, 48507, 97216,  ..., 44545, 10809,  6488]),
-       values=tensor([0.2196, 0.1379, 0.1607,  ..., 0.1720, 0.9833, 0.1649]),
+       col_indices=tensor([14042, 63263, 67415,  ...,  5690, 28440,  2947]),
+       values=tensor([0.6549, 0.8223, 0.8286,  ..., 0.0757, 0.1191, 0.8433]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6606, 0.9945, 0.6366,  ..., 0.7352, 0.9827, 0.2989])
+tensor([0.4605, 0.0591, 0.4857,  ..., 0.9698, 0.5094, 0.1166])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 16.123133897781372 seconds
+Time: 11.716068029403687 seconds
 
-[17.72, 17.76, 17.68, 17.88, 17.72, 17.52, 17.6, 17.48, 17.6, 17.6]
-[17.72, 17.72, 17.8, 21.08, 22.76, 35.32, 49.56, 66.44, 76.68, 89.56, 88.0, 87.44, 86.52, 85.84, 85.76, 86.6, 86.04, 85.28]
-18.57763910293579
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4862, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 16.123133897781372, 'TIME_S_1KI': 3.316152591069801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1103.8810626983643, 'W': 59.41987873604025}
-[17.72, 17.76, 17.68, 17.88, 17.72, 17.52, 17.6, 17.48, 17.6, 17.6, 17.8, 17.8, 17.76, 17.68, 17.76, 17.88, 17.72, 17.48, 17.52, 17.48]
-318.14
-15.907
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4862, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 16.123133897781372, 'TIME_S_1KI': 3.316152591069801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1103.8810626983643, 'W': 59.41987873604025, 'J_1KI': 227.04258796757802, 'W_1KI': 12.221283162492854, 'W_D': 43.51287873604025, 'J_D': 808.3665574879647, 'W_D_1KI': 8.949584273146904, 'J_D_1KI': 1.8407207472535796}
+[20.72, 20.72, 21.04, 21.12, 21.0, 20.84, 20.76, 20.64, 20.76, 20.88]
+[20.68, 20.6, 21.76, 23.12, 29.64, 29.64, 46.92, 60.56, 75.84, 88.72, 89.44, 88.68, 87.76, 85.84, 84.76, 84.72]
+16.53323221206665
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4377, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.716068029403687, 'TIME_S_1KI': 2.6767347565464217, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 931.1554330062867, 'W': 56.32022952696994}
+[20.72, 20.72, 21.04, 21.12, 21.0, 20.84, 20.76, 20.64, 20.76, 20.88, 21.2, 21.0, 21.0, 20.96, 20.88, 20.88, 21.0, 20.84, 20.96, 20.88]
+376.24
+18.812
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4377, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.716068029403687, 'TIME_S_1KI': 2.6767347565464217, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 931.1554330062867, 'W': 56.32022952696994, 'J_1KI': 212.73827576108903, 'W_1KI': 12.867313120166767, 'W_D': 37.50822952696994, 'J_D': 620.1322686328889, 'W_D_1KI': 8.569392169744102, 'J_D_1KI': 1.9578232053333566}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_5e-05.json
index 0831cb8..a8d877c 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_5e-05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1295, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 13.363306522369385, "TIME_S_1KI": 10.319155615729255, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1148.1477067947387, "W": 58.62479671787461, "J_1KI": 886.600545787443, "W_1KI": 45.27011329565607, "W_D": 42.857796717874606, "J_D": 839.3561048357486, "W_D_1KI": 33.09482372036649, "J_D_1KI": 25.55584843271544}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 978, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.565504789352417, "TIME_S_1KI": 10.80317463123969, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1068.5367103862764, "W": 60.39816254634489, "J_1KI": 1092.5733235033501, "W_1KI": 61.75681241957555, "W_D": 41.59016254634489, "J_D": 735.7941632347108, "W_D_1KI": 42.52572857499478, "J_D_1KI": 43.482340056231884}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_5e-05.output
index fe6c43d..7837d89 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_5e-05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_100000_5e-05.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.8107287883758545}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10 -ss 100000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.14230060577392578}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,     12,  ..., 499988, 499996,
+tensor(crow_indices=tensor([     0,      8,     11,  ..., 499993, 499997,
                             500000]),
-       col_indices=tensor([ 2924,  5581, 32898,  ..., 25573, 35176, 44980]),
-       values=tensor([0.4338, 0.2090, 0.8667,  ..., 0.7968, 0.4161, 0.0285]),
+       col_indices=tensor([ 9796, 23171, 67855,  ..., 67066, 76742, 83085]),
+       values=tensor([0.7201, 0.1273, 0.8027,  ..., 0.9756, 0.6707, 0.8283]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.7311, 0.8750, 0.4611,  ..., 0.2473, 0.3600, 0.7684])
+tensor([0.4403, 0.8898, 0.0117,  ..., 0.2918, 0.2383, 0.8005])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 0.8107287883758545 seconds
+Time: 0.14230060577392578 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1295 -ss 100000 -sd 5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 13.363306522369385}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 737 -ss 100000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.9050374031066895}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      7,  ..., 499992, 499997,
+tensor(crow_indices=tensor([     0,      6,     10,  ..., 499991, 499994,
                             500000]),
-       col_indices=tensor([20312, 45798, 57469,  ...,  9915, 72511, 98823]),
-       values=tensor([0.2450, 0.9842, 0.2161,  ..., 0.8948, 0.9103, 0.6478]),
+       col_indices=tensor([ 6674, 21503, 46805,  ..., 26719, 29980, 87585]),
+       values=tensor([0.5980, 0.3045, 0.5949,  ..., 0.7519, 0.5339, 0.5740]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.2448, 0.6389, 0.3272,  ..., 0.3843, 0.8480, 0.1017])
+tensor([0.4906, 0.6526, 0.2791,  ..., 0.1176, 0.6105, 0.9789])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 13.363306522369385 seconds
+Time: 7.9050374031066895 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 978 -ss 100000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.565504789352417}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      7,  ..., 499992, 499997,
+tensor(crow_indices=tensor([     0,      6,     10,  ..., 499990, 499995,
                             500000]),
-       col_indices=tensor([20312, 45798, 57469,  ...,  9915, 72511, 98823]),
-       values=tensor([0.2450, 0.9842, 0.2161,  ..., 0.8948, 0.9103, 0.6478]),
+       col_indices=tensor([ 8342, 20491, 53690,  ..., 28124, 41534, 52404]),
+       values=tensor([0.2581, 0.1900, 0.2922,  ..., 0.1401, 0.2546, 0.0818]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.2448, 0.6389, 0.3272,  ..., 0.3843, 0.8480, 0.1017])
+tensor([0.6836, 0.9613, 0.4530,  ..., 0.7394, 0.7530, 0.5065])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 13.363306522369385 seconds
+Time: 10.565504789352417 seconds
 
-[17.48, 17.6, 17.56, 17.72, 17.52, 17.48, 17.56, 17.6, 17.6, 17.8]
-[18.0, 17.84, 17.76, 18.88, 19.6, 35.4, 49.36, 64.12, 77.28, 86.8, 84.84, 85.28, 83.64, 83.64, 82.96, 82.4, 81.96, 80.44, 80.16]
-19.58467698097229
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1295, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 13.363306522369385, 'TIME_S_1KI': 10.319155615729255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1148.1477067947387, 'W': 58.62479671787461}
-[17.48, 17.6, 17.56, 17.72, 17.52, 17.48, 17.56, 17.6, 17.6, 17.8, 17.64, 17.44, 17.36, 17.52, 17.36, 17.2, 17.52, 17.44, 17.6, 17.6]
-315.34
-15.767
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1295, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 13.363306522369385, 'TIME_S_1KI': 10.319155615729255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1148.1477067947387, 'W': 58.62479671787461, 'J_1KI': 886.600545787443, 'W_1KI': 45.27011329565607, 'W_D': 42.857796717874606, 'J_D': 839.3561048357486, 'W_D_1KI': 33.09482372036649, 'J_D_1KI': 25.55584843271544}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     10,  ..., 499990, 499995,
+                            500000]),
+       col_indices=tensor([ 8342, 20491, 53690,  ..., 28124, 41534, 52404]),
+       values=tensor([0.2581, 0.1900, 0.2922,  ..., 0.1401, 0.2546, 0.0818]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.6836, 0.9613, 0.4530,  ..., 0.7394, 0.7530, 0.5065])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.565504789352417 seconds
+
+[20.72, 20.72, 20.84, 20.8, 20.96, 21.12, 21.04, 21.04, 21.08, 21.0]
+[21.04, 21.12, 24.28, 25.44, 29.92, 47.24, 47.24, 60.52, 73.88, 86.68, 91.28, 89.52, 88.64, 89.4, 88.52, 88.48, 88.84]
+17.691543340682983
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 978, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.565504789352417, 'TIME_S_1KI': 10.80317463123969, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1068.5367103862764, 'W': 60.39816254634489}
+[20.72, 20.72, 20.84, 20.8, 20.96, 21.12, 21.04, 21.04, 21.08, 21.0, 20.96, 20.96, 20.8, 20.56, 20.76, 20.92, 20.8, 20.8, 21.04, 21.16]
+376.16
+18.808
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 978, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.565504789352417, 'TIME_S_1KI': 10.80317463123969, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1068.5367103862764, 'W': 60.39816254634489, 'J_1KI': 1092.5733235033501, 'W_1KI': 61.75681241957555, 'W_D': 41.59016254634489, 'J_D': 735.7941632347108, 'W_D_1KI': 42.52572857499478, 'J_D_1KI': 43.482340056231884}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.json
index 30708d9..d2c9447 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 33188, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.848255395889282, "TIME_S_1KI": 0.3268728274041606, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 283.2797647285461, "W": 19.966696619139725, "J_1KI": 8.535608193580394, "W_1KI": 0.6016239791231688, "W_D": 4.899696619139723, "J_D": 69.51499950075144, "W_D_1KI": 0.14763458536638915, "J_D_1KI": 0.0044484327276843785}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 32642, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.62722635269165, "TIME_S_1KI": 0.32556909358163255, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 316.55393952369684, "W": 22.276847086365876, "J_1KI": 9.697749510559918, "W_1KI": 0.6824596252179975, "W_D": 3.922847086365877, "J_D": 55.743646958827945, "W_D_1KI": 0.12017790228435381, "J_D_1KI": 0.0036816954317858528}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.output
index ce5f5e4..6982470 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.039971351623535156}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.040006160736083984}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ...,  9999,  9999, 10000]),
-       col_indices=tensor([5709, 5957, 5382,  ..., 8260, 3428, 9778]),
-       values=tensor([0.1410, 0.8327, 0.5618,  ..., 0.6127, 0.7950, 0.7693]),
+tensor(crow_indices=tensor([    0,     1,     3,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([1340, 1915, 9033,  ...,  503, 5965, 6883]),
+       values=tensor([0.0083, 0.0540, 0.2304,  ..., 0.4985, 0.4495, 0.5188]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.9182, 0.7141, 0.2679,  ..., 0.7671, 0.9231, 0.7252])
+tensor([0.8249, 0.8459, 0.0607,  ..., 0.1481, 0.6545, 0.4281])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 0.039971351623535156 seconds
+Time: 0.040006160736083984 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 26268 -ss 10000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.310471773147583}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 26245 -ss 10000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.442145824432373}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     2,  ...,  9999,  9999, 10000]),
-       col_indices=tensor([6011, 6260, 4075,  ..., 5576, 1824, 8975]),
-       values=tensor([0.9641, 0.0766, 0.9967,  ..., 0.9539, 0.3769, 0.8002]),
+tensor(crow_indices=tensor([    0,     1,     3,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([2860, 3463, 6973,  ..., 9469, 1415, 8396]),
+       values=tensor([0.6085, 0.7024, 0.8682,  ..., 0.8356, 0.2567, 0.3000]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.0022, 0.8912, 0.7670,  ..., 0.3905, 0.8453, 0.1961])
+tensor([0.0965, 0.5015, 0.6181,  ..., 0.3988, 0.7058, 0.2579])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 8.310471773147583 seconds
+Time: 8.442145824432373 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 33188 -ss 10000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.848255395889282}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 32642 -ss 10000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.62722635269165}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     4,  ...,  9995,  9997, 10000]),
-       col_indices=tensor([1948, 7195, 8876,  ...,  111, 6612, 9607]),
-       values=tensor([0.9860, 0.8888, 0.2852,  ..., 0.2300, 0.3266, 0.6773]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([9643, 3533, 4391,  ..., 7997, 4934, 5111]),
+       values=tensor([0.3528, 0.0869, 0.9026,  ..., 0.8307, 0.9649, 0.1279]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.8303, 0.1186, 0.7718,  ..., 0.9103, 0.1807, 0.5186])
+tensor([0.0038, 0.4961, 0.2678,  ..., 0.2768, 0.1846, 0.1025])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.848255395889282 seconds
+Time: 10.62722635269165 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     4,  ...,  9995,  9997, 10000]),
-       col_indices=tensor([1948, 7195, 8876,  ...,  111, 6612, 9607]),
-       values=tensor([0.9860, 0.8888, 0.2852,  ..., 0.2300, 0.3266, 0.6773]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([9643, 3533, 4391,  ..., 7997, 4934, 5111]),
+       values=tensor([0.3528, 0.0869, 0.9026,  ..., 0.8307, 0.9649, 0.1279]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.8303, 0.1186, 0.7718,  ..., 0.9103, 0.1807, 0.5186])
+tensor([0.0038, 0.4961, 0.2678,  ..., 0.2768, 0.1846, 0.1025])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.848255395889282 seconds
+Time: 10.62722635269165 seconds
 
-[17.16, 17.24, 17.28, 17.28, 17.12, 16.8, 16.4, 16.48, 16.32, 16.6]
-[16.76, 16.84, 20.04, 22.12, 23.96, 24.76, 24.76, 25.32, 22.2, 20.84, 19.88, 20.16, 20.24, 20.04]
-14.187613010406494
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 33188, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.848255395889282, 'TIME_S_1KI': 0.3268728274041606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 283.2797647285461, 'W': 19.966696619139725}
-[17.16, 17.24, 17.28, 17.28, 17.12, 16.8, 16.4, 16.48, 16.32, 16.6, 16.72, 16.48, 16.8, 16.64, 16.64, 16.68, 16.68, 16.56, 16.44, 16.52]
-301.34000000000003
-15.067000000000002
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 33188, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.848255395889282, 'TIME_S_1KI': 0.3268728274041606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 283.2797647285461, 'W': 19.966696619139725, 'J_1KI': 8.535608193580394, 'W_1KI': 0.6016239791231688, 'W_D': 4.899696619139723, 'J_D': 69.51499950075144, 'W_D_1KI': 0.14763458536638915, 'J_D_1KI': 0.0044484327276843785}
+[20.44, 20.4, 20.28, 20.6, 20.64, 20.8, 20.64, 20.64, 20.44, 20.08]
+[19.88, 19.96, 20.4, 23.88, 26.4, 27.16, 27.92, 25.4, 24.92, 23.48, 23.48, 23.52, 23.48, 23.4]
+14.209997415542603
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 32642, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.62722635269165, 'TIME_S_1KI': 0.32556909358163255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.55393952369684, 'W': 22.276847086365876}
+[20.44, 20.4, 20.28, 20.6, 20.64, 20.8, 20.64, 20.64, 20.44, 20.08, 20.36, 20.2, 20.16, 20.24, 20.28, 20.16, 20.28, 20.28, 20.4, 20.4]
+367.08
+18.354
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 32642, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.62722635269165, 'TIME_S_1KI': 0.32556909358163255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 316.55393952369684, 'W': 22.276847086365876, 'J_1KI': 9.697749510559918, 'W_1KI': 0.6824596252179975, 'W_D': 3.922847086365877, 'J_D': 55.743646958827945, 'W_D_1KI': 0.12017790228435381, 'J_D_1KI': 0.0036816954317858528}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.json
index 9b3c069..58fd1a6 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4682, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.500732183456421, "TIME_S_1KI": 2.2427877367484883, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 298.49395805358887, "W": 21.02945948615313, "J_1KI": 63.753515175905356, "W_1KI": 4.491554781322753, "W_D": 5.974459486153128, "J_D": 84.80199219703674, "W_D_1KI": 1.2760485873885365, "J_D_1KI": 0.27254348299627007}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4524, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.524961471557617, "TIME_S_1KI": 2.326472473819102, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 325.2464212036133, "W": 24.58634437533153, "J_1KI": 71.89355022184202, "W_1KI": 5.434647297818641, "W_D": 6.037344375331532, "J_D": 79.86647472572331, "W_D_1KI": 1.334514671823946, "J_D_1KI": 0.29498555964278206}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.output
index faf5980..f9d776f 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2965726852416992}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2320706844329834}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     10,     21,  ...,  99978,  99991,
+tensor(crow_indices=tensor([     0,      7,     19,  ...,  99974,  99985,
                             100000]),
-       col_indices=tensor([ 670, 2215, 2340,  ..., 5626, 6766, 7426]),
-       values=tensor([0.8454, 0.9971, 0.8602,  ..., 0.7286, 0.6853, 0.9650]),
+       col_indices=tensor([1906, 2047, 2463,  ..., 7748, 7892, 9833]),
+       values=tensor([0.5643, 0.8413, 0.3457,  ..., 0.8525, 0.1641, 0.7374]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8368, 0.0517, 0.8880,  ..., 0.1583, 0.3761, 0.3465])
+tensor([0.0072, 0.2945, 0.2791,  ..., 0.1876, 0.3163, 0.8664])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 0.2965726852416992 seconds
+Time: 0.2320706844329834 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3540 -ss 10000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.9380598068237305}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4524 -ss 10000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.524961471557617}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      9,     19,  ...,  99981,  99990,
+tensor(crow_indices=tensor([     0,     11,     23,  ...,  99980,  99990,
                             100000]),
-       col_indices=tensor([ 218, 1968, 1988,  ..., 6580, 8417, 9626]),
-       values=tensor([0.6495, 0.9774, 0.8878,  ..., 0.7618, 0.8151, 0.2290]),
+       col_indices=tensor([1014, 1917, 2238,  ..., 8617, 8839, 8958]),
+       values=tensor([0.4207, 0.3514, 0.4674,  ..., 0.3563, 0.0399, 0.1482]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.1753, 0.6932, 0.8031,  ..., 0.2090, 0.4929, 0.1708])
+tensor([0.4775, 0.1329, 0.4678,  ..., 0.3641, 0.6182, 0.5714])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 7.9380598068237305 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4682 -ss 10000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.500732183456421}
+Time: 10.524961471557617 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     20,  ...,  99977,  99983,
+tensor(crow_indices=tensor([     0,     11,     23,  ...,  99980,  99990,
                             100000]),
-       col_indices=tensor([  32,  224, 1507,  ..., 8865, 9626, 9660]),
-       values=tensor([0.3062, 0.4385, 0.1947,  ..., 0.8116, 0.9937, 0.3510]),
+       col_indices=tensor([1014, 1917, 2238,  ..., 8617, 8839, 8958]),
+       values=tensor([0.4207, 0.3514, 0.4674,  ..., 0.3563, 0.0399, 0.1482]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0637, 0.2734, 0.9054,  ..., 0.1369, 0.8787, 0.2539])
+tensor([0.4775, 0.1329, 0.4678,  ..., 0.3641, 0.6182, 0.5714])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,30 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.500732183456421 seconds
+Time: 10.524961471557617 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     20,  ...,  99977,  99983,
-                            100000]),
-       col_indices=tensor([  32,  224, 1507,  ..., 8865, 9626, 9660]),
-       values=tensor([0.3062, 0.4385, 0.1947,  ..., 0.8116, 0.9937, 0.3510]),
-       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0637, 0.2734, 0.9054,  ..., 0.1369, 0.8787, 0.2539])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 100000
-Density: 0.001
-Time: 10.500732183456421 seconds
-
-[16.56, 16.28, 16.36, 16.32, 16.4, 16.32, 16.36, 16.44, 16.36, 16.28]
-[16.24, 15.96, 19.32, 19.32, 21.08, 28.0, 29.28, 30.04, 27.12, 25.96, 20.44, 20.04, 19.96, 19.96]
-14.194086074829102
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.500732183456421, 'TIME_S_1KI': 2.2427877367484883, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 298.49395805358887, 'W': 21.02945948615313}
-[16.56, 16.28, 16.36, 16.32, 16.4, 16.32, 16.36, 16.44, 16.36, 16.28, 16.52, 16.8, 17.04, 17.04, 17.16, 17.48, 17.4, 17.4, 16.84, 16.84]
-301.1
-15.055000000000001
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.500732183456421, 'TIME_S_1KI': 2.2427877367484883, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 298.49395805358887, 'W': 21.02945948615313, 'J_1KI': 63.753515175905356, 'W_1KI': 4.491554781322753, 'W_D': 5.974459486153128, 'J_D': 84.80199219703674, 'W_D_1KI': 1.2760485873885365, 'J_D_1KI': 0.27254348299627007}
+[20.36, 20.56, 20.56, 20.52, 20.68, 20.68, 20.56, 20.36, 20.8, 20.8]
+[20.64, 20.76, 20.76, 24.36, 25.84, 32.36, 33.28, 33.76, 30.6, 26.92, 24.52, 24.16, 24.16]
+13.228742599487305
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4524, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.524961471557617, 'TIME_S_1KI': 2.326472473819102, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.2464212036133, 'W': 24.58634437533153}
+[20.36, 20.56, 20.56, 20.52, 20.68, 20.68, 20.56, 20.36, 20.8, 20.8, 20.8, 20.8, 20.68, 20.72, 20.68, 20.52, 20.52, 20.56, 20.48, 20.64]
+370.98
+18.549
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4524, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.524961471557617, 'TIME_S_1KI': 2.326472473819102, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 325.2464212036133, 'W': 24.58634437533153, 'J_1KI': 71.89355022184202, 'W_1KI': 5.434647297818641, 'W_D': 6.037344375331532, 'J_D': 79.86647472572331, 'W_D_1KI': 1.334514671823946, 'J_D_1KI': 0.29498555964278206}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.json
index b135810..4a3134d 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 481, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.463550806045532, "TIME_S_1KI": 21.75374387951254, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.4283616256714, "W": 23.410421194984455, "J_1KI": 691.1192549390258, "W_1KI": 48.67031433468701, "W_D": 6.792421194984453, "J_D": 96.45249141454698, "W_D_1KI": 14.121457785830463, "J_D_1KI": 29.35854009528163}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 478, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.185055017471313, "TIME_S_1KI": 21.307646480065507, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 359.0441580104829, "W": 25.246291403734826, "J_1KI": 751.1384058796713, "W_1KI": 52.81650921283437, "W_D": 6.905291403734825, "J_D": 98.20470255303393, "W_D_1KI": 14.446216325805073, "J_D_1KI": 30.222209886621492}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.output
index bf59b23..7774b9b 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.1813583374023438}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.1943562030792236}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     110,     205,  ...,  999812,
-                             999909, 1000000]),
-       col_indices=tensor([ 238,  361,  384,  ..., 9612, 9634, 9698]),
-       values=tensor([0.5750, 0.8151, 0.3516,  ..., 0.9302, 0.8544, 0.3311]),
+tensor(crow_indices=tensor([      0,      86,     175,  ...,  999809,
+                             999908, 1000000]),
+       col_indices=tensor([ 216,  253,  262,  ..., 9562, 9600, 9698]),
+       values=tensor([0.0465, 0.0516, 0.1303,  ..., 0.2073, 0.6303, 0.1025]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.6330, 0.0586, 0.5281,  ..., 0.1634, 0.9727, 0.9265])
+tensor([0.5186, 0.3626, 0.2367,  ..., 0.6864, 0.2085, 0.5807])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 2.1813583374023438 seconds
+Time: 2.1943562030792236 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 481 -ss 10000 -sd 0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.463550806045532}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 478 -ss 10000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.185055017471313}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      92,     212,  ...,  999818,
-                             999919, 1000000]),
-       col_indices=tensor([ 159,  190,  205,  ..., 9628, 9649, 9961]),
-       values=tensor([0.6162, 0.4289, 0.4486,  ..., 0.9461, 0.6549, 0.6632]),
+tensor(crow_indices=tensor([      0,      86,     179,  ...,  999806,
+                             999912, 1000000]),
+       col_indices=tensor([  78,  134,  234,  ..., 9468, 9789, 9980]),
+       values=tensor([0.0445, 0.5511, 0.1857,  ..., 0.1042, 0.0198, 0.6991]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8230, 0.2220, 0.9348,  ..., 0.2779, 0.8915, 0.3439])
+tensor([0.2366, 0.8915, 0.1308,  ..., 0.5580, 0.2955, 0.4245])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.463550806045532 seconds
+Time: 10.185055017471313 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      92,     212,  ...,  999818,
-                             999919, 1000000]),
-       col_indices=tensor([ 159,  190,  205,  ..., 9628, 9649, 9961]),
-       values=tensor([0.6162, 0.4289, 0.4486,  ..., 0.9461, 0.6549, 0.6632]),
+tensor(crow_indices=tensor([      0,      86,     179,  ...,  999806,
+                             999912, 1000000]),
+       col_indices=tensor([  78,  134,  234,  ..., 9468, 9789, 9980]),
+       values=tensor([0.0445, 0.5511, 0.1857,  ..., 0.1042, 0.0198, 0.6991]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8230, 0.2220, 0.9348,  ..., 0.2779, 0.8915, 0.3439])
+tensor([0.2366, 0.8915, 0.1308,  ..., 0.5580, 0.2955, 0.4245])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.463550806045532 seconds
+Time: 10.185055017471313 seconds
 
-[20.24, 19.08, 17.84, 18.08, 19.12, 19.88, 20.84, 22.4, 22.4, 22.88]
-[22.68, 22.68, 21.44, 22.2, 23.24, 31.92, 32.08, 31.76, 30.52, 23.92, 21.88, 21.88, 22.0, 22.24]
-14.200016260147095
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 481, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.463550806045532, 'TIME_S_1KI': 21.75374387951254, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.4283616256714, 'W': 23.410421194984455}
-[20.24, 19.08, 17.84, 18.08, 19.12, 19.88, 20.84, 22.4, 22.4, 22.88, 16.72, 17.0, 17.08, 16.92, 17.04, 16.84, 16.64, 16.56, 16.56, 16.32]
-332.36
-16.618000000000002
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 481, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.463550806045532, 'TIME_S_1KI': 21.75374387951254, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.4283616256714, 'W': 23.410421194984455, 'J_1KI': 691.1192549390258, 'W_1KI': 48.67031433468701, 'W_D': 6.792421194984453, 'J_D': 96.45249141454698, 'W_D_1KI': 14.121457785830463, 'J_D_1KI': 29.35854009528163}
+[20.68, 20.56, 20.6, 20.68, 20.88, 20.68, 20.68, 20.72, 20.52, 20.56]
+[20.84, 20.88, 24.12, 26.0, 26.0, 33.56, 34.08, 34.64, 30.68, 29.36, 23.6, 24.0, 24.24, 24.2]
+14.221659421920776
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 478, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.185055017471313, 'TIME_S_1KI': 21.307646480065507, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 359.0441580104829, 'W': 25.246291403734826}
+[20.68, 20.56, 20.6, 20.68, 20.88, 20.68, 20.68, 20.72, 20.52, 20.56, 20.08, 19.96, 20.2, 20.28, 20.28, 20.04, 19.96, 20.0, 20.0, 20.24]
+366.82
+18.341
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 478, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.185055017471313, 'TIME_S_1KI': 21.307646480065507, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 359.0441580104829, 'W': 25.246291403734826, 'J_1KI': 751.1384058796713, 'W_1KI': 52.81650921283437, 'W_D': 6.905291403734825, 'J_D': 98.20470255303393, 'W_D_1KI': 14.446216325805073, 'J_D_1KI': 30.222209886621492}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.json
index 18a5242..3f166c3 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 11.54654312133789, "TIME_S_1KI": 115.4654312133789, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 353.263671875, "W": 21.71035974754772, "J_1KI": 3532.63671875, "W_1KI": 217.1035974754772, "W_D": 6.712359747547719, "J_D": 109.22126021575927, "W_D_1KI": 67.1235974754772, "J_D_1KI": 671.235974754772}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.613454580307007, "TIME_S_1KI": 106.13454580307007, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 404.4998287200927, "W": 24.764895943428325, "J_1KI": 4044.998287200927, "W_1KI": 247.64895943428326, "W_D": 6.426895943428324, "J_D": 104.97432794618597, "W_D_1KI": 64.26895943428323, "J_D_1KI": 642.6895943428324}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.output
index d49136b..1d6440a 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 11.54654312133789}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.613454580307007}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     487,     979,  ..., 4998949,
-                            4999474, 5000000]),
-       col_indices=tensor([   0,    2,    9,  ..., 9960, 9969, 9987]),
-       values=tensor([0.4699, 0.5377, 0.9444,  ..., 0.8781, 0.2092, 0.8180]),
+tensor(crow_indices=tensor([      0,     466,     940,  ..., 4998981,
+                            4999488, 5000000]),
+       col_indices=tensor([   7,   62,   67,  ..., 9933, 9947, 9995]),
+       values=tensor([0.3280, 0.5982, 0.7566,  ..., 0.0214, 0.1608, 0.2417]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.7212, 0.1994, 0.8974,  ..., 0.2164, 0.6888, 0.0461])
+tensor([0.6482, 0.9620, 0.9641,  ..., 0.2268, 0.6878, 0.0467])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 11.54654312133789 seconds
+Time: 10.613454580307007 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     487,     979,  ..., 4998949,
-                            4999474, 5000000]),
-       col_indices=tensor([   0,    2,    9,  ..., 9960, 9969, 9987]),
-       values=tensor([0.4699, 0.5377, 0.9444,  ..., 0.8781, 0.2092, 0.8180]),
+tensor(crow_indices=tensor([      0,     466,     940,  ..., 4998981,
+                            4999488, 5000000]),
+       col_indices=tensor([   7,   62,   67,  ..., 9933, 9947, 9995]),
+       values=tensor([0.3280, 0.5982, 0.7566,  ..., 0.0214, 0.1608, 0.2417]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.7212, 0.1994, 0.8974,  ..., 0.2164, 0.6888, 0.0461])
+tensor([0.6482, 0.9620, 0.9641,  ..., 0.2268, 0.6878, 0.0467])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 11.54654312133789 seconds
+Time: 10.613454580307007 seconds
 
-[16.56, 16.48, 16.72, 16.72, 16.8, 16.8, 16.64, 16.52, 16.52, 16.48]
-[16.44, 16.64, 17.04, 18.24, 19.64, 27.0, 30.2, 30.0, 30.0, 30.08, 26.08, 22.8, 20.4, 20.48, 20.64, 20.48]
-16.271663665771484
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 11.54654312133789, 'TIME_S_1KI': 115.4654312133789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 353.263671875, 'W': 21.71035974754772}
-[16.56, 16.48, 16.72, 16.72, 16.8, 16.8, 16.64, 16.52, 16.52, 16.48, 16.52, 16.36, 16.6, 16.84, 16.68, 16.68, 16.68, 16.96, 16.76, 16.84]
-299.96000000000004
-14.998000000000001
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 11.54654312133789, 'TIME_S_1KI': 115.4654312133789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 353.263671875, 'W': 21.71035974754772, 'J_1KI': 3532.63671875, 'W_1KI': 217.1035974754772, 'W_D': 6.712359747547719, 'J_D': 109.22126021575927, 'W_D_1KI': 67.1235974754772, 'J_D_1KI': 671.235974754772}
+[20.4, 20.44, 20.2, 20.08, 20.12, 20.08, 20.2, 20.16, 20.4, 20.56]
+[20.8, 20.8, 20.88, 21.88, 22.92, 26.0, 32.08, 34.68, 34.76, 34.4, 27.36, 24.32, 24.48, 24.36, 24.64, 24.64]
+16.33359694480896
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.613454580307007, 'TIME_S_1KI': 106.13454580307007, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 404.4998287200927, 'W': 24.764895943428325}
+[20.4, 20.44, 20.2, 20.08, 20.12, 20.08, 20.2, 20.16, 20.4, 20.56, 20.68, 20.52, 20.68, 20.6, 20.4, 20.4, 20.32, 20.24, 20.64, 20.92]
+366.76
+18.338
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.613454580307007, 'TIME_S_1KI': 106.13454580307007, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 404.4998287200927, 'W': 24.764895943428325, 'J_1KI': 4044.998287200927, 'W_1KI': 247.64895943428326, 'W_D': 6.426895943428324, 'J_D': 104.97432794618597, 'W_D_1KI': 64.26895943428323, 'J_D_1KI': 642.6895943428324}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.json
index 94990e6..33437d6 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.129345178604126, "TIME_S_1KI": 211.29345178604126, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 619.1563564968109, "W": 21.836067076186225, "J_1KI": 6191.563564968109, "W_1KI": 218.36067076186225, "W_D": 6.581067076186224, "J_D": 186.6045519340038, "W_D_1KI": 65.81067076186224, "J_D_1KI": 658.1067076186224}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.31971502304077, "TIME_S_1KI": 213.19715023040771, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 743.9782962799072, "W": 25.26275875228295, "J_1KI": 7439.782962799072, "W_1KI": 252.6275875228295, "W_D": 6.6447587522829465, "J_D": 195.68552841711036, "W_D_1KI": 66.44758752282947, "J_D_1KI": 664.4758752282946}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.output
index 12caf81..fb5c8df 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.129345178604126}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.31971502304077}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      944,     1951,  ...,  9997982,
-                             9998990, 10000000]),
-       col_indices=tensor([  12,   17,   18,  ..., 9973, 9979, 9988]),
-       values=tensor([0.0454, 0.8748, 0.1892,  ..., 0.1417, 0.8974, 0.6226]),
+tensor(crow_indices=tensor([       0,     1001,     2011,  ...,  9998036,
+                             9998985, 10000000]),
+       col_indices=tensor([   6,    7,   22,  ..., 9960, 9965, 9984]),
+       values=tensor([0.5702, 0.0939, 0.6315,  ..., 0.0656, 0.0225, 0.3527]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.7518, 0.8666, 0.4972,  ..., 0.4338, 0.0856, 0.8852])
+tensor([0.2410, 0.7808, 0.4106,  ..., 0.9776, 0.6480, 0.7308])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 21.129345178604126 seconds
+Time: 21.31971502304077 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      944,     1951,  ...,  9997982,
-                             9998990, 10000000]),
-       col_indices=tensor([  12,   17,   18,  ..., 9973, 9979, 9988]),
-       values=tensor([0.0454, 0.8748, 0.1892,  ..., 0.1417, 0.8974, 0.6226]),
+tensor(crow_indices=tensor([       0,     1001,     2011,  ...,  9998036,
+                             9998985, 10000000]),
+       col_indices=tensor([   6,    7,   22,  ..., 9960, 9965, 9984]),
+       values=tensor([0.5702, 0.0939, 0.6315,  ..., 0.0656, 0.0225, 0.3527]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.7518, 0.8666, 0.4972,  ..., 0.4338, 0.0856, 0.8852])
+tensor([0.2410, 0.7808, 0.4106,  ..., 0.9776, 0.6480, 0.7308])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 21.129345178604126 seconds
+Time: 21.31971502304077 seconds
 
-[16.8, 16.76, 16.76, 16.76, 16.88, 16.92, 16.92, 17.0, 17.04, 17.04]
-[16.88, 17.04, 20.52, 21.6, 23.68, 23.68, 29.08, 30.56, 30.32, 30.08, 28.48, 23.96, 23.32, 20.92, 20.92, 21.08, 21.44, 21.44, 20.92, 20.72, 20.64, 20.4, 20.4, 20.56, 20.44, 20.36, 20.48, 20.68]
-28.354756116867065
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.129345178604126, 'TIME_S_1KI': 211.29345178604126, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 619.1563564968109, 'W': 21.836067076186225}
-[16.8, 16.76, 16.76, 16.76, 16.88, 16.92, 16.92, 17.0, 17.04, 17.04, 17.12, 17.28, 17.24, 17.24, 16.84, 16.64, 16.96, 16.72, 17.12, 17.08]
-305.1
-15.255
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.129345178604126, 'TIME_S_1KI': 211.29345178604126, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 619.1563564968109, 'W': 21.836067076186225, 'J_1KI': 6191.563564968109, 'W_1KI': 218.36067076186225, 'W_D': 6.581067076186224, 'J_D': 186.6045519340038, 'W_D_1KI': 65.81067076186224, 'J_D_1KI': 658.1067076186224}
+[20.36, 20.52, 20.36, 20.48, 20.68, 21.0, 21.24, 21.24, 21.16, 20.84]
+[20.52, 20.32, 20.44, 21.76, 22.64, 26.96, 33.24, 37.24, 37.04, 37.08, 28.88, 28.88, 27.96, 25.08, 25.04, 25.0, 24.72, 24.48, 24.16, 24.2, 24.12, 24.08, 24.36, 24.36, 24.52, 24.6, 24.56, 24.6, 24.64]
+29.44960618019104
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.31971502304077, 'TIME_S_1KI': 213.19715023040771, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 743.9782962799072, 'W': 25.26275875228295}
+[20.36, 20.52, 20.36, 20.48, 20.68, 21.0, 21.24, 21.24, 21.16, 20.84, 20.68, 20.64, 20.8, 20.8, 20.6, 20.48, 20.4, 20.24, 20.52, 20.52]
+372.36
+18.618000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.31971502304077, 'TIME_S_1KI': 213.19715023040771, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 743.9782962799072, 'W': 25.26275875228295, 'J_1KI': 7439.782962799072, 'W_1KI': 252.6275875228295, 'W_D': 6.6447587522829465, 'J_D': 195.68552841711036, 'W_D_1KI': 66.44758752282947, 'J_D_1KI': 664.4758752282946}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.2.json
index 364c8ea..68787ce 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.2.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.2.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.021928787231445, "TIME_S_1KI": 420.21928787231445, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1136.679587249756, "W": 21.603080734566593, "J_1KI": 11366.79587249756, "W_1KI": 216.03080734566592, "W_D": 6.324080734566593, "J_D": 332.751312992096, "W_D_1KI": 63.24080734566592, "J_D_1KI": 632.4080734566592}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.423949003219604, "TIME_S_1KI": 424.23949003219604, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1297.7673851776126, "W": 25.077883657212237, "J_1KI": 12977.673851776126, "W_1KI": 250.77883657212234, "W_D": 6.708883657212237, "J_D": 347.18122630643876, "W_D_1KI": 67.08883657212236, "J_D_1KI": 670.8883657212235}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.2.output
index b23f94c..add5f7d 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.2.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.2']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.021928787231445}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.423949003219604}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1977,     4005,  ..., 19996092,
-                            19998061, 20000000]),
-       col_indices=tensor([   0,   21,   29,  ..., 9987, 9990, 9994]),
-       values=tensor([0.7851, 0.1514, 0.2063,  ..., 0.8497, 0.4761, 0.4899]),
+tensor(crow_indices=tensor([       0,     1994,     3991,  ..., 19995993,
+                            19997995, 20000000]),
+       col_indices=tensor([   0,    1,    3,  ..., 9992, 9994, 9999]),
+       values=tensor([0.6041, 0.5243, 0.8741,  ..., 0.1407, 0.3932, 0.5137]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.7703, 0.2799, 0.3874,  ..., 0.1897, 0.4899, 0.7472])
+tensor([0.3259, 0.3007, 0.5756,  ..., 0.3180, 0.3051, 0.2793])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 42.021928787231445 seconds
+Time: 42.423949003219604 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1977,     4005,  ..., 19996092,
-                            19998061, 20000000]),
-       col_indices=tensor([   0,   21,   29,  ..., 9987, 9990, 9994]),
-       values=tensor([0.7851, 0.1514, 0.2063,  ..., 0.8497, 0.4761, 0.4899]),
+tensor(crow_indices=tensor([       0,     1994,     3991,  ..., 19995993,
+                            19997995, 20000000]),
+       col_indices=tensor([   0,    1,    3,  ..., 9992, 9994, 9999]),
+       values=tensor([0.6041, 0.5243, 0.8741,  ..., 0.1407, 0.3932, 0.5137]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.7703, 0.2799, 0.3874,  ..., 0.1897, 0.4899, 0.7472])
+tensor([0.3259, 0.3007, 0.5756,  ..., 0.3180, 0.3051, 0.2793])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 42.021928787231445 seconds
+Time: 42.423949003219604 seconds
 
-[16.84, 16.68, 16.68, 16.88, 16.96, 17.08, 17.12, 17.24, 17.0, 17.0]
-[16.76, 16.8, 19.84, 20.68, 22.88, 22.88, 24.36, 33.76, 31.92, 32.48, 31.68, 31.64, 25.4, 24.92, 23.64, 20.2, 20.04, 20.04, 20.04, 19.84, 20.12, 20.36, 20.72, 20.76, 20.48, 20.32, 20.4, 20.28, 20.76, 20.84, 20.84, 20.92, 21.0, 20.96, 20.8, 20.8, 20.84, 20.6, 20.56, 20.56, 20.84, 20.76, 20.76, 20.8, 21.0, 21.08, 20.8, 20.96, 20.72, 20.72, 20.72, 20.76]
-52.61655044555664
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.021928787231445, 'TIME_S_1KI': 420.21928787231445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1136.679587249756, 'W': 21.603080734566593}
-[16.84, 16.68, 16.68, 16.88, 16.96, 17.08, 17.12, 17.24, 17.0, 17.0, 17.2, 17.16, 17.08, 16.84, 16.84, 16.8, 17.0, 17.04, 17.16, 17.0]
-305.58
-15.279
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.021928787231445, 'TIME_S_1KI': 420.21928787231445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1136.679587249756, 'W': 21.603080734566593, 'J_1KI': 11366.79587249756, 'W_1KI': 216.03080734566592, 'W_D': 6.324080734566593, 'J_D': 332.751312992096, 'W_D_1KI': 63.24080734566592, 'J_D_1KI': 632.4080734566592}
+[20.44, 20.56, 20.28, 20.28, 20.12, 20.0, 20.16, 20.32, 20.52, 20.6]
+[20.52, 20.28, 20.92, 22.92, 23.88, 25.76, 34.6, 34.6, 35.28, 35.44, 36.72, 34.64, 28.64, 28.32, 26.96, 24.44, 24.28, 24.44, 24.32, 24.16, 24.16, 24.2, 24.36, 24.32, 24.52, 24.44, 24.44, 24.44, 24.28, 24.16, 24.2, 24.28, 24.28, 24.56, 24.52, 24.4, 24.44, 24.32, 24.16, 24.28, 24.28, 24.04, 23.88, 23.64, 23.68, 23.68, 24.12, 24.08, 24.32, 24.24, 24.16]
+51.74947786331177
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.423949003219604, 'TIME_S_1KI': 424.23949003219604, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1297.7673851776126, 'W': 25.077883657212237}
+[20.44, 20.56, 20.28, 20.28, 20.12, 20.0, 20.16, 20.32, 20.52, 20.6, 20.12, 20.16, 20.28, 20.44, 20.72, 20.68, 20.64, 20.72, 20.72, 20.4]
+367.38
+18.369
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.423949003219604, 'TIME_S_1KI': 424.23949003219604, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1297.7673851776126, 'W': 25.077883657212237, 'J_1KI': 12977.673851776126, 'W_1KI': 250.77883657212234, 'W_D': 6.708883657212237, 'J_D': 347.18122630643876, 'W_D_1KI': 67.08883657212236, 'J_D_1KI': 670.8883657212235}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.3.json
index f2ba0ed..87a99e2 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.3.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.3.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 63.62427043914795, "TIME_S_1KI": 636.2427043914795, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1670.8393529319762, "W": 21.725499941161722, "J_1KI": 16708.393529319765, "W_1KI": 217.2549994116172, "W_D": 6.518499941161723, "J_D": 501.3171735184193, "W_D_1KI": 65.18499941161723, "J_D_1KI": 651.8499941161723}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 63.51457595825195, "TIME_S_1KI": 635.1457595825195, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1889.5348065662383, "W": 24.82306850434245, "J_1KI": 18895.348065662383, "W_1KI": 248.23068504342453, "W_D": 6.464068504342453, "J_D": 492.04563202357303, "W_D_1KI": 64.64068504342453, "J_D_1KI": 646.4068504342453}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.3.output
index 6258d91..197fdaa 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.3.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.3']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 63.62427043914795}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 63.51457595825195}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2940,     5882,  ..., 29993962,
-                            29997042, 30000000]),
-       col_indices=tensor([   8,   14,   15,  ..., 9977, 9993, 9996]),
-       values=tensor([0.8256, 0.2654, 0.0882,  ..., 0.9659, 0.5243, 0.2720]),
+tensor(crow_indices=tensor([       0,     2997,     6036,  ..., 29994089,
+                            29997023, 30000000]),
+       col_indices=tensor([   3,    4,    5,  ..., 9990, 9992, 9998]),
+       values=tensor([0.6330, 0.2666, 0.3549,  ..., 0.7937, 0.6841, 0.3201]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.4780, 0.6736, 0.3887,  ..., 0.7294, 0.7408, 0.5543])
+tensor([0.0444, 0.4833, 0.9897,  ..., 0.1872, 0.1999, 0.3106])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,16 +16,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 63.62427043914795 seconds
+Time: 63.51457595825195 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2940,     5882,  ..., 29993962,
-                            29997042, 30000000]),
-       col_indices=tensor([   8,   14,   15,  ..., 9977, 9993, 9996]),
-       values=tensor([0.8256, 0.2654, 0.0882,  ..., 0.9659, 0.5243, 0.2720]),
+tensor(crow_indices=tensor([       0,     2997,     6036,  ..., 29994089,
+                            29997023, 30000000]),
+       col_indices=tensor([   3,    4,    5,  ..., 9990, 9992, 9998]),
+       values=tensor([0.6330, 0.2666, 0.3549,  ..., 0.7937, 0.6841, 0.3201]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.4780, 0.6736, 0.3887,  ..., 0.7294, 0.7408, 0.5543])
+tensor([0.0444, 0.4833, 0.9897,  ..., 0.1872, 0.1999, 0.3106])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -33,13 +33,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 63.62427043914795 seconds
+Time: 63.51457595825195 seconds
 
-[16.52, 16.44, 16.52, 16.76, 16.76, 16.68, 17.12, 16.6, 16.6, 16.64]
-[16.76, 16.68, 16.6, 20.16, 21.48, 23.48, 24.68, 31.48, 34.68, 34.64, 35.08, 35.08, 34.68, 24.92, 25.84, 25.44, 24.52, 23.52, 20.92, 20.76, 20.76, 20.88, 20.6, 20.8, 20.8, 20.96, 20.88, 20.6, 20.8, 20.56, 20.44, 20.52, 20.48, 20.8, 20.56, 20.6, 20.6, 20.68, 20.72, 20.68, 20.88, 20.92, 20.8, 20.8, 20.52, 20.56, 20.68, 20.96, 20.8, 20.8, 20.92, 20.8, 20.56, 20.68, 20.8, 20.72, 20.8, 21.12, 21.24, 21.16, 21.12, 21.12, 21.04, 20.6, 20.36, 20.2, 20.4, 20.48, 20.52, 20.88, 20.64, 20.32, 20.4, 20.4, 20.4, 20.4]
-76.90683102607727
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 63.62427043914795, 'TIME_S_1KI': 636.2427043914795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1670.8393529319762, 'W': 21.725499941161722}
-[16.52, 16.44, 16.52, 16.76, 16.76, 16.68, 17.12, 16.6, 16.6, 16.64, 16.32, 16.72, 16.92, 17.2, 17.32, 17.52, 17.16, 17.24, 17.28, 17.12]
-304.14
-15.206999999999999
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 63.62427043914795, 'TIME_S_1KI': 636.2427043914795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1670.8393529319762, 'W': 21.725499941161722, 'J_1KI': 16708.393529319765, 'W_1KI': 217.2549994116172, 'W_D': 6.518499941161723, 'J_D': 501.3171735184193, 'W_D_1KI': 65.18499941161723, 'J_D_1KI': 651.8499941161723}
+[20.36, 20.68, 20.6, 20.56, 20.56, 20.4, 20.04, 20.04, 20.04, 20.04]
+[20.24, 20.76, 20.96, 24.4, 26.68, 27.72, 29.08, 34.08, 34.68, 33.64, 34.52, 34.48, 34.48, 28.36, 30.48, 29.8, 28.56, 27.48, 24.2, 24.16, 24.04, 23.92, 24.12, 24.32, 24.08, 24.08, 24.2, 23.96, 23.8, 23.96, 24.0, 24.24, 24.4, 24.32, 24.32, 24.48, 24.52, 24.52, 24.56, 24.68, 24.52, 24.44, 24.32, 24.16, 24.36, 24.64, 24.56, 24.68, 24.64, 24.52, 24.52, 24.64, 24.64, 24.44, 24.44, 24.2, 24.16, 24.28, 24.36, 24.36, 24.52, 24.44, 24.32, 24.32, 24.4, 24.52, 24.56, 24.52, 24.48, 24.32, 24.24, 24.44, 24.44, 24.72, 24.6]
+76.12011408805847
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 63.51457595825195, 'TIME_S_1KI': 635.1457595825195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1889.5348065662383, 'W': 24.82306850434245}
+[20.36, 20.68, 20.6, 20.56, 20.56, 20.4, 20.04, 20.04, 20.04, 20.04, 21.12, 20.32, 20.32, 20.08, 20.24, 20.36, 20.44, 20.68, 20.76, 20.6]
+367.17999999999995
+18.358999999999998
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 63.51457595825195, 'TIME_S_1KI': 635.1457595825195, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1889.5348065662383, 'W': 24.82306850434245, 'J_1KI': 18895.348065662383, 'W_1KI': 248.23068504342453, 'W_D': 6.464068504342453, 'J_D': 492.04563202357303, 'W_D_1KI': 64.64068504342453, 'J_D_1KI': 646.4068504342453}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..3d915b6
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 84.6539237499237, "TIME_S_1KI": 846.5392374992371, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2521.516366138459, "W": 24.86361045903338, "J_1KI": 25215.16366138459, "W_1KI": 248.63610459033382, "W_D": 6.453610459033381, "J_D": 654.4859774065027, "W_D_1KI": 64.53610459033382, "J_D_1KI": 645.3610459033382}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..47e9940
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.4.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 84.6539237499237}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3917,     7951,  ..., 39991952,
+                            39996009, 40000000]),
+       col_indices=tensor([   1,    2,    4,  ..., 9995, 9996, 9999]),
+       values=tensor([0.0966, 0.2927, 0.1488,  ..., 0.0691, 0.2938, 0.1140]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.4209, 0.9243, 0.0966,  ..., 0.4873, 0.3754, 0.4036])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 84.6539237499237 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3917,     7951,  ..., 39991952,
+                            39996009, 40000000]),
+       col_indices=tensor([   1,    2,    4,  ..., 9995, 9996, 9999]),
+       values=tensor([0.0966, 0.2927, 0.1488,  ..., 0.0691, 0.2938, 0.1140]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.4209, 0.9243, 0.0966,  ..., 0.4873, 0.3754, 0.4036])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 84.6539237499237 seconds
+
+[20.0, 20.04, 20.0, 20.0, 20.28, 20.56, 20.52, 20.56, 20.84, 20.64]
+[20.52, 20.6, 21.16, 24.48, 26.48, 27.76, 28.64, 27.96, 27.96, 38.92, 38.12, 38.68, 37.92, 32.92, 25.72, 25.88, 25.44, 27.52, 27.12, 26.76, 26.68, 26.68, 24.24, 24.52, 24.56, 24.84, 24.84, 24.6, 24.64, 24.4, 24.24, 24.16, 24.08, 24.08, 24.04, 24.32, 24.32, 24.36, 24.44, 24.32, 24.52, 24.56, 24.64, 24.88, 24.88, 24.68, 24.68, 24.76, 24.76, 24.48, 24.44, 24.4, 24.48, 24.52, 24.72, 24.72, 24.64, 24.52, 24.52, 24.6, 24.36, 24.48, 24.36, 24.36, 24.4, 24.24, 24.36, 24.36, 24.36, 24.36, 24.32, 24.32, 24.36, 24.64, 24.76, 24.76, 24.84, 24.64, 24.68, 24.4, 24.44, 24.32, 24.24, 24.24, 24.28, 24.2, 24.36, 24.28, 24.32, 24.44, 24.44, 24.44, 24.48, 24.52, 24.76, 24.76, 24.76, 24.8, 24.68, 24.44]
+101.41392660140991
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 84.6539237499237, 'TIME_S_1KI': 846.5392374992371, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2521.516366138459, 'W': 24.86361045903338}
+[20.0, 20.04, 20.0, 20.0, 20.28, 20.56, 20.52, 20.56, 20.84, 20.64, 20.52, 20.52, 20.64, 20.84, 20.72, 20.6, 20.36, 20.56, 20.4, 20.36]
+368.2
+18.41
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 84.6539237499237, 'TIME_S_1KI': 846.5392374992371, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2521.516366138459, 'W': 24.86361045903338, 'J_1KI': 25215.16366138459, 'W_1KI': 248.63610459033382, 'W_D': 6.453610459033381, 'J_D': 654.4859774065027, 'W_D_1KI': 64.53610459033382, 'J_D_1KI': 645.3610459033382}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..74b3dc6
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 107.38222455978394, "TIME_S_1KI": 1073.8222455978394, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3211.2984370231634, "W": 24.76028863403025, "J_1KI": 32112.984370231636, "W_1KI": 247.6028863403025, "W_D": 6.163288634030248, "J_D": 799.3509061999323, "W_D_1KI": 61.632886340302484, "J_D_1KI": 616.3288634030248}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..80ae590
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_0.5.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 107.38222455978394}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     5036,     9995,  ..., 49989852,
+                            49994946, 50000000]),
+       col_indices=tensor([   0,    1,    5,  ..., 9992, 9993, 9994]),
+       values=tensor([0.1426, 0.2723, 0.4807,  ..., 0.6484, 0.4851, 0.2642]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.1080, 0.5862, 0.7267,  ..., 0.6646, 0.4667, 0.9799])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 107.38222455978394 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     5036,     9995,  ..., 49989852,
+                            49994946, 50000000]),
+       col_indices=tensor([   0,    1,    5,  ..., 9992, 9993, 9994]),
+       values=tensor([0.1426, 0.2723, 0.4807,  ..., 0.6484, 0.4851, 0.2642]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.1080, 0.5862, 0.7267,  ..., 0.6646, 0.4667, 0.9799])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 107.38222455978394 seconds
+
+[20.8, 20.8, 20.64, 20.6, 20.6, 20.68, 20.72, 20.6, 20.56, 20.44]
+[20.56, 20.56, 20.56, 21.84, 22.92, 24.12, 25.4, 25.92, 27.4, 38.8, 39.56, 40.04, 40.28, 34.76, 27.88, 27.88, 28.28, 28.6, 28.96, 31.0, 29.84, 28.44, 27.6, 26.56, 24.24, 24.32, 24.36, 24.36, 24.24, 24.32, 24.44, 24.44, 24.44, 24.44, 24.32, 24.2, 24.32, 24.44, 24.36, 24.36, 24.36, 24.28, 24.16, 23.88, 23.96, 24.12, 24.12, 24.28, 24.64, 24.6, 24.64, 24.8, 24.8, 24.64, 24.4, 24.36, 24.48, 24.4, 24.28, 24.08, 23.96, 24.0, 24.16, 24.08, 24.4, 24.4, 24.32, 24.6, 24.68, 24.44, 24.44, 24.16, 24.12, 24.24, 24.48, 24.6, 24.64, 24.64, 24.48, 24.4, 24.28, 24.12, 24.2, 24.08, 23.92, 24.08, 23.96, 23.84, 24.0, 24.0, 24.0, 24.0, 24.08, 24.12, 24.2, 24.32, 24.4, 24.32, 24.36, 24.72, 24.6, 24.64, 24.64, 24.48, 24.24, 24.28, 24.24, 24.4, 24.36, 24.36, 24.24, 24.2, 24.12, 24.16, 24.2, 24.2, 24.28, 24.28, 24.28, 24.24, 24.36, 24.2, 24.36, 24.44, 24.76, 24.48, 24.44, 24.44]
+129.69551706314087
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 107.38222455978394, 'TIME_S_1KI': 1073.8222455978394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3211.2984370231634, 'W': 24.76028863403025}
+[20.8, 20.8, 20.64, 20.6, 20.6, 20.68, 20.72, 20.6, 20.56, 20.44, 20.92, 20.92, 20.92, 20.88, 20.76, 20.4, 20.72, 20.48, 20.44, 20.28]
+371.94
+18.597
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 107.38222455978394, 'TIME_S_1KI': 1073.8222455978394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3211.2984370231634, 'W': 24.76028863403025, 'J_1KI': 32112.984370231636, 'W_1KI': 247.6028863403025, 'W_D': 6.163288634030248, 'J_D': 799.3509061999323, 'W_D_1KI': 61.632886340302484, 'J_D_1KI': 616.3288634030248}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.json
index 66821f5..1ef843a 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 141552, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.798607587814331, "TIME_S_1KI": 0.07628721309352274, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 269.76709407806396, "W": 18.985490730093364, "J_1KI": 1.9057808725985077, "W_1KI": 0.13412379005661076, "W_D": 4.025490730093365, "J_D": 57.198676185607916, "W_D_1KI": 0.028438246934648505, "J_D_1KI": 0.0002009031799949736}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 140907, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.631132364273071, "TIME_S_1KI": 0.07544786535994004, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 311.0322494506836, "W": 21.8403623772563, "J_1KI": 2.2073583956133027, "W_1KI": 0.15499842007321354, "W_D": 3.2933623772562974, "J_D": 46.90132383155815, "W_D_1KI": 0.023372595948081342, "J_D_1KI": 0.00016587249709440512}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.output
index 2de78b8..52e484b 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_1e-05.output
@@ -1,266 +1,373 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.015316009521484375}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.015331745147705078}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
-       col_indices=tensor([9112, 5292, 3575, 5961, 5092, 9328, 2019, 8963, 6545,
-                           7422,  818, 4088,  407, 2015,   64, 4328, 8365, 6206,
-                           9094, 5899, 6513, 5954, 4347, 8726, 6057, 3857, 2262,
-                           5262, 3242, 3739, 9546, 2710, 9191,  261, 5342, 5031,
-                           4219, 1193, 8424, 3055, 1118, 7444, 3421, 4719,  884,
-                           9797, 6413, 7746, 2915, 8061, 1921, 7039, 8615, 6298,
-                           8962, 4075, 2597, 5310, 5194, 9913,  649,  821, 1630,
-                           6166, 2303,  666, 4248, 9703, 9421,  933, 8076, 2414,
-                           2921, 3906, 2279, 4858, 3629, 8129,  746, 1260, 8400,
-                           3559, 6490, 9630, 9771, 2054, 6368, 4294,   59, 1873,
-                           8913, 6245, 1629, 8022, 7105, 7514, 7136,  705, 7667,
-                            598, 5268, 8736, 8255, 3780, 9168,  855,  891, 5701,
-                           1088, 5566, 9882, 6656, 6138, 1874, 1409, 2224, 9088,
-                           5917, 2460, 1920, 8927, 3689, 9209, 2413, 7383, 5435,
-                           1164, 4656, 1145, 9858, 1276, 1566, 4567, 6670, 8019,
-                             77, 4463, 9904, 1378, 6274, 6811, 7718, 6698, 6133,
-                           1817, 5750, 9902, 8179, 7778, 9310, 3186, 2889, 6830,
-                           3732, 9331,   80, 4061, 9725, 2461,  365, 5801, 7394,
-                           1364, 7916, 4458, 9726, 1384, 8245, 4422, 1021, 8441,
-                           8635, 9830, 6237, 8296, 9417, 1755, 5150, 8755,  573,
-                           6482, 6077, 4649, 5929, 3346, 2879, 2567, 9845, 9617,
-                           7984, 1423,   20, 9389, 7465, 1857, 6041,   81, 3754,
-                           3443, 6292, 8270,  777, 2884, 6004, 5456, 7516, 4457,
-                           2299, 9549, 3283, 8815, 7872, 6555, 9764, 3765, 3453,
-                           3743,  966, 1219, 6368, 7586, 7598,  502, 5829, 8794,
-                            426, 1814, 9137, 7261, 9402, 2755,   22, 4007, 5976,
-                             44, 8313, 1900, 8548, 3115, 7074, 1012, 8434, 6097,
-                           7768, 5045, 3244, 2886, 1954,  541, 5982, 5878, 5999,
-                           4618, 9582, 4847, 7320, 9835, 5610, 1305, 8884, 6363,
-                           6478,  839, 5162, 9728, 5499, 1528, 5689, 3183, 7129,
-                           2488, 4352,  928, 8220, 1382, 3689, 8735, 2195, 9727,
-                            771, 6448, 8679, 1573, 5638, 2952, 6446, 4389, 8540,
-                           4711, 8193, 7117, 2236, 5186, 1639, 2122, 9434, 4600,
-                           6263, 5259, 9546,  979, 6158, 4405, 3909, 3341, 4623,
-                           4859, 8302, 4199, 6104, 2336, 9661, 8228, 7721, 9724,
-                           9263, 6964, 9466, 2887, 6905, 9477, 5133, 4559,  533,
-                           2189,  788, 3859, 2643, 7749, 7409, 8072, 2895, 4935,
-                           3245, 8702, 7464, 5201, 7428,   20, 9781, 4720, 1259,
-                           9047, 5360, 2447, 5616,  727,  910, 8863, 9370, 6403,
-                           6786, 6714, 5315, 5973, 2498, 2170, 4132, 8638, 1133,
-                           5562, 8468, 1057, 3841, 5439, 5548,  364, 3661, 4774,
-                           5311, 8396, 2293, 3242, 6666, 9193, 5519, 7639, 9475,
-                           3599, 3244, 2790, 1000, 3648, 8996,  802, 8274, 5880,
-                           3912, 7828, 7630,   88, 1120, 4545,  662, 8080, 8593,
-                           2010, 2001, 5509, 1694, 1693, 9968,  188, 5356, 2456,
-                           5653, 1100, 9884, 8606, 5679, 1447, 3651, 3652, 4270,
-                           4855, 4081, 6277, 7485, 8206,  943, 9917, 8822, 8258,
-                           9850, 3678, 6542, 8398, 7438, 4466, 4288, 3429, 1080,
-                           3756,  619, 8368, 2943, 5279, 4660, 7902, 7718, 2077,
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+                      3.6308e-01, 2.2972e-01, 5.2855e-02, 8.9525e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.8762, 0.1751, 0.9607,  ..., 0.4579, 0.7370, 0.8447])
+tensor([0.2994, 0.2473, 0.7750,  ..., 0.9795, 0.5606, 0.1160])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -268,378 +375,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.015316009521484375 seconds
+Time: 0.015331745147705078 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 68555 -ss 10000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.085230827331543}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 68485 -ss 10000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.103281259536743}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      1.9268e-01, 2.6403e-02, 5.3412e-01, 2.2108e-02]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4288, 0.6749, 0.2273,  ..., 0.2100, 0.8485, 0.9690])
+tensor([0.4069, 0.2647, 0.7465,  ..., 0.2206, 0.0986, 0.2189])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -647,271 +754,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 5.085230827331543 seconds
+Time: 5.103281259536743 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 141552 -ss 10000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.798607587814331}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 140907 -ss 10000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.631132364273071}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.0219, 0.9207, 0.3669,  ..., 0.7955, 0.2670, 0.3543])
+tensor([0.7308, 0.4342, 0.3494,  ..., 0.0020, 0.7901, 0.4274])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -919,268 +1133,375 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.798607587814331 seconds
+Time: 10.631132364273071 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      9.3704e-01, 8.3071e-01, 1.9322e-01, 1.3391e-01,
+                      7.9375e-01, 8.9058e-01, 3.2576e-01, 7.6412e-01,
+                      2.9206e-01, 9.4303e-02, 2.6508e-01, 2.6732e-02,
+                      8.6474e-01, 6.5165e-01, 6.6929e-01, 3.2381e-01,
+                      7.3144e-01, 5.0760e-01, 8.3973e-01, 5.6446e-01,
+                      5.0774e-01, 4.5819e-01, 5.0472e-01, 8.2957e-01,
+                      6.9893e-01, 5.4532e-01, 2.1384e-01, 2.7863e-01,
+                      9.7407e-01, 4.9597e-01, 5.9598e-01, 1.0999e-01,
+                      6.0630e-01, 4.4774e-01, 1.2226e-01, 7.8700e-01,
+                      2.9926e-01, 9.9008e-02, 6.1613e-01, 5.9640e-01,
+                      6.9008e-01, 2.2797e-01, 4.3699e-01, 7.4226e-01,
+                      2.5932e-01, 9.4341e-01, 3.6656e-01, 8.9090e-01,
+                      1.1825e-01, 1.8231e-01, 9.1802e-01, 5.6671e-01,
+                      2.4127e-01, 8.4582e-02, 4.4272e-02, 3.4668e-01,
+                      5.0760e-01, 8.9506e-01, 4.8637e-01, 1.2431e-01,
+                      8.0726e-01, 9.6584e-01, 7.1123e-01, 8.4781e-01,
+                      1.6727e-01, 6.6336e-01, 5.4632e-01, 3.5048e-02,
+                      5.4051e-01, 7.8963e-01, 5.6210e-01, 7.3553e-01,
+                      4.1628e-01, 4.2604e-01, 2.2175e-01, 9.6073e-01,
+                      5.1360e-01, 4.9418e-01, 7.9926e-01, 3.7731e-01,
+                      9.8090e-01, 3.0631e-01, 1.4232e-02, 2.8290e-01,
+                      1.9600e-01, 2.2115e-01, 6.7154e-01, 4.3191e-01,
+                      4.9638e-01, 2.2288e-01, 6.3807e-01, 7.0286e-01,
+                      5.3409e-01, 4.7524e-01, 6.8467e-02, 9.4389e-02,
+                      3.3462e-01, 3.4218e-01, 6.9728e-01, 5.3604e-01,
+                      4.3637e-01, 1.6252e-01, 7.1201e-01, 1.5898e-01,
+                      1.5749e-01, 6.4281e-01, 2.9275e-02, 7.4966e-01,
+                      3.4188e-01, 2.2876e-01, 1.6752e-01, 7.7030e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.0219, 0.9207, 0.3669,  ..., 0.7955, 0.2670, 0.3543])
+tensor([0.7308, 0.4342, 0.3494,  ..., 0.0020, 0.7901, 0.4274])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1188,13 +1509,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.798607587814331 seconds
+Time: 10.631132364273071 seconds
 
-[16.32, 16.4, 16.24, 16.76, 16.72, 17.12, 17.44, 17.24, 17.36, 17.12]
-[16.8, 16.56, 16.56, 19.68, 21.72, 23.52, 24.24, 24.8, 20.88, 20.0, 19.92, 19.92, 19.64, 19.64]
-14.209118843078613
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 141552, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.798607587814331, 'TIME_S_1KI': 0.07628721309352274, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 269.76709407806396, 'W': 18.985490730093364}
-[16.32, 16.4, 16.24, 16.76, 16.72, 17.12, 17.44, 17.24, 17.36, 17.12, 16.28, 16.24, 16.24, 16.36, 16.6, 16.4, 16.24, 16.4, 16.44, 16.28]
-299.2
-14.959999999999999
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 141552, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.798607587814331, 'TIME_S_1KI': 0.07628721309352274, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 269.76709407806396, 'W': 18.985490730093364, 'J_1KI': 1.9057808725985077, 'W_1KI': 0.13412379005661076, 'W_D': 4.025490730093365, 'J_D': 57.198676185607916, 'W_D_1KI': 0.028438246934648505, 'J_D_1KI': 0.0002009031799949736}
+[20.2, 20.24, 20.32, 20.32, 20.48, 20.56, 20.76, 20.64, 20.76, 20.56]
+[20.48, 20.32, 21.2, 23.12, 23.88, 24.68, 24.68, 25.56, 25.0, 24.08, 23.76, 23.68, 23.4, 23.52]
+14.241167068481445
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 140907, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.631132364273071, 'TIME_S_1KI': 0.07544786535994004, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 311.0322494506836, 'W': 21.8403623772563}
+[20.2, 20.24, 20.32, 20.32, 20.48, 20.56, 20.76, 20.64, 20.76, 20.56, 20.52, 20.72, 20.96, 21.0, 21.0, 20.56, 20.56, 20.68, 20.48, 20.52]
+370.94000000000005
+18.547000000000004
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 140907, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.631132364273071, 'TIME_S_1KI': 0.07544786535994004, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 311.0322494506836, 'W': 21.8403623772563, 'J_1KI': 2.2073583956133027, 'W_1KI': 0.15499842007321354, 'W_D': 3.2933623772562974, 'J_D': 46.90132383155815, 'W_D_1KI': 0.023372595948081342, 'J_D_1KI': 0.00016587249709440512}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_5e-05.json
index ccc8254..04529c9 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_5e-05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 52454, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.6396803855896, "TIME_S_1KI": 0.20283830376309908, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 254.19103302001952, "W": 17.900584259593533, "J_1KI": 4.845979963778158, "W_1KI": 0.3412625206770415, "W_D": 2.811584259593534, "J_D": 39.92492630434038, "W_D_1KI": 0.05360095053939707, "J_D_1KI": 0.0010218658355777839}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 53204, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.560401201248169, "TIME_S_1KI": 0.19848885800406302, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 308.5575102233887, "W": 21.652677512658492, "J_1KI": 5.799517145767023, "W_1KI": 0.40697461680810637, "W_D": 3.1216775126584935, "J_D": 44.484892940521284, "W_D_1KI": 0.05867373717499612, "J_D_1KI": 0.001102806878712054}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_5e-05.output
index 6f0e024..967cdd4 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_5e-05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_10000_5e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.02812814712524414}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.027580976486206055}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    2,  ..., 4998, 4999, 5000]),
-       col_indices=tensor([4541, 9541, 3383,  ..., 9920, 3344, 2731]),
-       values=tensor([0.3320, 0.1825, 0.5042,  ..., 0.6612, 0.1900, 0.5121]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 4999, 5000, 5000]),
+       col_indices=tensor([4636, 2207, 9397,  ..., 7394, 6819, 4909]),
+       values=tensor([0.7602, 0.5956, 0.1963,  ..., 0.6661, 0.3565, 0.0864]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.0144, 0.2553, 0.6494,  ..., 0.0787, 0.9201, 0.6475])
+tensor([0.2838, 0.4836, 0.6308,  ..., 0.2591, 0.6627, 0.6296])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 0.02812814712524414 seconds
+Time: 0.027580976486206055 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 37329 -ss 10000 -sd 5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.472296953201294}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 38069 -ss 10000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.513024568557739}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 4999, 5000]),
-       col_indices=tensor([6333, 2190, 7526,  ..., 2226, 3084, 9881]),
-       values=tensor([0.4590, 0.1089, 0.5094,  ..., 0.8341, 0.9457, 0.0387]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4998, 5000, 5000]),
+       col_indices=tensor([2398, 9128, 2154,  ..., 1626, 1842, 5097]),
+       values=tensor([0.8254, 0.9519, 0.4888,  ..., 0.7669, 0.5685, 0.1906]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.3900, 0.6084, 0.4843,  ..., 0.7689, 0.5332, 0.9837])
+tensor([0.5556, 0.7922, 0.2509,  ..., 0.4770, 0.1507, 0.6847])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 7.472296953201294 seconds
+Time: 7.513024568557739 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 52454 -ss 10000 -sd 5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.6396803855896}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 53204 -ss 10000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.560401201248169}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([2165, 7276,  636,  ..., 1970, 2680, 2527]),
-       values=tensor([0.0210, 0.6731, 0.7347,  ..., 0.2518, 0.3264, 0.9787]),
+tensor(crow_indices=tensor([   0,    0,    3,  ..., 4998, 4999, 5000]),
+       col_indices=tensor([ 182, 2198, 3609,  ..., 9551, 4834, 5813]),
+       values=tensor([0.1200, 0.5485, 0.3269,  ..., 0.1404, 0.5270, 0.0735]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.5715, 0.5141, 0.9224,  ..., 0.0062, 0.8236, 0.9187])
+tensor([0.0967, 0.0255, 0.9669,  ..., 0.7699, 0.3609, 0.2228])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.6396803855896 seconds
+Time: 10.560401201248169 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([2165, 7276,  636,  ..., 1970, 2680, 2527]),
-       values=tensor([0.0210, 0.6731, 0.7347,  ..., 0.2518, 0.3264, 0.9787]),
+tensor(crow_indices=tensor([   0,    0,    3,  ..., 4998, 4999, 5000]),
+       col_indices=tensor([ 182, 2198, 3609,  ..., 9551, 4834, 5813]),
+       values=tensor([0.1200, 0.5485, 0.3269,  ..., 0.1404, 0.5270, 0.0735]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.5715, 0.5141, 0.9224,  ..., 0.0062, 0.8236, 0.9187])
+tensor([0.0967, 0.0255, 0.9669,  ..., 0.7699, 0.3609, 0.2228])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.6396803855896 seconds
+Time: 10.560401201248169 seconds
 
-[16.64, 16.48, 16.64, 16.84, 17.0, 16.8, 16.72, 16.4, 16.4, 16.2]
-[16.28, 16.44, 16.8, 17.88, 19.0, 19.88, 20.6, 20.92, 20.48, 20.0, 20.12, 20.12, 19.92, 19.84]
-14.20015287399292
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 52454, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.6396803855896, 'TIME_S_1KI': 0.20283830376309908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 254.19103302001952, 'W': 17.900584259593533}
-[16.64, 16.48, 16.64, 16.84, 17.0, 16.8, 16.72, 16.4, 16.4, 16.2, 16.72, 16.92, 16.8, 16.88, 17.04, 17.16, 16.96, 16.8, 16.84, 16.64]
-301.78
-15.088999999999999
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 52454, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.6396803855896, 'TIME_S_1KI': 0.20283830376309908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 254.19103302001952, 'W': 17.900584259593533, 'J_1KI': 4.845979963778158, 'W_1KI': 0.3412625206770415, 'W_D': 2.811584259593534, 'J_D': 39.92492630434038, 'W_D_1KI': 0.05360095053939707, 'J_D_1KI': 0.0010218658355777839}
+[20.28, 20.4, 20.36, 20.6, 20.6, 20.56, 20.8, 21.12, 20.96, 21.0]
+[20.96, 20.72, 21.76, 21.76, 22.76, 23.56, 24.32, 24.84, 24.2, 24.16, 24.24, 24.04, 23.92, 23.72]
+14.250316619873047
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 53204, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.560401201248169, 'TIME_S_1KI': 0.19848885800406302, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 308.5575102233887, 'W': 21.652677512658492}
+[20.28, 20.4, 20.36, 20.6, 20.6, 20.56, 20.8, 21.12, 20.96, 21.0, 20.36, 20.32, 20.36, 20.36, 20.24, 20.4, 20.68, 20.72, 20.88, 20.88]
+370.62
+18.531
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 53204, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.560401201248169, 'TIME_S_1KI': 0.19848885800406302, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 308.5575102233887, 'W': 21.652677512658492, 'J_1KI': 5.799517145767023, 'W_1KI': 0.40697461680810637, 'W_D': 3.1216775126584935, 'J_D': 44.484892940521284, 'W_D_1KI': 0.05867373717499612, 'J_D_1KI': 0.001102806878712054}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_0.0001.json
index 2f5c509..0be7b79 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.72194480895996, "TIME_S_1KI": 227.2194480895996, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2080.890781612396, "W": 61.453409935621025, "J_1KI": 20808.90781612396, "W_1KI": 614.5340993562103, "W_D": 45.112409935621024, "J_D": 1527.5636953213213, "W_D_1KI": 451.12409935621025, "J_D_1KI": 4511.240993562103}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 48, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.713890314102173, "TIME_S_1KI": 244.03938154379526, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1150.7120539855955, "W": 48.64237277538323, "J_1KI": 23973.167791366574, "W_1KI": 1013.3827661538173, "W_D": 29.75037277538323, "J_D": 703.791994713783, "W_D_1KI": 619.799432820484, "J_D_1KI": 12912.488183760082}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_0.0001.output
index 4a89b40..6a80d34 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_0.0001.output
@@ -1,15 +1,15 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.72194480895996}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10 -ss 500000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.7228524684906006}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       56,      116,  ..., 24999900,
-                            24999953, 25000000]),
-       col_indices=tensor([   647,   4700,  33413,  ..., 445020, 463377,
-                           482076]),
-       values=tensor([0.2494, 0.9199, 0.9974,  ..., 0.2647, 0.3316, 0.8056]),
+tensor(crow_indices=tensor([       0,       47,       96,  ..., 24999908,
+                            24999948, 25000000]),
+       col_indices=tensor([  1989,   3552,   6705,  ..., 466244, 486493,
+                           489174]),
+       values=tensor([0.6522, 0.2346, 0.2842,  ..., 0.3095, 0.0896, 0.9153]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.1920, 0.0118, 0.3050,  ..., 0.0609, 0.1776, 0.7503])
+tensor([0.6400, 0.1984, 0.0727,  ..., 0.5920, 0.9764, 0.2461])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,17 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 22.72194480895996 seconds
+Time: 2.7228524684906006 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 38 -ss 500000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.282682418823242}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       56,      116,  ..., 24999900,
-                            24999953, 25000000]),
-       col_indices=tensor([   647,   4700,  33413,  ..., 445020, 463377,
-                           482076]),
-       values=tensor([0.2494, 0.9199, 0.9974,  ..., 0.2647, 0.3316, 0.8056]),
+tensor(crow_indices=tensor([       0,       50,       88,  ..., 24999898,
+                            24999958, 25000000]),
+       col_indices=tensor([   963,  38747,  47222,  ..., 450900, 451431,
+                           483744]),
+       values=tensor([0.0669, 0.7481, 0.2594,  ..., 0.6735, 0.7504, 0.2787]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.1920, 0.0118, 0.3050,  ..., 0.0609, 0.1776, 0.7503])
+tensor([0.9383, 0.0659, 0.9642,  ..., 0.2790, 0.6849, 0.3467])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -35,13 +38,52 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 22.72194480895996 seconds
+Time: 8.282682418823242 seconds
 
-[17.96, 18.12, 17.96, 17.96, 17.88, 17.96, 17.88, 17.92, 17.76, 17.8]
-[17.8, 17.6, 18.48, 20.36, 21.4, 22.72, 22.72, 24.08, 25.12, 34.04, 34.44, 33.88, 34.28, 32.56, 45.4, 59.36, 77.8, 92.4, 98.2, 98.2, 95.48, 95.96, 96.12, 95.0, 93.72, 96.32, 96.24, 93.28, 93.88, 93.4, 91.28, 91.4, 93.2]
-33.8612744808197
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.72194480895996, 'TIME_S_1KI': 227.2194480895996, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2080.890781612396, 'W': 61.453409935621025}
-[17.96, 18.12, 17.96, 17.96, 17.88, 17.96, 17.88, 17.92, 17.76, 17.8, 17.72, 18.0, 18.44, 18.6, 18.6, 18.64, 18.76, 18.48, 18.16, 17.92]
-326.82000000000005
-16.341
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.72194480895996, 'TIME_S_1KI': 227.2194480895996, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2080.890781612396, 'W': 61.453409935621025, 'J_1KI': 20808.90781612396, 'W_1KI': 614.5340993562103, 'W_D': 45.112409935621024, 'J_D': 1527.5636953213213, 'W_D_1KI': 451.12409935621025, 'J_D_1KI': 4511.240993562103}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 48 -ss 500000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.713890314102173}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       53,      101,  ..., 24999900,
+                            24999950, 25000000]),
+       col_indices=tensor([  1386,   3010,   3959,  ..., 488913, 496080,
+                           496424]),
+       values=tensor([0.0244, 0.0960, 0.4481,  ..., 0.2771, 0.3942, 0.7484]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3602, 0.3084, 0.6398,  ..., 0.1971, 0.4339, 0.7294])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 11.713890314102173 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       53,      101,  ..., 24999900,
+                            24999950, 25000000]),
+       col_indices=tensor([  1386,   3010,   3959,  ..., 488913, 496080,
+                           496424]),
+       values=tensor([0.0244, 0.0960, 0.4481,  ..., 0.2771, 0.3942, 0.7484]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3602, 0.3084, 0.6398,  ..., 0.1971, 0.4339, 0.7294])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 11.713890314102173 seconds
+
+[21.04, 20.88, 21.04, 20.92, 21.0, 20.84, 20.96, 20.8, 20.84, 20.92]
+[21.24, 21.24, 21.32, 25.36, 26.92, 28.04, 30.04, 30.72, 32.12, 39.2, 39.72, 38.88, 39.52, 34.64, 49.48, 59.92, 59.92, 74.88, 89.32, 91.52, 94.96, 99.12, 97.92]
+23.656577348709106
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 48, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.713890314102173, 'TIME_S_1KI': 244.03938154379526, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1150.7120539855955, 'W': 48.64237277538323}
+[21.04, 20.88, 21.04, 20.92, 21.0, 20.84, 20.96, 20.8, 20.84, 20.92, 21.32, 21.28, 21.36, 21.16, 21.08, 20.92, 20.84, 20.96, 20.88, 20.88]
+377.84
+18.892
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 48, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.713890314102173, 'TIME_S_1KI': 244.03938154379526, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1150.7120539855955, 'W': 48.64237277538323, 'J_1KI': 23973.167791366574, 'W_1KI': 1013.3827661538173, 'W_D': 29.75037277538323, 'J_D': 703.791994713783, 'W_D_1KI': 619.799432820484, 'J_D_1KI': 12912.488183760082}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.json
index 208d33f..c21bdce 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 279, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 12.429174900054932, "TIME_S_1KI": 44.549013978691505, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 925.7997315406801, "W": 55.99377793769148, "J_1KI": 3318.2786076726884, "W_1KI": 200.69454457953935, "W_D": 40.002777937691484, "J_D": 661.4049353270533, "W_D_1KI": 143.37913239315944, "J_D_1KI": 513.9037003339048}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 364, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 12.988611936569214, "TIME_S_1KI": 35.6829998257396, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 885.5386364746093, "W": 57.44468165762061, "J_1KI": 2432.798451853322, "W_1KI": 157.8150594989577, "W_D": 38.28968165762061, "J_D": 590.2546851634979, "W_D_1KI": 105.19143312533136, "J_D_1KI": 288.9874536410202}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.output
index 2fab080..303fcff 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,15 +1,15 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.175914764404297}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10 -ss 500000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.28778886795043945}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       5,       9,  ..., 2499991,
+tensor(crow_indices=tensor([      0,       3,      12,  ..., 2499992,
                             2499995, 2500000]),
-       col_indices=tensor([236491, 268930, 282894,  ..., 290854, 362096,
-                           428990]),
-       values=tensor([0.4942, 0.2006, 0.6461,  ..., 0.6339, 0.7923, 0.0061]),
+       col_indices=tensor([281211, 330355, 387890,  ..., 320961, 441469,
+                           468706]),
+       values=tensor([0.2795, 0.2341, 0.6091,  ..., 0.8168, 0.5124, 0.1850]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6272, 0.4782, 0.6613,  ..., 0.5722, 0.7323, 0.6099])
+tensor([0.9736, 0.9446, 0.8562,  ..., 0.4755, 0.1400, 0.2649])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 4.175914764404297 seconds
+Time: 0.28778886795043945 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 251 -ss 500000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.986790895462036}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 364 -ss 500000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 12.988611936569214}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       3,       4,  ..., 2499995,
-                            2499998, 2500000]),
-       col_indices=tensor([168226, 184311, 332682,  ..., 175948,  40749,
-                           152556]),
-       values=tensor([0.8367, 0.0584, 0.1423,  ..., 0.1509, 0.1566, 0.6036]),
+tensor(crow_indices=tensor([      0,       4,      12,  ..., 2499990,
+                            2499996, 2500000]),
+       col_indices=tensor([294772, 391884, 399546,  ..., 177105, 373352,
+                           469734]),
+       values=tensor([0.9560, 0.6476, 0.4890,  ..., 0.2401, 0.0683, 0.2223]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8910, 0.6965, 0.0939,  ..., 0.1566, 0.5700, 0.8005])
+tensor([0.4409, 0.5878, 0.2813,  ..., 0.9916, 0.6033, 0.0135])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,20 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 9.986790895462036 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 263 -ss 500000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.86619520187378}
+Time: 12.988611936569214 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       1,       4,  ..., 2499987,
-                            2499997, 2500000]),
-       col_indices=tensor([120869, 339930, 358219,  ...,  35981,  71933,
-                           400518]),
-       values=tensor([0.0243, 0.7300, 0.4495,  ..., 0.8433, 0.9453, 0.9296]),
+tensor(crow_indices=tensor([      0,       4,      12,  ..., 2499990,
+                            2499996, 2500000]),
+       col_indices=tensor([294772, 391884, 399546,  ..., 177105, 373352,
+                           469734]),
+       values=tensor([0.9560, 0.6476, 0.4890,  ..., 0.2401, 0.0683, 0.2223]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0560, 0.8217, 0.0541,  ..., 0.5269, 0.7792, 0.2112])
+tensor([0.4409, 0.5878, 0.2813,  ..., 0.9916, 0.6033, 0.0135])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -59,52 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 9.86619520187378 seconds
+Time: 12.988611936569214 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 279 -ss 500000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 12.429174900054932}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       4,       4,  ..., 2499986,
-                            2499991, 2500000]),
-       col_indices=tensor([  4708,  62252, 239037,  ..., 346193, 443276,
-                           467019]),
-       values=tensor([0.7542, 0.0207, 0.5398,  ..., 0.0649, 0.4673, 0.8331]),
-       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8471, 0.4897, 0.4001,  ..., 0.3407, 0.6143, 0.4869])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 2500000
-Density: 1e-05
-Time: 12.429174900054932 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       4,       4,  ..., 2499986,
-                            2499991, 2500000]),
-       col_indices=tensor([  4708,  62252, 239037,  ..., 346193, 443276,
-                           467019]),
-       values=tensor([0.7542, 0.0207, 0.5398,  ..., 0.0649, 0.4673, 0.8331]),
-       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8471, 0.4897, 0.4001,  ..., 0.3407, 0.6143, 0.4869])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 2500000
-Density: 1e-05
-Time: 12.429174900054932 seconds
-
-[17.72, 17.72, 17.84, 17.8, 17.8, 18.04, 17.92, 17.8, 17.72, 17.32]
-[17.36, 17.64, 18.04, 20.2, 21.44, 35.64, 48.92, 66.32, 77.96, 77.96, 91.04, 90.76, 89.6, 87.56, 86.4, 84.76]
-16.53397512435913
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 279, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 12.429174900054932, 'TIME_S_1KI': 44.549013978691505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 925.7997315406801, 'W': 55.99377793769148}
-[17.72, 17.72, 17.84, 17.8, 17.8, 18.04, 17.92, 17.8, 17.72, 17.32, 17.84, 17.72, 17.8, 17.92, 17.8, 17.72, 17.6, 17.72, 17.72, 17.48]
-319.82
-15.991
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 279, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 12.429174900054932, 'TIME_S_1KI': 44.549013978691505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 925.7997315406801, 'W': 55.99377793769148, 'J_1KI': 3318.2786076726884, 'W_1KI': 200.69454457953935, 'W_D': 40.002777937691484, 'J_D': 661.4049353270533, 'W_D_1KI': 143.37913239315944, 'J_D_1KI': 513.9037003339048}
+[20.96, 20.96, 21.12, 21.32, 21.32, 21.32, 21.44, 20.96, 20.88, 20.92]
+[21.0, 20.88, 21.44, 25.64, 27.72, 39.36, 56.0, 68.8, 68.8, 84.4, 92.08, 95.0, 94.44, 95.52, 92.8]
+15.415502548217773
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 364, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 12.988611936569214, 'TIME_S_1KI': 35.6829998257396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 885.5386364746093, 'W': 57.44468165762061}
+[20.96, 20.96, 21.12, 21.32, 21.32, 21.32, 21.44, 20.96, 20.88, 20.92, 20.56, 20.8, 20.88, 21.08, 21.44, 21.96, 21.92, 21.8, 21.8, 21.76]
+383.1
+19.155
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 364, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 12.988611936569214, 'TIME_S_1KI': 35.6829998257396, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 885.5386364746093, 'W': 57.44468165762061, 'J_1KI': 2432.798451853322, 'W_1KI': 157.8150594989577, 'W_D': 38.28968165762061, 'J_D': 590.2546851634979, 'W_D_1KI': 105.19143312533136, 'J_D_1KI': 288.9874536410202}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_5e-05.json
index fd07ea0..aaa275d 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_5e-05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 16.55699610710144, "TIME_S_1KI": 165.5699610710144, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1413.1975269508362, "W": 57.485721407839506, "J_1KI": 14131.97526950836, "W_1KI": 574.857214078395, "W_D": 41.59072140783951, "J_D": 1022.44354246974, "W_D_1KI": 415.9072140783951, "J_D_1KI": 4159.072140783951}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 61, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.86849045753479, "TIME_S_1KI": 178.1719747136851, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 749.9812630844115, "W": 45.45168786127534, "J_1KI": 12294.774804662482, "W_1KI": 745.1096370700875, "W_D": 26.504687861275343, "J_D": 437.3439186797141, "W_D_1KI": 434.50307969303844, "J_D_1KI": 7123.001306443253}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_5e-05.output
index 33e7f40..48f5d7a 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_5e-05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_500000_5e-05.output
@@ -1,15 +1,15 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 16.55699610710144}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10 -ss 500000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.7125613689422607}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       27,       44,  ..., 12499945,
-                            12499971, 12500000]),
-       col_indices=tensor([  7086,  20899,  31000,  ..., 441979, 480995,
-                           482795]),
-       values=tensor([0.3494, 0.1791, 0.5321,  ..., 0.0256, 0.8127, 0.6614]),
+tensor(crow_indices=tensor([       0,       24,       51,  ..., 12499946,
+                            12499976, 12500000]),
+       col_indices=tensor([  5171,  17970,  38847,  ..., 438477, 489000,
+                           492656]),
+       values=tensor([0.9313, 0.2908, 0.9600,  ..., 0.3041, 0.7302, 0.1974]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.9181, 0.2128, 0.6316,  ..., 0.6360, 0.7946, 0.3835])
+tensor([0.9523, 0.8640, 0.6914,  ..., 0.9451, 0.6561, 0.2747])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,17 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 16.55699610710144 seconds
+Time: 1.7125613689422607 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 61 -ss 500000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.86849045753479}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       27,       44,  ..., 12499945,
-                            12499971, 12500000]),
-       col_indices=tensor([  7086,  20899,  31000,  ..., 441979, 480995,
-                           482795]),
-       values=tensor([0.3494, 0.1791, 0.5321,  ..., 0.0256, 0.8127, 0.6614]),
+tensor(crow_indices=tensor([       0,       25,       52,  ..., 12499949,
+                            12499973, 12500000]),
+       col_indices=tensor([  1262,  25976,  29054,  ..., 430668, 440930,
+                           487529]),
+       values=tensor([0.7736, 0.2160, 0.8850,  ..., 0.0805, 0.0894, 0.3255]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.9181, 0.2128, 0.6316,  ..., 0.6360, 0.7946, 0.3835])
+tensor([0.0040, 0.9165, 0.7576,  ..., 0.7760, 0.7936, 0.1567])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -35,13 +38,31 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 16.55699610710144 seconds
+Time: 10.86849045753479 seconds
 
-[17.52, 17.52, 17.88, 17.84, 17.84, 17.64, 17.48, 17.72, 17.44, 17.28]
-[17.4, 17.16, 20.64, 21.36, 23.44, 25.32, 33.76, 33.76, 31.56, 35.12, 47.32, 60.6, 67.04, 80.92, 86.0, 85.72, 87.24, 88.0, 90.8, 92.64, 93.72, 92.0, 92.0, 90.8]
-24.583452939987183
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 16.55699610710144, 'TIME_S_1KI': 165.5699610710144, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1413.1975269508362, 'W': 57.485721407839506}
-[17.52, 17.52, 17.88, 17.84, 17.84, 17.64, 17.48, 17.72, 17.44, 17.28, 18.0, 17.76, 17.6, 17.64, 17.44, 17.4, 17.8, 17.88, 17.76, 17.72]
-317.9
-15.895
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 16.55699610710144, 'TIME_S_1KI': 165.5699610710144, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1413.1975269508362, 'W': 57.485721407839506, 'J_1KI': 14131.97526950836, 'W_1KI': 574.857214078395, 'W_D': 41.59072140783951, 'J_D': 1022.44354246974, 'W_D_1KI': 415.9072140783951, 'J_D_1KI': 4159.072140783951}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       25,       52,  ..., 12499949,
+                            12499973, 12500000]),
+       col_indices=tensor([  1262,  25976,  29054,  ..., 430668, 440930,
+                           487529]),
+       values=tensor([0.7736, 0.2160, 0.8850,  ..., 0.0805, 0.0894, 0.3255]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0040, 0.9165, 0.7576,  ..., 0.7760, 0.7936, 0.1567])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.86849045753479 seconds
+
+[21.36, 21.48, 21.52, 21.4, 21.32, 21.12, 21.04, 21.04, 21.08, 21.16]
+[21.12, 21.04, 21.2, 22.68, 23.64, 25.64, 31.92, 35.32, 36.08, 46.32, 59.48, 69.52, 69.52, 82.92, 96.08, 98.0]
+16.500625133514404
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 61, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.86849045753479, 'TIME_S_1KI': 178.1719747136851, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 749.9812630844115, 'W': 45.45168786127534}
+[21.36, 21.48, 21.52, 21.4, 21.32, 21.12, 21.04, 21.04, 21.08, 21.16, 20.92, 21.0, 20.96, 20.84, 21.0, 21.04, 20.92, 20.64, 20.56, 20.52]
+378.93999999999994
+18.946999999999996
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 61, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.86849045753479, 'TIME_S_1KI': 178.1719747136851, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 749.9812630844115, 'W': 45.45168786127534, 'J_1KI': 12294.774804662482, 'W_1KI': 745.1096370700875, 'W_D': 26.504687861275343, 'J_D': 437.3439186797141, 'W_D_1KI': 434.50307969303844, 'J_D_1KI': 7123.001306443253}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.json
index 9346872..100c092 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1658, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 14.466236591339111, "TIME_S_1KI": 8.725112540011526, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1691.1909151077273, "W": 65.36355506616682, "J_1KI": 1020.0186460239609, "W_1KI": 39.423133333031856, "W_D": 49.293555066166824, "J_D": 1275.40205573082, "W_D_1KI": 29.730732850522816, "J_D_1KI": 17.93168446955538}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1354, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 13.57641315460205, "TIME_S_1KI": 10.026893024078325, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 978.2523400211333, "W": 59.41936290336582, "J_1KI": 722.4906499417527, "W_1KI": 43.884315290521286, "W_D": 40.69436290336582, "J_D": 669.9727797591684, "W_D_1KI": 30.05492090351981, "J_D_1KI": 22.197135083840333}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.output
index 686a0dc..0d4fdd4 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.8603906631469727}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10 -ss 50000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.12745881080627441}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     11,  ..., 249990, 249996,
+tensor(crow_indices=tensor([     0,      5,      7,  ..., 249993, 249996,
                             250000]),
-       col_indices=tensor([  116,  2220,  2597,  ..., 31423, 34504, 36695]),
-       values=tensor([0.0356, 0.7526, 0.0114,  ..., 0.2051, 0.2717, 0.4326]),
+       col_indices=tensor([32413, 35033, 38444,  ..., 17723, 37171, 38352]),
+       values=tensor([0.4501, 0.5081, 0.6641,  ..., 0.4983, 0.1681, 0.9317]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.6822, 0.2973, 0.4245,  ..., 0.4266, 0.4462, 0.3842])
+tensor([0.7699, 0.9328, 0.9800,  ..., 0.6628, 0.4452, 0.0772])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 0.8603906631469727 seconds
+Time: 0.12745881080627441 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1220 -ss 50000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.725424766540527}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 823 -ss 50000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.379318475723267}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,     12,  ..., 249990, 249995,
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 249994, 249999,
                             250000]),
-       col_indices=tensor([ 1339, 17035, 23748,  ..., 19329, 30492, 33219]),
-       values=tensor([0.1487, 0.4152, 0.3651,  ..., 0.6580, 0.7478, 0.7026]),
+       col_indices=tensor([19687, 37471,  1460,  ..., 44613, 48031, 39271]),
+       values=tensor([0.5848, 0.6955, 0.1599,  ..., 0.4498, 0.0484, 0.3845]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4336, 0.1383, 0.7901,  ..., 0.8031, 0.7630, 0.0295])
+tensor([0.7337, 0.3585, 0.9639,  ..., 0.1882, 0.5518, 0.0852])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 7.725424766540527 seconds
+Time: 6.379318475723267 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1658 -ss 50000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 14.466236591339111}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1354 -ss 50000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 13.57641315460205}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      7,  ..., 249989, 249992,
+tensor(crow_indices=tensor([     0,      6,      9,  ..., 249989, 249994,
                             250000]),
-       col_indices=tensor([14878, 23641, 26434,  ..., 39221, 43609, 44125]),
-       values=tensor([0.9348, 0.1734, 0.7472,  ..., 0.0129, 0.0523, 0.4218]),
+       col_indices=tensor([ 7528,  9953, 13162,  ..., 34281, 39345, 47690]),
+       values=tensor([0.1278, 0.6541, 0.4504,  ..., 0.2745, 0.8378, 0.9762]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4659, 0.1302, 0.1589,  ..., 0.1214, 0.1279, 0.7413])
+tensor([0.9309, 0.5895, 0.4899,  ..., 0.9489, 0.4389, 0.2461])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 14.466236591339111 seconds
+Time: 13.57641315460205 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,      7,  ..., 249989, 249992,
+tensor(crow_indices=tensor([     0,      6,      9,  ..., 249989, 249994,
                             250000]),
-       col_indices=tensor([14878, 23641, 26434,  ..., 39221, 43609, 44125]),
-       values=tensor([0.9348, 0.1734, 0.7472,  ..., 0.0129, 0.0523, 0.4218]),
+       col_indices=tensor([ 7528,  9953, 13162,  ..., 34281, 39345, 47690]),
+       values=tensor([0.1278, 0.6541, 0.4504,  ..., 0.2745, 0.8378, 0.9762]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4659, 0.1302, 0.1589,  ..., 0.1214, 0.1279, 0.7413])
+tensor([0.9309, 0.5895, 0.4899,  ..., 0.9489, 0.4389, 0.2461])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 14.466236591339111 seconds
+Time: 13.57641315460205 seconds
 
-[18.12, 18.08, 18.12, 18.12, 18.08, 18.0, 17.76, 17.64, 17.48, 17.48]
-[17.28, 17.4, 17.28, 19.64, 20.44, 35.08, 49.0, 64.92, 77.28, 86.8, 86.8, 86.52, 85.52, 84.64, 84.32, 84.0, 84.16, 84.16, 84.24, 84.24, 84.04, 84.28, 82.92, 82.4, 81.88]
-25.87360668182373
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1658, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 14.466236591339111, 'TIME_S_1KI': 8.725112540011526, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1691.1909151077273, 'W': 65.36355506616682}
-[18.12, 18.08, 18.12, 18.12, 18.08, 18.0, 17.76, 17.64, 17.48, 17.48, 18.04, 17.84, 17.8, 17.64, 17.76, 17.92, 17.8, 17.8, 17.72, 18.04]
-321.4
-16.07
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1658, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 14.466236591339111, 'TIME_S_1KI': 8.725112540011526, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1691.1909151077273, 'W': 65.36355506616682, 'J_1KI': 1020.0186460239609, 'W_1KI': 39.423133333031856, 'W_D': 49.293555066166824, 'J_D': 1275.40205573082, 'W_D_1KI': 29.730732850522816, 'J_D_1KI': 17.93168446955538}
+[20.84, 20.88, 20.92, 20.8, 20.88, 20.96, 20.96, 20.72, 20.84, 20.88]
+[20.8, 20.96, 21.12, 23.52, 25.24, 42.44, 56.84, 72.36, 86.48, 91.96, 91.12, 89.24, 89.24, 87.4, 85.72, 87.4]
+16.46352791786194
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1354, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 13.57641315460205, 'TIME_S_1KI': 10.026893024078325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 978.2523400211333, 'W': 59.41936290336582}
+[20.84, 20.88, 20.92, 20.8, 20.88, 20.96, 20.96, 20.72, 20.84, 20.88, 20.48, 20.64, 20.76, 20.96, 21.0, 21.08, 20.84, 20.56, 20.4, 20.4]
+374.5
+18.725
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1354, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 13.57641315460205, 'TIME_S_1KI': 10.026893024078325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 978.2523400211333, 'W': 59.41936290336582, 'J_1KI': 722.4906499417527, 'W_1KI': 43.884315290521286, 'W_D': 40.69436290336582, 'J_D': 669.9727797591684, 'W_D_1KI': 30.05492090351981, 'J_D_1KI': 22.197135083840333}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.json
index c314337..da68769 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 135, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.963478088378906, "TIME_S_1KI": 81.21094880280671, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 582.8568821525573, "W": 43.83509814272925, "J_1KI": 4317.458386315239, "W_1KI": 324.7044306868833, "W_D": 28.06209814272925, "J_D": 373.12992837095254, "W_D_1KI": 207.8673936498463, "J_D_1KI": 1539.758471480343}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 239, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 12.746366024017334, "TIME_S_1KI": 53.33207541429847, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 629.8619846343995, "W": 47.3255940324987, "J_1KI": 2635.4057934493703, "W_1KI": 198.0150377928816, "W_D": 28.3985940324987, "J_D": 377.96028055882465, "W_D_1KI": 118.82256917363472, "J_D_1KI": 497.16556139596116}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.output
index 514e8a4..6db1157 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.26869797706604}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10 -ss 50000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.6939270496368408}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      44,     102,  ..., 2499900,
-                            2499955, 2500000]),
-       col_indices=tensor([  878,  3105,  3271,  ..., 44510, 45389, 45985]),
-       values=tensor([0.2421, 0.0051, 0.2486,  ..., 0.1294, 0.9249, 0.4412]),
+tensor(crow_indices=tensor([      0,      66,     106,  ..., 2499911,
+                            2499950, 2500000]),
+       col_indices=tensor([  134,   759,   866,  ..., 44498, 44758, 48976]),
+       values=tensor([0.2990, 0.7798, 0.5925,  ..., 0.3339, 0.3471, 0.5198]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3218, 0.5110, 0.5510,  ..., 0.0407, 0.3623, 0.3415])
+tensor([0.2796, 0.3993, 0.3429,  ..., 0.1835, 0.2482, 0.6761])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 8.26869797706604 seconds
+Time: 0.6939270496368408 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 126 -ss 50000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.729291677474976}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 151 -ss 50000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.231975555419922}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      44,     101,  ..., 2499906,
-                            2499957, 2500000]),
-       col_indices=tensor([  430,  2871,  2934,  ..., 46471, 47392, 47877]),
-       values=tensor([0.4189, 0.2667, 0.2640,  ..., 0.7329, 0.4126, 0.0437]),
+tensor(crow_indices=tensor([      0,      52,      98,  ..., 2499895,
+                            2499944, 2500000]),
+       col_indices=tensor([  493,   755,  1559,  ..., 48761, 49039, 49905]),
+       values=tensor([0.9495, 0.4314, 0.0398,  ..., 0.4761, 0.8641, 0.4230]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6895, 0.8779, 0.8508,  ..., 0.5330, 0.3990, 0.7739])
+tensor([0.3749, 0.5339, 0.1389,  ..., 0.8895, 0.3881, 0.8722])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 9.729291677474976 seconds
+Time: 7.231975555419922 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 135 -ss 50000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.963478088378906}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 219 -ss 50000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.581777334213257}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      51,     106,  ..., 2499884,
-                            2499945, 2500000]),
-       col_indices=tensor([ 1316,  2608,  2921,  ..., 47281, 49169, 49691]),
-       values=tensor([0.1237, 0.8262, 0.6046,  ..., 0.7531, 0.6389, 0.8086]),
+tensor(crow_indices=tensor([      0,      35,      91,  ..., 2499916,
+                            2499963, 2500000]),
+       col_indices=tensor([  597,   922,   947,  ..., 43933, 45150, 49587]),
+       values=tensor([0.2433, 0.0550, 0.3468,  ..., 0.1153, 0.4739, 0.8564]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.9516, 0.0943, 0.1293,  ..., 0.9488, 0.5626, 0.5458])
+tensor([0.1386, 0.9905, 0.1855,  ..., 0.6873, 0.7628, 0.6710])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.963478088378906 seconds
+Time: 9.581777334213257 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 239 -ss 50000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 12.746366024017334}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      51,     106,  ..., 2499884,
-                            2499945, 2500000]),
-       col_indices=tensor([ 1316,  2608,  2921,  ..., 47281, 49169, 49691]),
-       values=tensor([0.1237, 0.8262, 0.6046,  ..., 0.7531, 0.6389, 0.8086]),
+tensor(crow_indices=tensor([      0,      55,     109,  ..., 2499908,
+                            2499954, 2500000]),
+       col_indices=tensor([ 1697,  2355,  3243,  ..., 48565, 49427, 49466]),
+       values=tensor([0.6723, 0.4018, 0.1468,  ..., 0.2877, 0.1034, 0.7125]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.9516, 0.0943, 0.1293,  ..., 0.9488, 0.5626, 0.5458])
+tensor([0.9538, 0.9945, 0.0722,  ..., 0.6194, 0.2993, 0.0465])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +76,30 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.963478088378906 seconds
+Time: 12.746366024017334 seconds
 
-[17.96, 17.88, 17.56, 17.44, 17.36, 17.24, 17.44, 17.68, 17.68, 17.68]
-[17.88, 17.88, 17.88, 22.04, 23.04, 29.76, 42.2, 54.16, 64.96, 76.44, 81.04, 80.44, 80.44]
-13.296579837799072
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 135, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.963478088378906, 'TIME_S_1KI': 81.21094880280671, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 582.8568821525573, 'W': 43.83509814272925}
-[17.96, 17.88, 17.56, 17.44, 17.36, 17.24, 17.44, 17.68, 17.68, 17.68, 17.84, 17.6, 17.48, 17.48, 17.52, 17.32, 17.4, 17.36, 17.44, 17.68]
-315.46
-15.773
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 135, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.963478088378906, 'TIME_S_1KI': 81.21094880280671, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 582.8568821525573, 'W': 43.83509814272925, 'J_1KI': 4317.458386315239, 'W_1KI': 324.7044306868833, 'W_D': 28.06209814272925, 'J_D': 373.12992837095254, 'W_D_1KI': 207.8673936498463, 'J_D_1KI': 1539.758471480343}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      55,     109,  ..., 2499908,
+                            2499954, 2500000]),
+       col_indices=tensor([ 1697,  2355,  3243,  ..., 48565, 49427, 49466]),
+       values=tensor([0.6723, 0.4018, 0.1468,  ..., 0.2877, 0.1034, 0.7125]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9538, 0.9945, 0.0722,  ..., 0.6194, 0.2993, 0.0465])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 12.746366024017334 seconds
+
+[21.0, 21.08, 20.84, 20.84, 20.96, 21.08, 21.08, 20.8, 21.0, 21.32]
+[21.6, 21.64, 24.52, 26.32, 30.72, 42.16, 42.16, 57.76, 66.2, 78.64, 80.6, 81.8, 82.24]
+13.309119462966919
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 239, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 12.746366024017334, 'TIME_S_1KI': 53.33207541429847, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 629.8619846343995, 'W': 47.3255940324987}
+[21.0, 21.08, 20.84, 20.84, 20.96, 21.08, 21.08, 20.8, 21.0, 21.32, 21.08, 21.2, 21.44, 21.36, 20.92, 20.96, 21.0, 20.84, 20.84, 21.2]
+378.54
+18.927
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 239, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 12.746366024017334, 'TIME_S_1KI': 53.33207541429847, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 629.8619846343995, 'W': 47.3255940324987, 'J_1KI': 2635.4057934493703, 'W_1KI': 198.0150377928816, 'W_D': 28.3985940324987, 'J_D': 377.96028055882465, 'W_D_1KI': 118.82256917363472, 'J_D_1KI': 497.16556139596116}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.01.json
index de9236a..1861861 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.01.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.01.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 30.498249053955078, "TIME_S_1KI": 304.9824905395508, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2007.0959778976435, "W": 47.944381683758614, "J_1KI": 20070.959778976434, "W_1KI": 479.44381683758616, "W_D": 32.291381683758615, "J_D": 1351.8143319842811, "W_D_1KI": 322.9138168375861, "J_D_1KI": 3229.138168375861}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 30, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.391724348068237, "TIME_S_1KI": 346.3908116022746, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 833.8964260101318, "W": 36.84918723219108, "J_1KI": 27796.54753367106, "W_1KI": 1228.3062410730358, "W_D": 17.98218723219108, "J_D": 406.9365647139549, "W_D_1KI": 599.406241073036, "J_D_1KI": 19980.208035767868}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.01.output
index 9e427ef..adbb5c5 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.01.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_0.01.output
@@ -1,14 +1,14 @@
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 30.498249053955078}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10 -ss 50000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.4301743507385254}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      519,     1009,  ..., 24999042,
-                            24999488, 25000000]),
-       col_indices=tensor([  129,   342,   437,  ..., 49566, 49630, 49865]),
-       values=tensor([0.8700, 0.4704, 0.0527,  ..., 0.4978, 0.7115, 0.5319]),
+tensor(crow_indices=tensor([       0,      499,     1026,  ..., 24999002,
+                            24999506, 25000000]),
+       col_indices=tensor([   76,   152,   185,  ..., 49731, 49807, 49819]),
+       values=tensor([0.9174, 0.2553, 0.8878,  ..., 0.4714, 0.3776, 0.8139]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.3352, 0.1102, 0.4216,  ..., 0.1668, 0.6074, 0.9924])
+tensor([0.8947, 0.0925, 0.0743,  ..., 0.5161, 0.0685, 0.1122])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,16 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 30.498249053955078 seconds
+Time: 3.4301743507385254 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 30 -ss 50000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.391724348068237}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      519,     1009,  ..., 24999042,
-                            24999488, 25000000]),
-       col_indices=tensor([  129,   342,   437,  ..., 49566, 49630, 49865]),
-       values=tensor([0.8700, 0.4704, 0.0527,  ..., 0.4978, 0.7115, 0.5319]),
+tensor(crow_indices=tensor([       0,      555,     1044,  ..., 24999009,
+                            24999484, 25000000]),
+       col_indices=tensor([  323,   369,   402,  ..., 49562, 49819, 49853]),
+       values=tensor([0.9309, 0.0394, 0.6812,  ..., 0.9223, 0.0551, 0.5539]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.3352, 0.1102, 0.4216,  ..., 0.1668, 0.6074, 0.9924])
+tensor([0.2747, 0.6581, 0.2131,  ..., 0.8836, 0.0051, 0.2302])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -33,13 +36,30 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 30.498249053955078 seconds
+Time: 10.391724348068237 seconds
 
-[17.68, 17.6, 17.56, 17.16, 17.16, 17.0, 17.0, 17.08, 17.36, 17.4]
-[17.52, 17.72, 18.04, 18.88, 20.68, 21.64, 23.68, 24.04, 31.04, 33.36, 33.36, 32.8, 32.72, 32.52, 28.16, 34.64, 41.2, 49.36, 57.8, 58.4, 58.92, 62.72, 62.72, 61.04, 61.04, 59.8, 60.88, 61.56, 64.04, 67.12, 70.76, 68.32, 68.92, 65.8, 66.72, 65.88, 65.84, 68.88, 68.88, 67.08, 67.04]
-41.863006830215454
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 30.498249053955078, 'TIME_S_1KI': 304.9824905395508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2007.0959778976435, 'W': 47.944381683758614}
-[17.68, 17.6, 17.56, 17.16, 17.16, 17.0, 17.0, 17.08, 17.36, 17.4, 17.56, 17.4, 17.44, 17.44, 17.4, 17.6, 17.56, 17.52, 17.6, 17.72]
-313.05999999999995
-15.652999999999997
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 30.498249053955078, 'TIME_S_1KI': 304.9824905395508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2007.0959778976435, 'W': 47.944381683758614, 'J_1KI': 20070.959778976434, 'W_1KI': 479.44381683758616, 'W_D': 32.291381683758615, 'J_D': 1351.8143319842811, 'W_D_1KI': 322.9138168375861, 'J_D_1KI': 3229.138168375861}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      555,     1044,  ..., 24999009,
+                            24999484, 25000000]),
+       col_indices=tensor([  323,   369,   402,  ..., 49562, 49819, 49853]),
+       values=tensor([0.9309, 0.0394, 0.6812,  ..., 0.9223, 0.0551, 0.5539]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.2747, 0.6581, 0.2131,  ..., 0.8836, 0.0051, 0.2302])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.391724348068237 seconds
+
+[20.92, 20.72, 21.0, 21.32, 21.32, 21.24, 21.08, 20.76, 20.76, 20.76]
+[20.88, 20.88, 21.2, 23.44, 24.12, 25.32, 27.52, 28.24, 30.8, 39.0, 39.28, 38.24, 38.24, 38.36, 32.76, 39.0, 45.44, 53.84, 59.64, 63.36, 63.0, 63.52]
+22.629981517791748
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 30, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.391724348068237, 'TIME_S_1KI': 346.3908116022746, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 833.8964260101318, 'W': 36.84918723219108}
+[20.92, 20.72, 21.0, 21.32, 21.32, 21.24, 21.08, 20.76, 20.76, 20.76, 21.0, 20.76, 20.76, 20.68, 20.6, 20.8, 21.16, 21.16, 21.28, 21.2]
+377.34
+18.866999999999997
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 30, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.391724348068237, 'TIME_S_1KI': 346.3908116022746, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 833.8964260101318, 'W': 36.84918723219108, 'J_1KI': 27796.54753367106, 'W_1KI': 1228.3062410730358, 'W_D': 17.98218723219108, 'J_D': 406.9365647139549, 'W_D_1KI': 599.406241073036, 'J_D_1KI': 19980.208035767868}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.json
index 5f4cac5..f973965 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 8811, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 14.461963891983032, "TIME_S_1KI": 1.6413532961052129, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1601.2496216583254, "W": 63.889106877708535, "J_1KI": 181.7330180068466, "W_1KI": 7.251061954115144, "W_D": 47.822106877708535, "J_D": 1198.5631712055208, "W_D_1KI": 5.4275458946440285, "J_D_1KI": 0.6159965832078116}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 7814, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 12.055179357528687, "TIME_S_1KI": 1.5427667465483346, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1079.0575048923492, "W": 61.106530536994505, "J_1KI": 138.0928467996352, "W_1KI": 7.820134442922256, "W_D": 42.1665305369945, "J_D": 744.6030863046644, "W_D_1KI": 5.396279823009278, "J_D_1KI": 0.6905912238302122}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.output
index 6d049d2..537f362 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.2421858310699463}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.19840216636657715}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     2,     2,  ..., 24999, 25000, 25000]),
-       col_indices=tensor([11801, 48673, 42443,  ..., 35599, 34008, 22453]),
-       values=tensor([0.3951, 0.6998, 0.6224,  ..., 0.4352, 0.5927, 0.1013]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([40039, 46168, 43905,  ...,  5354, 30188, 25611]),
+       values=tensor([0.7128, 0.9467, 0.3393,  ..., 0.8693, 0.4957, 0.7713]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.6391, 0.6971, 0.7049,  ..., 0.7658, 0.2053, 0.9702])
+tensor([0.6738, 0.8011, 0.8829,  ..., 0.3394, 0.1656, 0.5423])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,19 +15,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.2421858310699463 seconds
+Time: 0.19840216636657715 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4335 -ss 50000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.2435102462768555}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 5292 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 8.100382566452026}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([48470, 32812, 24371,  ..., 21811,  5693, 27792]),
-       values=tensor([7.9337e-01, 8.6969e-01, 2.1228e-02,  ...,
-                      3.1628e-01, 5.2154e-01, 8.3659e-04]),
+tensor(crow_indices=tensor([    0,     2,     3,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([23900, 46764, 22189,  ..., 17050, 32623, 22770]),
+       values=tensor([0.3210, 0.3821, 0.9500,  ..., 0.5752, 0.4593, 0.0859]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5123, 0.3871, 0.5639,  ..., 0.2434, 0.7885, 0.9337])
+tensor([0.4131, 0.4291, 0.1695,  ..., 0.7277, 0.7700, 0.1738])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -35,18 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 6.2435102462768555 seconds
+Time: 8.100382566452026 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 7290 -ss 50000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.1812264919281}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 6859 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.215805768966675}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
-       col_indices=tensor([ 1561, 34915, 11685,  ...,  9985, 24943, 27218]),
-       values=tensor([0.3207, 0.5476, 0.7721,  ..., 0.5221, 0.2072, 0.7139]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 4953, 12651, 31779,  ..., 28640, 12333, 41102]),
+       values=tensor([0.6176, 0.0952, 0.2603,  ..., 0.5516, 0.6412, 0.0213]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7737, 0.6011, 0.2764,  ..., 0.4575, 0.9058, 0.1946])
+tensor([0.4613, 0.6467, 0.1961,  ..., 0.3830, 0.8289, 0.7018])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -54,18 +53,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 9.1812264919281 seconds
+Time: 9.215805768966675 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 8337 -ss 50000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.935004234313965}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 7814 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 12.055179357528687}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 24999, 25000, 25000]),
-       col_indices=tensor([ 4660, 37796, 36819,  ..., 14791,   855,   165]),
-       values=tensor([0.6560, 0.1119, 0.0106,  ..., 0.5425, 0.3178, 0.8843]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 25000, 25000]),
+       col_indices=tensor([ 5385, 33159, 44473,  ..., 49968, 12582, 30371]),
+       values=tensor([0.5619, 0.7037, 0.4090,  ..., 0.1971, 0.5585, 0.9974]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7672, 0.9593, 0.0238,  ..., 0.4054, 0.5730, 0.7422])
+tensor([0.0784, 0.9306, 0.8133,  ..., 0.4010, 0.5072, 0.2577])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,18 +72,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 9.935004234313965 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 8811 -ss 50000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 14.461963891983032}
+Time: 12.055179357528687 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 24997, 24998, 25000]),
-       col_indices=tensor([ 8691, 13268, 31788,  ..., 20611,  3075,  9688]),
-       values=tensor([0.8778, 0.6640, 0.4350,  ..., 0.9614, 0.5782, 0.7592]),
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24998, 25000, 25000]),
+       col_indices=tensor([ 5385, 33159, 44473,  ..., 49968, 12582, 30371]),
+       values=tensor([0.5619, 0.7037, 0.4090,  ..., 0.1971, 0.5585, 0.9974]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9817, 0.3893, 0.5308,  ..., 0.4402, 0.5461, 0.2337])
+tensor([0.0784, 0.9306, 0.8133,  ..., 0.4010, 0.5072, 0.2577])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -92,29 +88,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 14.461963891983032 seconds
+Time: 12.055179357528687 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 24997, 24998, 25000]),
-       col_indices=tensor([ 8691, 13268, 31788,  ..., 20611,  3075,  9688]),
-       values=tensor([0.8778, 0.6640, 0.4350,  ..., 0.9614, 0.5782, 0.7592]),
-       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9817, 0.3893, 0.5308,  ..., 0.4402, 0.5461, 0.2337])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 25000
-Density: 1e-05
-Time: 14.461963891983032 seconds
-
-[18.04, 17.96, 17.84, 17.68, 17.72, 17.8, 17.84, 17.92, 17.8, 17.8]
-[17.8, 17.64, 17.64, 18.72, 19.96, 31.88, 49.36, 63.92, 79.84, 91.32, 91.4, 91.0, 90.8, 90.72, 90.68, 90.12, 89.48, 88.76, 88.76, 87.32, 86.72, 85.68, 84.4, 82.12]
-25.062952041625977
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8811, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 14.461963891983032, 'TIME_S_1KI': 1.6413532961052129, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1601.2496216583254, 'W': 63.889106877708535}
-[18.04, 17.96, 17.84, 17.68, 17.72, 17.8, 17.84, 17.92, 17.8, 17.8, 18.0, 17.84, 17.72, 17.92, 17.96, 17.88, 18.12, 17.92, 17.68, 17.64]
-321.34000000000003
-16.067
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8811, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 14.461963891983032, 'TIME_S_1KI': 1.6413532961052129, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1601.2496216583254, 'W': 63.889106877708535, 'J_1KI': 181.7330180068466, 'W_1KI': 7.251061954115144, 'W_D': 47.822106877708535, 'J_D': 1198.5631712055208, 'W_D_1KI': 5.4275458946440285, 'J_D_1KI': 0.6159965832078116}
+[21.0, 20.88, 21.08, 21.12, 21.12, 21.36, 21.48, 21.28, 21.28, 21.08]
+[20.88, 20.84, 20.84, 21.64, 22.72, 35.92, 51.24, 69.04, 82.6, 94.52, 92.8, 91.8, 90.52, 89.84, 90.16, 90.0, 89.88]
+17.658628225326538
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 7814, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 12.055179357528687, 'TIME_S_1KI': 1.5427667465483346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1079.0575048923492, 'W': 61.106530536994505}
+[21.0, 20.88, 21.08, 21.12, 21.12, 21.36, 21.48, 21.28, 21.28, 21.08, 21.16, 20.88, 20.88, 20.84, 20.84, 20.92, 21.0, 20.92, 20.84, 20.92]
+378.8
+18.94
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 7814, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 12.055179357528687, 'TIME_S_1KI': 1.5427667465483346, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1079.0575048923492, 'W': 61.106530536994505, 'J_1KI': 138.0928467996352, 'W_1KI': 7.820134442922256, 'W_D': 42.1665305369945, 'J_D': 744.6030863046644, 'W_D_1KI': 5.396279823009278, 'J_D_1KI': 0.6905912238302122}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_5e-05.json
index a856634..6f5991f 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_5e-05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 3016, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 12.238471984863281, "TIME_S_1KI": 4.057848801347242, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1021.0734484481814, "W": 58.16446532539802, "J_1KI": 338.5522043926331, "W_1KI": 19.285300174203588, "W_D": 42.31446532539802, "J_D": 742.827717702389, "W_D_1KI": 14.02999513441579, "J_D_1KI": 4.651855150668366}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 2491, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 13.007378578186035, "TIME_S_1KI": 5.2217497303035065, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 968.181078557968, "W": 57.69933801128247, "J_1KI": 388.67164936088636, "W_1KI": 23.163122445316127, "W_D": 38.79533801128247, "J_D": 650.9764841918944, "W_D_1KI": 15.574202332911469, "J_D_1KI": 6.252188812890995}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_5e-05.output
index 0ab6a55..f8cff14 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_5e-05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_50000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.37006235122680664}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.4517648220062256}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      5,  ..., 124994, 124997,
+tensor(crow_indices=tensor([     0,      3,      4,  ..., 124993, 124997,
                             125000]),
-       col_indices=tensor([ 3192, 33329, 36206,  ..., 17521, 36763, 39198]),
-       values=tensor([0.7954, 0.1728, 0.6419,  ..., 0.6370, 0.0715, 0.4891]),
+       col_indices=tensor([ 6450, 12457, 13896,  ..., 11554, 15088, 16580]),
+       values=tensor([0.0565, 0.5167, 0.4504,  ..., 0.4471, 0.0501, 0.7306]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.4965, 0.4773, 0.1313,  ..., 0.0503, 0.1495, 0.6552])
+tensor([0.2612, 0.0403, 0.2390,  ..., 0.0627, 0.5163, 0.3230])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 0.37006235122680664 seconds
+Time: 0.4517648220062256 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 2837 -ss 50000 -sd 5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.87511658668518}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 2324 -ss 50000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.793811798095703}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      4,  ..., 124994, 124997,
+tensor(crow_indices=tensor([     0,      3,      9,  ..., 124992, 124997,
                             125000]),
-       col_indices=tensor([18930, 21812, 35293,  ...,   949,  2935, 28377]),
-       values=tensor([0.1354, 0.7141, 0.1182,  ..., 0.8833, 0.5348, 0.3796]),
+       col_indices=tensor([ 3716,  5281, 36891,  ..., 21656, 39958, 48014]),
+       values=tensor([0.2453, 0.4520, 0.9669,  ..., 0.2730, 0.3289, 0.7482]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.0699, 0.1291, 0.1295,  ..., 0.2384, 0.2084, 0.3934])
+tensor([0.3004, 0.4648, 0.1335,  ..., 0.3195, 0.9169, 0.1653])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 9.87511658668518 seconds
+Time: 9.793811798095703 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3016 -ss 50000 -sd 5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 12.238471984863281}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 2491 -ss 50000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 13.007378578186035}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      8,  ..., 124995, 124998,
+tensor(crow_indices=tensor([     0,      2,      4,  ..., 124995, 124998,
                             125000]),
-       col_indices=tensor([ 1947, 25944, 43942,  ..., 29833,   871, 28509]),
-       values=tensor([0.4807, 0.5814, 0.8403,  ..., 0.1650, 0.5150, 0.8001]),
+       col_indices=tensor([45362, 46706,  9382,  ..., 48043, 47493, 49777]),
+       values=tensor([0.5681, 0.3536, 0.5768,  ..., 0.4220, 0.9986, 0.2845]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.7842, 0.6693, 0.1792,  ..., 0.7791, 0.2472, 0.4723])
+tensor([0.4744, 0.3838, 0.9712,  ..., 0.0971, 0.2301, 0.3333])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 12.238471984863281 seconds
+Time: 13.007378578186035 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      3,      8,  ..., 124995, 124998,
+tensor(crow_indices=tensor([     0,      2,      4,  ..., 124995, 124998,
                             125000]),
-       col_indices=tensor([ 1947, 25944, 43942,  ..., 29833,   871, 28509]),
-       values=tensor([0.4807, 0.5814, 0.8403,  ..., 0.1650, 0.5150, 0.8001]),
+       col_indices=tensor([45362, 46706,  9382,  ..., 48043, 47493, 49777]),
+       values=tensor([0.5681, 0.3536, 0.5768,  ..., 0.4220, 0.9986, 0.2845]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.7842, 0.6693, 0.1792,  ..., 0.7791, 0.2472, 0.4723])
+tensor([0.4744, 0.3838, 0.9712,  ..., 0.0971, 0.2301, 0.3333])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 12.238471984863281 seconds
+Time: 13.007378578186035 seconds
 
-[17.56, 17.48, 17.4, 17.56, 17.72, 17.76, 17.48, 17.56, 17.56, 17.52]
-[17.52, 17.68, 17.76, 20.16, 20.96, 34.48, 49.64, 65.8, 77.8, 88.2, 87.72, 86.68, 86.76, 87.64, 87.64, 89.04, 89.44]
-17.554935693740845
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3016, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 12.238471984863281, 'TIME_S_1KI': 4.057848801347242, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1021.0734484481814, 'W': 58.16446532539802}
-[17.56, 17.48, 17.4, 17.56, 17.72, 17.76, 17.48, 17.56, 17.56, 17.52, 17.72, 17.84, 17.8, 17.68, 17.68, 17.52, 17.56, 17.6, 17.48, 17.84]
-317.0
-15.85
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3016, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 12.238471984863281, 'TIME_S_1KI': 4.057848801347242, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1021.0734484481814, 'W': 58.16446532539802, 'J_1KI': 338.5522043926331, 'W_1KI': 19.285300174203588, 'W_D': 42.31446532539802, 'J_D': 742.827717702389, 'W_D_1KI': 14.02999513441579, 'J_D_1KI': 4.651855150668366}
+[20.96, 21.0, 21.08, 20.88, 20.96, 21.2, 21.32, 21.36, 21.32, 21.32]
+[20.96, 20.88, 21.6, 23.64, 30.64, 30.64, 47.84, 61.28, 75.88, 89.28, 90.92, 90.88, 90.16, 87.96, 86.4, 85.72]
+16.779760599136353
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 2491, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 13.007378578186035, 'TIME_S_1KI': 5.2217497303035065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 968.181078557968, 'W': 57.69933801128247}
+[20.96, 21.0, 21.08, 20.88, 20.96, 21.2, 21.32, 21.36, 21.32, 21.32, 21.04, 21.12, 20.76, 20.84, 20.64, 20.76, 20.8, 20.96, 20.96, 20.92]
+378.08000000000004
+18.904000000000003
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 2491, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 13.007378578186035, 'TIME_S_1KI': 5.2217497303035065, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 968.181078557968, 'W': 57.69933801128247, 'J_1KI': 388.67164936088636, 'W_1KI': 23.163122445316127, 'W_D': 38.79533801128247, 'J_D': 650.9764841918944, 'W_D_1KI': 15.574202332911469, 'J_D_1KI': 6.252188812890995}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.json
index fa80299..5155f94 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 93664, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.030004501342773, "TIME_S_1KI": 0.107084947272621, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 253.85587940216067, "W": 17.876929919933424, "J_1KI": 2.7102822792338643, "W_1KI": 0.19086233686297213, "W_D": 2.859929919933423, "J_D": 40.61156071567538, "W_D_1KI": 0.03053392893676784, "J_D_1KI": 0.00032599428741851555}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 97563, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.701276540756226, "TIME_S_1KI": 0.10968580856222365, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 307.9483871459961, "W": 21.602081513270264, "J_1KI": 3.156405472832899, "W_1KI": 0.2214167411136421, "W_D": 3.030081513270261, "J_D": 43.19531496810911, "W_D_1KI": 0.0310576910639306, "J_D_1KI": 0.00031833472795968345}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.output
index c2bcc94..d679ce7 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.018899202346801758}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.01863241195678711}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([1031, 4368, 4092,  ...,  190, 4399, 1962]),
-       values=tensor([0.5248, 0.7492, 0.4749,  ..., 0.9820, 0.4892, 0.6710]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([2466, 3111,  709,  ..., 1729, 3787, 1090]),
+       values=tensor([0.6942, 0.7590, 0.7512,  ..., 0.6465, 0.5765, 0.6510]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.7298, 0.5753, 0.5464,  ..., 0.2530, 0.5594, 0.0214])
+tensor([0.7305, 0.7014, 0.7646,  ..., 0.2723, 0.1317, 0.4517])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,37 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.018899202346801758 seconds
+Time: 0.01863241195678711 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 55557 -ss 5000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.228086948394775}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    2,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([ 532, 4399, 2173,  ..., 2637, 3554, 2146]),
-       values=tensor([0.0459, 0.9731, 0.3457,  ..., 0.8215, 0.1549, 0.6550]),
-       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.2796, 0.3928, 0.8109,  ..., 0.2089, 0.4148, 0.7694])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 2500
-Density: 0.0001
-Time: 6.228086948394775 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 93664 -ss 5000 -sd 0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.030004501342773}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 56353 -ss 5000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.0648581981658936}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    1,  ..., 2498, 2498, 2500]),
-       col_indices=tensor([1824, 1160, 4169,  ..., 4733, 1262, 4559]),
-       values=tensor([0.9986, 0.6087, 0.2000,  ..., 0.7208, 0.5140, 0.1151]),
+       col_indices=tensor([3295, 3837, 4863,  ..., 1765,  634, 3388]),
+       values=tensor([0.5505, 0.3608, 0.8762,  ..., 0.7549, 0.6265, 0.4131]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.6500, 0.1776, 0.6063,  ..., 0.2915, 0.3213, 0.4804])
+tensor([0.3793, 0.4403, 0.5456,  ..., 0.1478, 0.4802, 0.0889])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.030004501342773 seconds
+Time: 6.0648581981658936 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 97563 -ss 5000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.701276540756226}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 2498, 2498, 2500]),
-       col_indices=tensor([1824, 1160, 4169,  ..., 4733, 1262, 4559]),
-       values=tensor([0.9986, 0.6087, 0.2000,  ..., 0.7208, 0.5140, 0.1151]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([2503, 3491, 1356,  ..., 4170,  132, 3874]),
+       values=tensor([0.5964, 0.9649, 0.4378,  ..., 0.7609, 0.5045, 0.4703]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.6500, 0.1776, 0.6063,  ..., 0.2915, 0.3213, 0.4804])
+tensor([0.5573, 0.9521, 0.0475,  ..., 0.2099, 0.5843, 0.2090])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +53,29 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.030004501342773 seconds
+Time: 10.701276540756226 seconds
 
-[16.8, 16.8, 17.16, 17.24, 17.04, 16.96, 16.96, 17.0, 16.6, 17.0]
-[16.8, 16.84, 17.08, 18.24, 19.2, 19.72, 20.36, 20.2, 20.2, 20.04, 19.8, 19.8, 20.0, 19.96]
-14.200194358825684
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 93664, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.030004501342773, 'TIME_S_1KI': 0.107084947272621, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 253.85587940216067, 'W': 17.876929919933424}
-[16.8, 16.8, 17.16, 17.24, 17.04, 16.96, 16.96, 17.0, 16.6, 17.0, 16.72, 16.48, 16.44, 16.48, 16.32, 16.48, 16.4, 16.28, 16.28, 16.32]
-300.34000000000003
-15.017000000000001
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 93664, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.030004501342773, 'TIME_S_1KI': 0.107084947272621, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 253.85587940216067, 'W': 17.876929919933424, 'J_1KI': 2.7102822792338643, 'W_1KI': 0.19086233686297213, 'W_D': 2.859929919933423, 'J_D': 40.61156071567538, 'W_D_1KI': 0.03053392893676784, 'J_D_1KI': 0.00032599428741851555}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([2503, 3491, 1356,  ..., 4170,  132, 3874]),
+       values=tensor([0.5964, 0.9649, 0.4378,  ..., 0.7609, 0.5045, 0.4703]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.5573, 0.9521, 0.0475,  ..., 0.2099, 0.5843, 0.2090])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.701276540756226 seconds
+
+[20.48, 20.48, 20.52, 20.44, 20.52, 20.52, 20.48, 20.8, 20.92, 20.96]
+[20.92, 21.0, 21.24, 22.08, 23.24, 23.84, 24.64, 24.44, 24.24, 24.24, 23.76, 23.48, 23.64, 23.36]
+14.25549602508545
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 97563, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.701276540756226, 'TIME_S_1KI': 0.10968580856222365, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 307.9483871459961, 'W': 21.602081513270264}
+[20.48, 20.48, 20.52, 20.44, 20.52, 20.52, 20.48, 20.8, 20.92, 20.96, 20.44, 20.32, 20.48, 20.4, 20.44, 20.68, 20.92, 21.08, 21.0, 21.0]
+371.44000000000005
+18.572000000000003
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 97563, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.701276540756226, 'TIME_S_1KI': 0.10968580856222365, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 307.9483871459961, 'W': 21.602081513270264, 'J_1KI': 3.156405472832899, 'W_1KI': 0.2214167411136421, 'W_D': 3.030081513270261, 'J_D': 43.19531496810911, 'W_D_1KI': 0.0310576910639306, 'J_D_1KI': 0.00031833472795968345}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.json
index 37907ba..ac118ca 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 17878, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.553908824920654, "TIME_S_1KI": 0.5903293894686572, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 275.88499256134037, "W": 19.43945882245899, "J_1KI": 15.431535549912763, "W_1KI": 1.0873396813099334, "W_D": 4.574458822458993, "J_D": 64.92076501369485, "W_D_1KI": 0.2558708369201808, "J_D_1KI": 0.014312050392671485}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 17864, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.506640911102295, "TIME_S_1KI": 0.5881460429412391, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 342.50576765060424, "W": 24.054170329528834, "J_1KI": 19.172960571574354, "W_1KI": 1.346516476126782, "W_D": 5.491170329528835, "J_D": 78.18841736173633, "W_D_1KI": 0.30738750165298, "J_D_1KI": 0.017207092569020376}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.output
index e1fffa0..4ba74a9 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06726479530334473}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.10043621063232422}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    11,    16,  ..., 24990, 24996, 25000]),
-       col_indices=tensor([ 219,  546,  972,  ..., 2610, 3216, 3318]),
-       values=tensor([0.2561, 0.1283, 0.3219,  ..., 0.1859, 0.9829, 0.7598]),
+tensor(crow_indices=tensor([    0,     4,     8,  ..., 24992, 24997, 25000]),
+       col_indices=tensor([ 381, 2226, 3241,  ...,  802, 3168, 3287]),
+       values=tensor([0.1743, 0.3626, 0.9122,  ..., 0.6539, 0.6235, 0.9126]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7133, 0.2668, 0.2369,  ..., 0.2811, 0.0980, 0.8981])
+tensor([0.5842, 0.8963, 0.5083,  ..., 0.9307, 0.9302, 0.9679])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.06726479530334473 seconds
+Time: 0.10043621063232422 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 15609 -ss 5000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.167168855667114}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 10454 -ss 5000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 6.144280433654785}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     9,    17,  ..., 24994, 24998, 25000]),
-       col_indices=tensor([ 299, 1244, 1941,  ..., 4267,  280, 4025]),
-       values=tensor([0.2861, 0.6940, 0.5528,  ..., 0.3063, 0.0705, 0.3058]),
+tensor(crow_indices=tensor([    0,     3,     6,  ..., 24993, 24996, 25000]),
+       col_indices=tensor([1192, 2785, 3904,  ...,  226,  840, 1091]),
+       values=tensor([0.3326, 0.6209, 0.1598,  ..., 0.1266, 0.1940, 0.6378]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.7288, 0.9023, 0.1050,  ..., 0.1276, 0.6415, 0.2460])
+tensor([0.7146, 0.7823, 0.7677,  ..., 0.0176, 0.3523, 0.1129])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 9.167168855667114 seconds
+Time: 6.144280433654785 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17878 -ss 5000 -sd 0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.553908824920654}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17864 -ss 5000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.506640911102295}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     6,    13,  ..., 24995, 24998, 25000]),
-       col_indices=tensor([1009, 1198, 2341,  ..., 3808,  999, 4327]),
-       values=tensor([0.4127, 0.3151, 0.8058,  ..., 0.6562, 0.4614, 0.0831]),
+tensor(crow_indices=tensor([    0,     5,    10,  ..., 24987, 24994, 25000]),
+       col_indices=tensor([ 191,  921, 1763,  ..., 2085, 2421, 3701]),
+       values=tensor([0.3338, 0.9400, 0.8684,  ..., 0.9864, 0.7382, 0.7373]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8967, 0.5216, 0.2765,  ..., 0.9189, 0.4761, 0.3303])
+tensor([0.6472, 0.3324, 0.5921,  ..., 0.3050, 0.5964, 0.5620])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.553908824920654 seconds
+Time: 10.506640911102295 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     6,    13,  ..., 24995, 24998, 25000]),
-       col_indices=tensor([1009, 1198, 2341,  ..., 3808,  999, 4327]),
-       values=tensor([0.4127, 0.3151, 0.8058,  ..., 0.6562, 0.4614, 0.0831]),
+tensor(crow_indices=tensor([    0,     5,    10,  ..., 24987, 24994, 25000]),
+       col_indices=tensor([ 191,  921, 1763,  ..., 2085, 2421, 3701]),
+       values=tensor([0.3338, 0.9400, 0.8684,  ..., 0.9864, 0.7382, 0.7373]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8967, 0.5216, 0.2765,  ..., 0.9189, 0.4761, 0.3303])
+tensor([0.6472, 0.3324, 0.5921,  ..., 0.3050, 0.5964, 0.5620])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.553908824920654 seconds
+Time: 10.506640911102295 seconds
 
-[16.32, 16.36, 16.36, 16.56, 16.68, 16.68, 16.64, 16.56, 16.68, 16.52]
-[16.44, 16.88, 16.88, 17.92, 19.12, 25.32, 25.8, 25.92, 25.32, 22.16, 19.68, 19.6, 19.64, 19.4]
-14.192009925842285
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 17878, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.553908824920654, 'TIME_S_1KI': 0.5903293894686572, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 275.88499256134037, 'W': 19.43945882245899}
-[16.32, 16.36, 16.36, 16.56, 16.68, 16.68, 16.64, 16.56, 16.68, 16.52, 16.52, 16.4, 16.4, 16.2, 16.28, 16.36, 16.32, 16.64, 17.04, 16.92]
-297.29999999999995
-14.864999999999998
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 17878, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.553908824920654, 'TIME_S_1KI': 0.5903293894686572, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 275.88499256134037, 'W': 19.43945882245899, 'J_1KI': 15.431535549912763, 'W_1KI': 1.0873396813099334, 'W_D': 4.574458822458993, 'J_D': 64.92076501369485, 'W_D_1KI': 0.2558708369201808, 'J_D_1KI': 0.014312050392671485}
+[20.24, 20.16, 20.08, 20.16, 20.52, 20.6, 20.6, 20.76, 20.88, 20.88]
+[21.0, 20.96, 20.88, 24.4, 25.24, 32.32, 33.08, 30.84, 30.64, 23.88, 23.96, 24.08, 24.08, 23.88]
+14.238934993743896
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 17864, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.506640911102295, 'TIME_S_1KI': 0.5881460429412391, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.50576765060424, 'W': 24.054170329528834}
+[20.24, 20.16, 20.08, 20.16, 20.52, 20.6, 20.6, 20.76, 20.88, 20.88, 20.92, 20.88, 20.76, 20.36, 20.56, 20.6, 21.0, 20.96, 20.96, 20.8]
+371.26
+18.563
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 17864, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.506640911102295, 'TIME_S_1KI': 0.5881460429412391, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 342.50576765060424, 'W': 24.054170329528834, 'J_1KI': 19.172960571574354, 'W_1KI': 1.346516476126782, 'W_D': 5.491170329528835, 'J_D': 78.18841736173633, 'W_D_1KI': 0.30738750165298, 'J_D_1KI': 0.017207092569020376}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.json
index b2ee0da..261bbac 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1934, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.419332265853882, "TIME_S_1KI": 5.38745205059663, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 296.42124423980715, "W": 20.84298720811279, "J_1KI": 153.26848202678755, "W_1KI": 10.777139197576416, "W_D": 5.978987208112789, "J_D": 85.03094157409672, "W_D_1KI": 3.0915135512475644, "J_D_1KI": 1.598507523912908}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1964, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.76218032836914, "TIME_S_1KI": 5.47972521811056, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 334.6203072166443, "W": 23.467862072508055, "J_1KI": 170.3769385013464, "W_1KI": 11.949013275207768, "W_D": 4.762862072508057, "J_D": 67.9120392394066, "W_D_1KI": 2.425082521643613, "J_D_1KI": 1.234767068046646}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.output
index 048fad0..21143e1 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.6361579895019531}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.602431058883667}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     45,     88,  ..., 249912, 249956,
+tensor(crow_indices=tensor([     0,     49,    106,  ..., 249903, 249951,
                             250000]),
-       col_indices=tensor([ 150,  155,  160,  ..., 4906, 4918, 4974]),
-       values=tensor([0.5565, 0.2611, 0.6011,  ..., 0.5545, 0.3341, 0.9118]),
+       col_indices=tensor([  47,  127,  262,  ..., 4669, 4927, 4936]),
+       values=tensor([0.1328, 0.6049, 0.1617,  ..., 0.5221, 0.5231, 0.9043]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3996, 0.8652, 0.4997,  ..., 0.5638, 0.5133, 0.5074])
+tensor([0.1699, 0.4621, 0.4296,  ..., 0.6998, 0.3934, 0.8399])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 0.6361579895019531 seconds
+Time: 0.602431058883667 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1650 -ss 5000 -sd 0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 8.95723843574524}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1742 -ss 5000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.31101369857788}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     48,    105,  ..., 249902, 249944,
+tensor(crow_indices=tensor([     0,     56,    108,  ..., 249894, 249946,
                             250000]),
-       col_indices=tensor([ 142,  192,  269,  ..., 4444, 4647, 4854]),
-       values=tensor([0.2391, 0.6427, 0.3721,  ..., 0.7376, 0.6381, 0.7309]),
+       col_indices=tensor([  29,  153,  200,  ..., 4735, 4743, 4750]),
+       values=tensor([0.5893, 0.8988, 0.3934,  ..., 0.8056, 0.0308, 0.8684]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0904, 0.1755, 0.9906,  ..., 0.2784, 0.3906, 0.1798])
+tensor([0.6292, 0.1193, 0.5842,  ..., 0.0946, 0.4658, 0.4768])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 8.95723843574524 seconds
+Time: 9.31101369857788 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1934 -ss 5000 -sd 0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.419332265853882}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1964 -ss 5000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.76218032836914}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     67,    123,  ..., 249901, 249950,
+tensor(crow_indices=tensor([     0,     51,    101,  ..., 249884, 249941,
                             250000]),
-       col_indices=tensor([  77,  297,  304,  ..., 4744, 4962, 4980]),
-       values=tensor([0.1458, 0.7428, 0.5307,  ..., 0.5713, 0.0836, 0.9823]),
+       col_indices=tensor([ 337,  422,  423,  ..., 4774, 4825, 4844]),
+       values=tensor([0.7788, 0.2591, 0.4387,  ..., 0.3372, 0.5277, 0.6109]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8374, 0.5393, 0.4548,  ..., 0.1972, 0.5711, 0.3877])
+tensor([0.9559, 0.3555, 0.1831,  ..., 0.4118, 0.5326, 0.1704])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.419332265853882 seconds
+Time: 10.76218032836914 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     67,    123,  ..., 249901, 249950,
+tensor(crow_indices=tensor([     0,     51,    101,  ..., 249884, 249941,
                             250000]),
-       col_indices=tensor([  77,  297,  304,  ..., 4744, 4962, 4980]),
-       values=tensor([0.1458, 0.7428, 0.5307,  ..., 0.5713, 0.0836, 0.9823]),
+       col_indices=tensor([ 337,  422,  423,  ..., 4774, 4825, 4844]),
+       values=tensor([0.7788, 0.2591, 0.4387,  ..., 0.3372, 0.5277, 0.6109]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8374, 0.5393, 0.4548,  ..., 0.1972, 0.5711, 0.3877])
+tensor([0.9559, 0.3555, 0.1831,  ..., 0.4118, 0.5326, 0.1704])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.419332265853882 seconds
+Time: 10.76218032836914 seconds
 
-[16.44, 16.56, 16.48, 16.6, 16.72, 16.56, 16.72, 16.6, 16.72, 16.64]
-[16.6, 16.56, 16.56, 20.2, 20.72, 28.04, 28.92, 29.0, 26.44, 26.36, 20.12, 20.28, 20.2, 20.4]
-14.221629619598389
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1934, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.419332265853882, 'TIME_S_1KI': 5.38745205059663, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.42124423980715, 'W': 20.84298720811279}
-[16.44, 16.56, 16.48, 16.6, 16.72, 16.56, 16.72, 16.6, 16.72, 16.64, 16.68, 16.44, 16.44, 16.2, 16.24, 16.32, 16.56, 16.56, 16.52, 16.32]
-297.28
-14.863999999999999
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1934, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.419332265853882, 'TIME_S_1KI': 5.38745205059663, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.42124423980715, 'W': 20.84298720811279, 'J_1KI': 153.26848202678755, 'W_1KI': 10.777139197576416, 'W_D': 5.978987208112789, 'J_D': 85.03094157409672, 'W_D_1KI': 3.0915135512475644, 'J_D_1KI': 1.598507523912908}
+[20.8, 20.8, 20.48, 20.4, 20.8, 21.2, 21.24, 21.52, 21.16, 21.16]
+[20.72, 20.72, 21.88, 22.96, 26.72, 26.72, 27.88, 28.92, 28.12, 27.84, 24.96, 24.72, 24.4, 24.28]
+14.25866174697876
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1964, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.76218032836914, 'TIME_S_1KI': 5.47972521811056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.6203072166443, 'W': 23.467862072508055}
+[20.8, 20.8, 20.48, 20.4, 20.8, 21.2, 21.24, 21.52, 21.16, 21.16, 20.6, 20.68, 20.6, 20.56, 20.52, 20.52, 20.68, 20.64, 20.64, 20.76]
+374.09999999999997
+18.705
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1964, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.76218032836914, 'TIME_S_1KI': 5.47972521811056, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 334.6203072166443, 'W': 23.467862072508055, 'J_1KI': 170.3769385013464, 'W_1KI': 11.949013275207768, 'W_D': 4.762862072508057, 'J_D': 67.9120392394066, 'W_D_1KI': 2.425082521643613, 'J_D_1KI': 1.234767068046646}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.json
index e125259..adda367 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 394, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.532436847686768, "TIME_S_1KI": 26.732073217479105, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 305.06099658966065, "W": 21.37670924510136, "J_1KI": 774.2664888062453, "W_1KI": 54.25560722106944, "W_D": 6.54270924510136, "J_D": 93.36916079187395, "W_D_1KI": 16.60586102817604, "J_D_1KI": 42.146855401462034}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 388, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.474353551864624, "TIME_S_1KI": 26.995756576970678, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 358.88038782119753, "W": 25.07640303399043, "J_1KI": 924.9494531474163, "W_1KI": 64.62990472677946, "W_D": 6.515403033990431, "J_D": 93.24504652762414, "W_D_1KI": 16.79227586080008, "J_D_1KI": 43.27906149690742}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.output
index 49ea4fe..b142ac2 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.876847505569458}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.7017886638641357}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     270,     507,  ..., 1249492,
-                            1249752, 1250000]),
-       col_indices=tensor([  22,   47,   49,  ..., 4884, 4921, 4983]),
-       values=tensor([0.5298, 0.6030, 0.6480,  ..., 0.1911, 0.5303, 0.8187]),
+tensor(crow_indices=tensor([      0,     241,     494,  ..., 1249537,
+                            1249753, 1250000]),
+       col_indices=tensor([   2,    8,   17,  ..., 4972, 4993, 4996]),
+       values=tensor([0.4645, 0.2180, 0.1087,  ..., 0.3537, 0.6799, 0.0264]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.2222, 0.3805, 0.6044,  ..., 0.3111, 0.2543, 0.4407])
+tensor([0.7534, 0.3194, 0.4895,  ..., 0.0345, 0.1094, 0.2075])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 2.876847505569458 seconds
+Time: 2.7017886638641357 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 364 -ss 5000 -sd 0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.686374425888062}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 388 -ss 5000 -sd 0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.474353551864624}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     236,     499,  ..., 1249504,
-                            1249745, 1250000]),
-       col_indices=tensor([  18,   50,   76,  ..., 4926, 4932, 4975]),
-       values=tensor([0.1676, 0.6835, 0.4526,  ..., 0.3904, 0.9402, 0.3969]),
+tensor(crow_indices=tensor([      0,     241,     503,  ..., 1249508,
+                            1249751, 1250000]),
+       col_indices=tensor([  38,   44,  134,  ..., 4933, 4935, 4955]),
+       values=tensor([0.2322, 0.9230, 0.4915,  ..., 0.3294, 0.3139, 0.8303]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.5247, 0.4082, 0.5074,  ..., 0.5246, 0.7808, 0.6822])
+tensor([0.3153, 0.8250, 0.4991,  ..., 0.6990, 0.6960, 0.9986])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 9.686374425888062 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 394 -ss 5000 -sd 0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.532436847686768}
+Time: 10.474353551864624 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     285,     546,  ..., 1249527,
-                            1249741, 1250000]),
-       col_indices=tensor([  14,   79,   87,  ..., 4936, 4956, 4998]),
-       values=tensor([0.7519, 0.7358, 0.0306,  ..., 0.8162, 0.7664, 0.2246]),
+tensor(crow_indices=tensor([      0,     241,     503,  ..., 1249508,
+                            1249751, 1250000]),
+       col_indices=tensor([  38,   44,  134,  ..., 4933, 4935, 4955]),
+       values=tensor([0.2322, 0.9230, 0.4915,  ..., 0.3294, 0.3139, 0.8303]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.9515, 0.5215, 0.2042,  ..., 0.7715, 0.5250, 0.3133])
+tensor([0.3153, 0.8250, 0.4991,  ..., 0.6990, 0.6960, 0.9986])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,30 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.532436847686768 seconds
+Time: 10.474353551864624 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     285,     546,  ..., 1249527,
-                            1249741, 1250000]),
-       col_indices=tensor([  14,   79,   87,  ..., 4936, 4956, 4998]),
-       values=tensor([0.7519, 0.7358, 0.0306,  ..., 0.8162, 0.7664, 0.2246]),
-       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.9515, 0.5215, 0.2042,  ..., 0.7715, 0.5250, 0.3133])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 1250000
-Density: 0.05
-Time: 10.532436847686768 seconds
-
-[16.32, 16.28, 16.04, 16.2, 16.32, 16.36, 16.36, 16.36, 16.64, 16.6]
-[16.72, 16.84, 17.04, 21.76, 24.68, 30.6, 31.68, 29.24, 27.56, 20.44, 20.44, 20.32, 20.44, 20.32]
-14.270718336105347
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 394, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.532436847686768, 'TIME_S_1KI': 26.732073217479105, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 305.06099658966065, 'W': 21.37670924510136}
-[16.32, 16.28, 16.04, 16.2, 16.32, 16.36, 16.36, 16.36, 16.64, 16.6, 16.32, 16.36, 16.44, 16.6, 16.76, 16.8, 16.64, 16.76, 16.76, 16.76]
-296.68
-14.834
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 394, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.532436847686768, 'TIME_S_1KI': 26.732073217479105, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 305.06099658966065, 'W': 21.37670924510136, 'J_1KI': 774.2664888062453, 'W_1KI': 54.25560722106944, 'W_D': 6.54270924510136, 'J_D': 93.36916079187395, 'W_D_1KI': 16.60586102817604, 'J_D_1KI': 42.146855401462034}
+[20.84, 20.92, 20.8, 20.8, 20.6, 20.48, 20.48, 20.4, 20.56, 20.64]
+[20.76, 20.68, 20.96, 24.88, 26.08, 34.24, 34.88, 32.48, 32.36, 26.92, 26.92, 24.32, 24.24, 24.08]
+14.311477899551392
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 388, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.474353551864624, 'TIME_S_1KI': 26.995756576970678, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 358.88038782119753, 'W': 25.07640303399043}
+[20.84, 20.92, 20.8, 20.8, 20.6, 20.48, 20.48, 20.4, 20.56, 20.64, 20.72, 20.76, 20.6, 20.52, 20.56, 20.6, 20.64, 20.64, 20.48, 20.56]
+371.22
+18.561
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 388, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.474353551864624, 'TIME_S_1KI': 26.995756576970678, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 358.88038782119753, 'W': 25.07640303399043, 'J_1KI': 924.9494531474163, 'W_1KI': 64.62990472677946, 'W_D': 6.515403033990431, 'J_D': 93.24504652762414, 'W_D_1KI': 16.79227586080008, 'J_D_1KI': 43.27906149690742}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.json
index 5879ae9..b7a7ec4 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 197, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.453055620193481, "TIME_S_1KI": 53.06119604159128, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 328.8498130035401, "W": 21.582740338257725, "J_1KI": 1669.2883908809142, "W_1KI": 109.55705755460775, "W_D": 6.5647403382577245, "J_D": 100.02500140476232, "W_D_1KI": 33.32355501653667, "J_D_1KI": 169.15510160678514}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 198, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.476684808731079, "TIME_S_1KI": 52.912549539045855, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 398.7726536369324, "W": 26.110499041665946, "J_1KI": 2014.0033011966284, "W_1KI": 131.87120728114112, "W_D": 5.921499041665946, "J_D": 90.43610704588892, "W_D_1KI": 29.906560816494675, "J_D_1KI": 151.0432364469428}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.output
index 45da6ca..ec6b73e 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,34 +1,14 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.324424982070923}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.296512126922607}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     475,     961,  ..., 2499006,
-                            2499520, 2500000]),
-       col_indices=tensor([  15,   18,   19,  ..., 4987, 4990, 4996]),
-       values=tensor([0.8221, 0.3138, 0.3999,  ..., 0.4846, 0.5872, 0.2809]),
-       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6625, 0.6086, 0.7821,  ..., 0.8108, 0.2752, 0.8534])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 2500000
-Density: 0.1
-Time: 5.324424982070923 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 197 -ss 5000 -sd 0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.453055620193481}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     504,     990,  ..., 2498981,
+tensor(crow_indices=tensor([      0,     476,     953,  ..., 2499033,
                             2499511, 2500000]),
-       col_indices=tensor([   3,   15,   19,  ..., 4959, 4975, 4978]),
-       values=tensor([0.1275, 0.4769, 0.3626,  ..., 0.0765, 0.7881, 0.0735]),
+       col_indices=tensor([   7,   24,   50,  ..., 4990, 4995, 4999]),
+       values=tensor([0.5541, 0.2923, 0.9039,  ..., 0.3203, 0.2222, 0.1676]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.2182, 0.1034, 0.8832,  ..., 0.0679, 0.0105, 0.3546])
+tensor([0.7824, 0.4557, 0.0424,  ..., 0.4238, 0.5244, 0.3285])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.453055620193481 seconds
+Time: 5.296512126922607 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 198 -ss 5000 -sd 0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.476684808731079}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     504,     990,  ..., 2498981,
-                            2499511, 2500000]),
-       col_indices=tensor([   3,   15,   19,  ..., 4959, 4975, 4978]),
-       values=tensor([0.1275, 0.4769, 0.3626,  ..., 0.0765, 0.7881, 0.0735]),
+tensor(crow_indices=tensor([      0,     499,    1004,  ..., 2499034,
+                            2499522, 2500000]),
+       col_indices=tensor([   1,   12,   20,  ..., 4980, 4986, 4992]),
+       values=tensor([0.7404, 0.5790, 0.7957,  ..., 0.1738, 0.3236, 0.5858]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.2182, 0.1034, 0.8832,  ..., 0.0679, 0.0105, 0.3546])
+tensor([0.0161, 0.9413, 0.4866,  ..., 0.2496, 0.7035, 0.1589])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +36,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.453055620193481 seconds
+Time: 10.476684808731079 seconds
 
-[16.48, 16.56, 16.52, 16.6, 16.92, 17.2, 17.24, 17.32, 17.32, 17.2]
-[17.2, 17.08, 16.8, 20.08, 22.0, 28.44, 31.8, 32.0, 28.2, 27.28, 20.16, 20.32, 20.56, 20.56, 20.32]
-15.236703395843506
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.453055620193481, 'TIME_S_1KI': 53.06119604159128, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.8498130035401, 'W': 21.582740338257725}
-[16.48, 16.56, 16.52, 16.6, 16.92, 17.2, 17.24, 17.32, 17.32, 17.2, 16.8, 16.52, 16.44, 16.56, 16.44, 16.36, 16.4, 16.48, 16.24, 16.0]
-300.36
-15.018
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.453055620193481, 'TIME_S_1KI': 53.06119604159128, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.8498130035401, 'W': 21.582740338257725, 'J_1KI': 1669.2883908809142, 'W_1KI': 109.55705755460775, 'W_D': 6.5647403382577245, 'J_D': 100.02500140476232, 'W_D_1KI': 33.32355501653667, 'J_D_1KI': 169.15510160678514}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     499,    1004,  ..., 2499034,
+                            2499522, 2500000]),
+       col_indices=tensor([   1,   12,   20,  ..., 4980, 4986, 4992]),
+       values=tensor([0.7404, 0.5790, 0.7957,  ..., 0.1738, 0.3236, 0.5858]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0161, 0.9413, 0.4866,  ..., 0.2496, 0.7035, 0.1589])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.476684808731079 seconds
+
+[23.88, 23.88, 24.04, 24.36, 24.28, 24.28, 24.6, 24.56, 24.4, 24.48]
+[24.56, 23.8, 24.16, 24.52, 28.56, 28.56, 34.0, 34.76, 34.2, 33.8, 27.4, 24.6, 24.56, 24.6, 24.56]
+15.272502183914185
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 198, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.476684808731079, 'TIME_S_1KI': 52.912549539045855, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 398.7726536369324, 'W': 26.110499041665946}
+[23.88, 23.88, 24.04, 24.36, 24.28, 24.28, 24.6, 24.56, 24.4, 24.48, 20.52, 20.32, 20.52, 20.52, 20.52, 20.72, 20.84, 20.8, 20.56, 20.28]
+403.78
+20.189
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 198, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.476684808731079, 'TIME_S_1KI': 52.912549539045855, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 398.7726536369324, 'W': 26.110499041665946, 'J_1KI': 2014.0033011966284, 'W_1KI': 131.87120728114112, 'W_D': 5.921499041665946, 'J_D': 90.43610704588892, 'W_D_1KI': 29.906560816494675, 'J_D_1KI': 151.0432364469428}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.2.json
index 22c3547..5a4d013 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.2.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.2.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.520986080169678, "TIME_S_1KI": 105.20986080169678, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 343.0604390716553, "W": 22.467293864380782, "J_1KI": 3430.604390716553, "W_1KI": 224.67293864380784, "W_D": 7.879293864380783, "J_D": 120.3115083198548, "W_D_1KI": 78.79293864380782, "J_D_1KI": 787.9293864380782}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.666005373001099, "TIME_S_1KI": 106.66005373001099, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 426.37482541084296, "W": 26.06617543346282, "J_1KI": 4263.74825410843, "W_1KI": 260.6617543346282, "W_D": 7.4991754334628205, "J_D": 122.66700284934048, "W_D_1KI": 74.99175433462821, "J_D_1KI": 749.9175433462822}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.2.output
index 46cc854..21d400e 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.2.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.2']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.520986080169678}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.666005373001099}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1010,    1992,  ..., 4998032,
-                            4999035, 5000000]),
-       col_indices=tensor([   7,   20,   27,  ..., 4987, 4995, 4999]),
-       values=tensor([0.6859, 0.1805, 0.1498,  ..., 0.8538, 0.5250, 0.8232]),
+tensor(crow_indices=tensor([      0,    1006,    1980,  ..., 4997928,
+                            4998954, 5000000]),
+       col_indices=tensor([   2,    4,   12,  ..., 4984, 4990, 4999]),
+       values=tensor([0.2907, 0.3111, 0.1138,  ..., 0.3742, 0.6542, 0.5964]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.6117, 0.2257, 0.9695,  ..., 0.2383, 0.1591, 0.7207])
+tensor([0.0716, 0.6336, 0.1460,  ..., 0.3435, 0.3580, 0.3753])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.520986080169678 seconds
+Time: 10.666005373001099 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1010,    1992,  ..., 4998032,
-                            4999035, 5000000]),
-       col_indices=tensor([   7,   20,   27,  ..., 4987, 4995, 4999]),
-       values=tensor([0.6859, 0.1805, 0.1498,  ..., 0.8538, 0.5250, 0.8232]),
+tensor(crow_indices=tensor([      0,    1006,    1980,  ..., 4997928,
+                            4998954, 5000000]),
+       col_indices=tensor([   2,    4,   12,  ..., 4984, 4990, 4999]),
+       values=tensor([0.2907, 0.3111, 0.1138,  ..., 0.3742, 0.6542, 0.5964]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.6117, 0.2257, 0.9695,  ..., 0.2383, 0.1591, 0.7207])
+tensor([0.0716, 0.6336, 0.1460,  ..., 0.3435, 0.3580, 0.3753])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +33,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.520986080169678 seconds
+Time: 10.666005373001099 seconds
 
-[16.36, 16.04, 16.16, 15.96, 15.88, 15.88, 16.04, 16.04, 16.28, 16.32]
-[16.4, 16.48, 16.8, 20.24, 21.88, 29.72, 32.92, 30.52, 30.52, 30.68, 27.2, 20.4, 20.72, 20.84, 20.72]
-15.269326210021973
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.520986080169678, 'TIME_S_1KI': 105.20986080169678, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 343.0604390716553, 'W': 22.467293864380782}
-[16.36, 16.04, 16.16, 15.96, 15.88, 15.88, 16.04, 16.04, 16.28, 16.32, 16.84, 17.08, 16.68, 16.36, 16.32, 16.0, 16.08, 16.08, 16.04, 16.16]
-291.76
-14.588
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.520986080169678, 'TIME_S_1KI': 105.20986080169678, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 343.0604390716553, 'W': 22.467293864380782, 'J_1KI': 3430.604390716553, 'W_1KI': 224.67293864380784, 'W_D': 7.879293864380783, 'J_D': 120.3115083198548, 'W_D_1KI': 78.79293864380782, 'J_D_1KI': 787.9293864380782}
+[20.6, 20.48, 20.52, 20.36, 20.4, 20.6, 20.68, 20.72, 21.0, 20.84]
+[20.6, 20.6, 20.68, 24.08, 25.44, 32.44, 38.52, 38.64, 35.92, 34.48, 26.72, 24.36, 24.4, 24.4, 24.44, 24.44]
+16.357398748397827
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.666005373001099, 'TIME_S_1KI': 106.66005373001099, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 426.37482541084296, 'W': 26.06617543346282}
+[20.6, 20.48, 20.52, 20.36, 20.4, 20.6, 20.68, 20.72, 21.0, 20.84, 20.68, 20.68, 20.68, 21.04, 21.0, 20.64, 20.64, 20.4, 20.24, 20.4]
+371.34000000000003
+18.567
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.666005373001099, 'TIME_S_1KI': 106.66005373001099, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 426.37482541084296, 'W': 26.06617543346282, 'J_1KI': 4263.74825410843, 'W_1KI': 260.6617543346282, 'W_D': 7.4991754334628205, 'J_D': 122.66700284934048, 'W_D_1KI': 74.99175433462821, 'J_D_1KI': 749.9175433462822}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.3.json
index 1e920c1..c6bb734 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.3.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.3.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 16.019237279891968, "TIME_S_1KI": 160.19237279891968, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 445.7399830436707, "W": 20.90802632853554, "J_1KI": 4457.399830436707, "W_1KI": 209.08026328535541, "W_D": 5.958026328535542, "J_D": 127.0196676111222, "W_D_1KI": 59.58026328535542, "J_D_1KI": 595.8026328535542}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 17.13407015800476, "TIME_S_1KI": 171.3407015800476, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 562.5266760253905, "W": 25.07427127954065, "J_1KI": 5625.266760253905, "W_1KI": 250.7427127954065, "W_D": 6.58627127954065, "J_D": 147.75916113281244, "W_D_1KI": 65.86271279540651, "J_D_1KI": 658.627127954065}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.3.output
index 039e7ba..7a83544 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.3.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.3']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 16.019237279891968}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 17.13407015800476}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1526,    3031,  ..., 7497025,
-                            7498559, 7500000]),
-       col_indices=tensor([   0,    3,    7,  ..., 4995, 4996, 4999]),
-       values=tensor([0.4727, 0.1556, 0.1081,  ..., 0.9285, 0.0937, 0.3872]),
+tensor(crow_indices=tensor([      0,    1530,    3021,  ..., 7496893,
+                            7498420, 7500000]),
+       col_indices=tensor([   1,    2,    3,  ..., 4995, 4996, 4998]),
+       values=tensor([0.0061, 0.9558, 0.6537,  ..., 0.2384, 0.9968, 0.4609]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.9962, 0.8284, 0.0086,  ..., 0.9887, 0.5066, 0.4274])
+tensor([0.2735, 0.5951, 0.9100,  ..., 0.6989, 0.4928, 0.5249])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 16.019237279891968 seconds
+Time: 17.13407015800476 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1526,    3031,  ..., 7497025,
-                            7498559, 7500000]),
-       col_indices=tensor([   0,    3,    7,  ..., 4995, 4996, 4999]),
-       values=tensor([0.4727, 0.1556, 0.1081,  ..., 0.9285, 0.0937, 0.3872]),
+tensor(crow_indices=tensor([      0,    1530,    3021,  ..., 7496893,
+                            7498420, 7500000]),
+       col_indices=tensor([   1,    2,    3,  ..., 4995, 4996, 4998]),
+       values=tensor([0.0061, 0.9558, 0.6537,  ..., 0.2384, 0.9968, 0.4609]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.9962, 0.8284, 0.0086,  ..., 0.9887, 0.5066, 0.4274])
+tensor([0.2735, 0.5951, 0.9100,  ..., 0.6989, 0.4928, 0.5249])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +33,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 16.019237279891968 seconds
+Time: 17.13407015800476 seconds
 
-[16.44, 16.68, 16.72, 16.56, 16.6, 16.76, 16.84, 16.84, 16.68, 16.52]
-[16.44, 16.48, 16.8, 17.84, 18.92, 25.84, 27.76, 29.92, 29.96, 29.48, 23.32, 23.32, 20.24, 20.12, 20.24, 20.04, 20.04, 20.24, 20.28, 20.2, 20.12]
-21.319084644317627
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 16.019237279891968, 'TIME_S_1KI': 160.19237279891968, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 445.7399830436707, 'W': 20.90802632853554}
-[16.44, 16.68, 16.72, 16.56, 16.6, 16.76, 16.84, 16.84, 16.68, 16.52, 16.44, 16.44, 16.56, 16.56, 16.72, 16.64, 16.56, 16.52, 16.4, 16.44]
-299.0
-14.95
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 16.019237279891968, 'TIME_S_1KI': 160.19237279891968, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 445.7399830436707, 'W': 20.90802632853554, 'J_1KI': 4457.399830436707, 'W_1KI': 209.08026328535541, 'W_D': 5.958026328535542, 'J_D': 127.0196676111222, 'W_D_1KI': 59.58026328535542, 'J_D_1KI': 595.8026328535542}
+[20.72, 20.56, 20.8, 20.72, 20.48, 20.52, 20.52, 20.36, 20.32, 20.36]
+[20.44, 20.56, 22.4, 22.4, 23.44, 25.04, 32.64, 36.04, 34.88, 34.84, 29.96, 27.04, 24.6, 24.44, 24.4, 24.36, 24.36, 24.52, 24.4, 24.52, 24.48, 24.6]
+22.434417724609375
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 17.13407015800476, 'TIME_S_1KI': 171.3407015800476, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 562.5266760253905, 'W': 25.07427127954065}
+[20.72, 20.56, 20.8, 20.72, 20.48, 20.52, 20.52, 20.36, 20.32, 20.36, 20.4, 20.28, 20.4, 20.32, 20.52, 20.72, 20.72, 20.76, 20.68, 20.68]
+369.76
+18.488
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 17.13407015800476, 'TIME_S_1KI': 171.3407015800476, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 562.5266760253905, 'W': 25.07427127954065, 'J_1KI': 5625.266760253905, 'W_1KI': 250.7427127954065, 'W_D': 6.58627127954065, 'J_D': 147.75916113281244, 'W_D_1KI': 65.86271279540651, 'J_D_1KI': 658.627127954065}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.4.json
index d6dabaf..d742da0 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.4.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.4.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.045433044433594, "TIME_S_1KI": 210.45433044433594, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 590.3603710937499, "W": 21.581829265499348, "J_1KI": 5903.603710937499, "W_1KI": 215.81829265499348, "W_D": 6.281829265499347, "J_D": 171.83636339187612, "W_D_1KI": 62.81829265499347, "J_D_1KI": 628.1829265499347}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 22.884809732437134, "TIME_S_1KI": 228.84809732437134, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 727.7558030414582, "W": 25.608977605538268, "J_1KI": 7277.558030414582, "W_1KI": 256.0897760553827, "W_D": 6.881977605538271, "J_D": 195.57200666022317, "W_D_1KI": 68.81977605538272, "J_D_1KI": 688.1977605538272}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.4.output
index bcbbf0c..ffdb132 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.4.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.4.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.4']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.045433044433594}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 22.884809732437134}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2076,     4151,  ...,  9996002,
-                             9998013, 10000000]),
-       col_indices=tensor([   1,   14,   15,  ..., 4994, 4998, 4999]),
-       values=tensor([0.5831, 0.7619, 0.5912,  ..., 0.7349, 0.2932, 0.8119]),
+tensor(crow_indices=tensor([       0,     1964,     3970,  ...,  9996031,
+                             9998010, 10000000]),
+       col_indices=tensor([   3,    4,    5,  ..., 4988, 4991, 4995]),
+       values=tensor([0.4690, 0.4427, 0.6781,  ..., 0.0015, 0.0307, 0.1366]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.6981, 0.1886, 0.6279,  ..., 0.1836, 0.5536, 0.9370])
+tensor([0.6025, 0.9562, 0.9790,  ..., 0.5619, 0.4470, 0.1844])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 21.045433044433594 seconds
+Time: 22.884809732437134 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2076,     4151,  ...,  9996002,
-                             9998013, 10000000]),
-       col_indices=tensor([   1,   14,   15,  ..., 4994, 4998, 4999]),
-       values=tensor([0.5831, 0.7619, 0.5912,  ..., 0.7349, 0.2932, 0.8119]),
+tensor(crow_indices=tensor([       0,     1964,     3970,  ...,  9996031,
+                             9998010, 10000000]),
+       col_indices=tensor([   3,    4,    5,  ..., 4988, 4991, 4995]),
+       values=tensor([0.4690, 0.4427, 0.6781,  ..., 0.0015, 0.0307, 0.1366]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.6981, 0.1886, 0.6279,  ..., 0.1836, 0.5536, 0.9370])
+tensor([0.6025, 0.9562, 0.9790,  ..., 0.5619, 0.4470, 0.1844])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +33,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 21.045433044433594 seconds
+Time: 22.884809732437134 seconds
 
-[16.72, 16.72, 16.28, 16.36, 16.28, 17.72, 18.32, 19.12, 19.12, 19.28]
-[18.6, 17.76, 17.4, 20.72, 22.2, 26.08, 32.2, 30.12, 32.12, 31.16, 23.52, 23.52, 22.96, 20.28, 20.12, 20.24, 20.28, 20.28, 20.32, 20.32, 20.2, 20.12, 20.28, 20.28, 20.48, 20.6, 20.48]
-27.35451030731201
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.045433044433594, 'TIME_S_1KI': 210.45433044433594, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 590.3603710937499, 'W': 21.581829265499348}
-[16.72, 16.72, 16.28, 16.36, 16.28, 17.72, 18.32, 19.12, 19.12, 19.28, 16.44, 16.48, 16.36, 16.32, 16.32, 16.52, 16.72, 16.56, 16.4, 16.36]
-306.0
-15.3
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.045433044433594, 'TIME_S_1KI': 210.45433044433594, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 590.3603710937499, 'W': 21.581829265499348, 'J_1KI': 5903.603710937499, 'W_1KI': 215.81829265499348, 'W_D': 6.281829265499347, 'J_D': 171.83636339187612, 'W_D_1KI': 62.81829265499347, 'J_D_1KI': 628.1829265499347}
+[20.44, 20.68, 20.68, 20.8, 20.88, 20.8, 20.8, 20.44, 20.48, 20.48]
+[20.56, 20.8, 21.04, 24.48, 26.44, 29.32, 37.88, 39.16, 38.08, 37.2, 28.32, 27.64, 24.52, 24.52, 24.6, 24.48, 24.4, 24.44, 24.12, 23.96, 23.96, 23.92, 24.2, 24.28, 24.44, 24.44, 24.4, 24.44]
+28.41799521446228
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 22.884809732437134, 'TIME_S_1KI': 228.84809732437134, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.7558030414582, 'W': 25.608977605538268}
+[20.44, 20.68, 20.68, 20.8, 20.88, 20.8, 20.8, 20.44, 20.48, 20.48, 20.76, 20.64, 20.8, 21.0, 21.0, 21.08, 21.28, 20.96, 21.0, 20.76]
+374.53999999999996
+18.726999999999997
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 22.884809732437134, 'TIME_S_1KI': 228.84809732437134, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.7558030414582, 'W': 25.608977605538268, 'J_1KI': 7277.558030414582, 'W_1KI': 256.0897760553827, 'W_D': 6.881977605538271, 'J_D': 195.57200666022317, 'W_D_1KI': 68.81977605538272, 'J_D_1KI': 688.1977605538272}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.5.json
index edd5f52..562706e 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.5.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.5.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.53720736503601, "TIME_S_1KI": 265.3720736503601, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 691.3309565734863, "W": 20.656596648884427, "J_1KI": 6913.309565734863, "W_1KI": 206.56596648884428, "W_D": 5.692596648884429, "J_D": 190.5187167835236, "W_D_1KI": 56.92596648884429, "J_D_1KI": 569.2596648884429}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 28.252235651016235, "TIME_S_1KI": 282.52235651016235, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 899.1067306137085, "W": 25.324976515518202, "J_1KI": 8991.067306137085, "W_1KI": 253.249765155182, "W_D": 6.684976515518201, "J_D": 237.33516101837156, "W_D_1KI": 66.84976515518201, "J_D_1KI": 668.4976515518201}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.5.output
index 7bfb817..7502a06 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.5.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_0.5.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.5']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.53720736503601}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 28.252235651016235}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2531,     5099,  ..., 12494952,
-                            12497469, 12500000]),
-       col_indices=tensor([   0,    7,    9,  ..., 4997, 4998, 4999]),
-       values=tensor([0.6564, 0.0127, 0.9586,  ..., 0.9277, 0.7224, 0.6295]),
+tensor(crow_indices=tensor([       0,     2468,     4932,  ..., 12494945,
+                            12497414, 12500000]),
+       col_indices=tensor([   3,    4,    5,  ..., 4992, 4993, 4996]),
+       values=tensor([0.0730, 0.7493, 0.4780,  ..., 0.8548, 0.0678, 0.5608]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.9909, 0.2884, 0.1156,  ..., 0.4898, 0.4767, 0.4308])
+tensor([0.6126, 0.9438, 0.3718,  ..., 0.5337, 0.9270, 0.9516])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,16 +16,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 26.53720736503601 seconds
+Time: 28.252235651016235 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2531,     5099,  ..., 12494952,
-                            12497469, 12500000]),
-       col_indices=tensor([   0,    7,    9,  ..., 4997, 4998, 4999]),
-       values=tensor([0.6564, 0.0127, 0.9586,  ..., 0.9277, 0.7224, 0.6295]),
+tensor(crow_indices=tensor([       0,     2468,     4932,  ..., 12494945,
+                            12497414, 12500000]),
+       col_indices=tensor([   3,    4,    5,  ..., 4992, 4993, 4996]),
+       values=tensor([0.0730, 0.7493, 0.4780,  ..., 0.8548, 0.0678, 0.5608]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.9909, 0.2884, 0.1156,  ..., 0.4898, 0.4767, 0.4308])
+tensor([0.6126, 0.9438, 0.3718,  ..., 0.5337, 0.9270, 0.9516])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -33,13 +33,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 26.53720736503601 seconds
+Time: 28.252235651016235 seconds
 
-[16.56, 16.48, 16.36, 16.32, 16.28, 16.28, 16.52, 16.48, 16.8, 16.84]
-[16.84, 16.88, 16.84, 18.36, 18.96, 19.92, 26.16, 27.2, 30.16, 30.2, 27.92, 24.32, 23.48, 20.0, 20.0, 20.32, 20.36, 20.32, 20.48, 20.28, 20.2, 20.28, 20.2, 20.12, 20.08, 19.92, 19.92, 19.92, 20.04, 20.04, 20.2, 20.32, 20.16]
-33.4678053855896
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.53720736503601, 'TIME_S_1KI': 265.3720736503601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 691.3309565734863, 'W': 20.656596648884427}
-[16.56, 16.48, 16.36, 16.32, 16.28, 16.28, 16.52, 16.48, 16.8, 16.84, 16.6, 16.96, 16.88, 17.08, 17.04, 16.88, 16.6, 16.6, 16.52, 16.4]
-299.28
-14.963999999999999
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.53720736503601, 'TIME_S_1KI': 265.3720736503601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 691.3309565734863, 'W': 20.656596648884427, 'J_1KI': 6913.309565734863, 'W_1KI': 206.56596648884428, 'W_D': 5.692596648884429, 'J_D': 190.5187167835236, 'W_D_1KI': 56.92596648884429, 'J_D_1KI': 569.2596648884429}
+[20.8, 20.56, 20.72, 20.76, 20.48, 20.48, 20.48, 20.56, 20.4, 20.52]
+[20.64, 20.52, 20.96, 22.72, 23.8, 25.68, 34.24, 34.88, 36.92, 36.92, 36.92, 28.84, 28.0, 26.44, 24.28, 24.36, 24.36, 24.56, 24.52, 24.48, 24.52, 24.52, 24.52, 24.52, 24.44, 24.48, 24.52, 24.76, 24.64, 24.24, 24.16, 23.92, 23.84, 24.12, 24.12]
+35.502766609191895
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 28.252235651016235, 'TIME_S_1KI': 282.52235651016235, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 899.1067306137085, 'W': 25.324976515518202}
+[20.8, 20.56, 20.72, 20.76, 20.48, 20.48, 20.48, 20.56, 20.4, 20.52, 20.64, 20.64, 20.84, 21.0, 20.96, 21.0, 20.88, 20.8, 20.84, 20.84]
+372.8
+18.64
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 28.252235651016235, 'TIME_S_1KI': 282.52235651016235, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 899.1067306137085, 'W': 25.324976515518202, 'J_1KI': 8991.067306137085, 'W_1KI': 253.249765155182, 'W_D': 6.684976515518201, 'J_D': 237.33516101837156, 'W_D_1KI': 66.84976515518201, 'J_D_1KI': 668.4976515518201}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.json
index 50a61b1..f2babdd 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 277466, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.306395292282104, "TIME_S_1KI": 0.037144714279522914, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 274.4401516246796, "W": 19.314621204373413, "J_1KI": 0.9890947057465764, "W_1KI": 0.06961076746114267, "W_D": 4.503621204373411, "J_D": 63.99165032076835, "W_D_1KI": 0.016231254295565625, "J_D_1KI": 5.849817381432545e-05}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 286286, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.521464109420776, "TIME_S_1KI": 0.03675158446246332, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 308.88434463500977, "W": 21.719583434179967, "J_1KI": 1.0789362547767258, "W_1KI": 0.07586673268752216, "W_D": 2.933583434179969, "J_D": 41.71986075353625, "W_D_1KI": 0.010247037697197798, "J_D_1KI": 3.579301012692831e-05}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.output
index 19817c6..18cd113 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,156 +1,75 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.012943029403686523}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 249, 250]),
-       col_indices=tensor([1969, 3660, 2508, 3210, 4469, 2580, 1634, 4852, 2880,
-                           4171,  145, 1923, 1759,  292,  976, 2609, 4097, 2004,
-                           2150,  921,  866,  680, 1106, 3111, 2786, 1102, 3351,
-                            842, 4401, 1712,  443, 1843, 4964, 3710, 2157, 2831,
-                           4456, 4953,  180, 4915, 1729, 1065, 1121, 4037, 4202,
-                            790, 3058,  490, 2402,  821, 2529,  697, 3606, 4981,
-                           1841, 2976, 2610, 4155, 4902, 3543, 1747, 1961, 3342,
-                           3548, 4564, 2128, 4095,  930, 3054, 2352, 4784, 3523,
-                           1341, 4422, 1218, 1510, 1390,  865, 1724,  166, 3901,
-                           1272, 2149, 1488, 1813, 2931,  985,  697, 3723,  958,
-                           3733,   65, 4612, 4936, 1010, 4190,  432, 2031, 1484,
-                           3237,  225, 3100, 3677,   30, 1750, 2122, 2814, 1486,
-                           4879, 4720, 3646, 3648, 1716,  344, 4485,  818, 3342,
-                           1481, 4585, 2091, 1043, 1961,  466, 2604, 1267, 2219,
-                            379, 4743, 1233, 4480, 2900, 1400, 1058, 3165, 3216,
-                           1136, 4573, 4401,  106, 3885,  340, 2591,   62, 4321,
-                           4518, 4971,  529, 1453, 2522, 1821, 3359, 1768, 3615,
-                            560, 3497, 2950, 4004, 3758, 1175,  749, 2979,  552,
-                            596, 1500,  318, 4113, 4216,  349, 3504, 1704, 2177,
-                            110, 1213, 2730,  623,  979, 4943,    5, 3100, 4274,
-                           1196, 1212, 1292, 4837, 2749, 2657, 2151,  116, 3452,
-                           1048, 4726, 3050, 2235, 3572, 3103, 2468,  336,  256,
-                           3689, 3259, 4778, 4872, 4322, 4966,  992, 1906, 4507,
-                           3887, 3908, 4661, 4040, 2063, 4632, 2722, 1787, 3381,
-                           2148, 4178, 2261, 3851, 1401, 2459, 2100, 1771, 2949,
-                           4688, 3574, 4745, 4027,  705, 4556,  616, 3800, 1985,
-                            763, 4632, 1608, 1959, 3621,  896, 2004, 4509, 4069,
-                           2954, 1079,  553, 3501,  741,  537,  251]),
-       values=tensor([0.4598, 0.9685, 0.1022, 0.6570, 0.2626, 0.0213, 0.4956,
-                      0.5380, 0.8437, 0.5470, 0.0848, 0.9620, 0.0264, 0.2693,
-                      0.6692, 0.6401, 0.9283, 0.8706, 0.6275, 0.5943, 0.3989,
-                      0.0766, 0.9946, 0.3522, 0.5912, 0.5773, 0.4771, 0.7005,
-                      0.3650, 0.1154, 0.5630, 0.9184, 0.7700, 0.6786, 0.7164,
-                      0.8177, 0.4007, 0.8722, 0.7773, 0.2355, 0.0635, 0.3183,
-                      0.2674, 0.1684, 0.4826, 0.2298, 0.6975, 0.5790, 0.7160,
-                      0.7090, 0.3192, 0.5709, 0.6972, 0.7933, 0.5030, 0.7005,
-                      0.6711, 0.9473, 0.3130, 0.7328, 0.0053, 0.6263, 0.3400,
-                      0.8317, 0.2127, 0.6912, 0.5279, 0.3351, 0.9454, 0.0502,
-                      0.5418, 0.3942, 0.6647, 0.1585, 0.1151, 0.4637, 0.2766,
-                      0.6645, 0.2561, 0.3586, 0.9007, 0.5641, 0.9024, 0.0924,
-                      0.7442, 0.4202, 0.9456, 0.3672, 0.6816, 0.6385, 0.3813,
-                      0.9352, 0.7740, 0.6736, 0.3753, 0.4691, 0.5509, 0.9780,
-                      0.7039, 0.8897, 0.5298, 0.1267, 0.2404, 0.1878, 0.8542,
-                      0.5178, 0.8945, 0.4190, 0.8436, 0.0710, 0.7443, 0.1508,
-                      0.8032, 0.7493, 0.7469, 0.6805, 0.5330, 0.9512, 0.6693,
-                      0.2875, 0.6060, 0.0101, 0.6329, 0.2104, 0.2244, 0.8216,
-                      0.9850, 0.4320, 0.6288, 0.3139, 0.5255, 0.8128, 0.7760,
-                      0.1620, 0.7643, 0.1907, 0.2993, 0.8513, 0.3012, 0.8852,
-                      0.2619, 0.0229, 0.3957, 0.9602, 0.2258, 0.9232, 0.3917,
-                      0.9188, 0.1363, 0.2426, 0.1136, 0.6949, 0.1461, 0.1346,
-                      0.9068, 0.3140, 0.4405, 0.5547, 0.7829, 0.5939, 0.7712,
-                      0.2027, 0.1672, 0.0139, 0.3950, 0.5428, 0.8675, 0.7414,
-                      0.0223, 0.4081, 0.0576, 0.0795, 0.5466, 0.8051, 0.7481,
-                      0.9772, 0.6723, 0.3420, 0.3703, 0.9258, 0.7375, 0.0725,
-                      0.3308, 0.3117, 0.7279, 0.7861, 0.0210, 0.0730, 0.7490,
-                      0.2970, 0.3351, 0.7560, 0.6932, 0.1496, 0.7733, 0.0425,
-                      0.6190, 0.6584, 0.3857, 0.2533, 0.8724, 0.8049, 0.1764,
-                      0.5947, 0.7425, 0.4336, 0.4669, 0.5320, 0.4576, 0.7077,
-                      0.9652, 0.4590, 0.0046, 0.8577, 0.7045, 0.2574, 0.6998,
-                      0.6151, 0.3438, 0.0111, 0.9822, 0.5390, 0.2832, 0.0368,
-                      0.2525, 0.0950, 0.9720, 0.8354, 0.5121, 0.4167, 0.7493,
-                      0.0233, 0.0628, 0.4681, 0.4028, 0.7881, 0.6370, 0.0526,
-                      0.9211, 0.1381, 0.0723, 0.8459, 0.6295, 0.9210, 0.7735,
-                      0.6887, 0.7409, 0.5606, 0.9654, 0.1055]),
-       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.8735, 0.3833, 0.2694,  ..., 0.9142, 0.8243, 0.1368])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 250
-Density: 1e-05
-Time: 0.012943029403686523 seconds
-
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 81124 -ss 5000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.069929599761963}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.011764049530029297}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 250, 250]),
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+                      0.1198, 0.9417, 0.4657, 0.9378, 0.9744]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.3638, 0.9432, 0.7166,  ..., 0.3961, 0.0448, 0.0792])
+tensor([0.1955, 0.8676, 0.0943,  ..., 0.3973, 0.2391, 0.7802])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -158,80 +77,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 3.069929599761963 seconds
+Time: 0.011764049530029297 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 277466 -ss 5000 -sd 1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.306395292282104}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 89254 -ss 5000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.2735228538513184}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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-       values=tensor([0.1255, 0.3198, 0.8133, 0.3742, 0.0163, 0.1439, 0.7607,
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+       col_indices=tensor([1899, 4347, 2560, 2708, 1203, 2011,  406, 1548,  309,
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+                           2923, 3263,   95, 4398, 2876, 2278, 3914,  750, 1293,
+                           4553, 4210, 1659,  934, 1080, 2186, 4584]),
+       values=tensor([0.1865, 0.1374, 0.0978, 0.1493, 0.3922, 0.7655, 0.5676,
+                      0.5576, 0.5879, 0.7309, 0.4380, 0.9592, 0.7921, 0.6054,
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+                      0.7011, 0.2023, 0.3746, 0.7831, 0.6587, 0.0149, 0.1065,
+                      0.0522, 0.1271, 0.4297, 0.6888, 0.1782, 0.8592, 0.8326,
+                      0.5458, 0.8215, 0.0908, 0.0732, 0.7733, 0.7251, 0.5513,
+                      0.8372, 0.9332, 0.4444, 0.9377, 0.7578, 0.9000, 0.3339,
+                      0.2421, 0.8321, 0.9477, 0.2579, 0.9973, 0.5690, 0.0837,
+                      0.6222, 0.9107, 0.3005, 0.7960, 0.3291, 0.4164, 0.1900,
+                      0.5758, 0.1753, 0.7747, 0.3270, 0.8689, 0.5147, 0.6637,
+                      0.1394, 0.1087, 0.2048, 0.2814, 0.5592, 0.9814, 0.4859,
+                      0.8549, 0.7406, 0.4951, 0.6767, 0.0162]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.1431, 0.4969, 0.0611,  ..., 0.8896, 0.3924, 0.7446])
+tensor([0.0316, 0.3219, 0.8965,  ..., 0.7553, 0.3032, 0.8741])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -239,77 +158,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.306395292282104 seconds
+Time: 3.2735228538513184 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 286286 -ss 5000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.521464109420776}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([2530, 1671, 1710,  529, 4147, 2671,  774, 3521, 3142,
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-                           2685,  115, 2166, 1456, 3440, 4502, 1541,  136, 4160,
-                           2313, 2928, 4917, 3863, 3827, 2109, 4794]),
-       values=tensor([0.1255, 0.3198, 0.8133, 0.3742, 0.0163, 0.1439, 0.7607,
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-                      0.0545, 0.7637, 0.1155, 0.4005, 0.7155]),
+       col_indices=tensor([ 480, 3870, 2034,  369, 3216, 3129, 1794, 4895, 3584,
+                           4220, 4163, 3480, 3145,  107, 1218, 2520, 4570, 3112,
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+                             15, 3896, 1872, 4780, 2123, 1983, 2477, 1909, 4840,
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+                             30, 2557, 2851, 3992, 1896,  798, 1733, 1864,  481,
+                           3442,  898,  456, 2713, 3510, 2216, 2406, 3808, 3537,
+                           1111, 1588, 4505, 4130, 1525, 3898,  222,  830, 3914,
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+                           2243, 4219, 4840, 1072, 2818,  258, 3175, 1718, 4668,
+                             41, 3058, 4453, 4554, 2991, 3590, 1823, 2320,  143,
+                           4314, 2194,  450, 3196, 2031,  143, 3675, 2233, 2449,
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+                           2110, 1916,  271, 2723, 3305, 2128, 3485, 1246,  974,
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+                           3005,  264, 3133, 3255,  587, 1077,  201, 2112, 2997,
+                           2326,  983, 3504, 1863, 4060, 2823, 3127]),
+       values=tensor([0.3121, 0.0043, 0.7946, 0.0923, 0.3670, 0.9349, 0.1161,
+                      0.1608, 0.7023, 0.7598, 0.7741, 0.6627, 0.1201, 0.6615,
+                      0.3268, 0.8452, 0.0925, 0.7134, 0.5197, 0.6435, 0.6396,
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+                      0.8264, 0.1624, 0.9753, 0.8596, 0.9817, 0.9575, 0.1043,
+                      0.7839, 0.5785, 0.1642, 0.0789, 0.7062, 0.3125, 0.9158,
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+                      0.3554, 0.7084, 0.8560, 0.8663, 0.9369, 0.2539, 0.5770,
+                      0.1520, 0.6019, 0.5215, 0.7355, 0.2025, 0.8182, 0.3900,
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+                      0.4492, 0.7678, 0.2303, 0.0424, 0.4082, 0.7925, 0.9663,
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+                      0.6038, 0.0276, 0.2246, 0.2941, 0.2287, 0.2264, 0.9905,
+                      0.8927, 0.5476, 0.4812, 0.3524, 0.2207, 0.0681, 0.0174,
+                      0.1924, 0.6012, 0.6840, 0.5947, 0.7402, 0.1348, 0.2467,
+                      0.1018, 0.8994, 0.3732, 0.1658, 0.0191, 0.3792, 0.7095,
+                      0.8089, 0.8762, 0.7268, 0.0611, 0.7059, 0.0864, 0.6562,
+                      0.5520, 0.1621, 0.4042, 0.5695, 0.7688, 0.5407, 0.0872,
+                      0.4184, 0.1074, 0.3488, 0.2190, 0.1130, 0.0411, 0.0654,
+                      0.2423, 0.7003, 0.2734, 0.9698, 0.7339, 0.1093, 0.1258,
+                      0.8524, 0.6958, 0.4360, 0.8331, 0.2091, 0.0345, 0.6824,
+                      0.1548, 0.5799, 0.3430, 0.2655, 0.7925, 0.1523, 0.5878,
+                      0.1459, 0.9012, 0.5523, 0.4627, 0.1559, 0.8525, 0.1919,
+                      0.8298, 0.9748, 0.1770, 0.5065, 0.6725, 0.3035, 0.7169,
+                      0.9011, 0.5673, 0.1765, 0.2238, 0.7180, 0.7244, 0.3321,
+                      0.8196, 0.8541, 0.0808, 0.5003, 0.5951, 0.6927, 0.7943,
+                      0.3562, 0.3748, 0.2034, 0.8188, 0.1786, 0.5449, 0.7555,
+                      0.4598, 0.6698, 0.8264, 0.3421, 0.6903, 0.7656, 0.1821,
+                      0.0159, 0.4418, 0.7846, 0.9920, 0.4231, 0.7305, 0.3985,
+                      0.4712, 0.9639, 0.9006, 0.5264, 0.9895, 0.6470, 0.2125,
+                      0.0665, 0.6922, 0.8492, 0.6881, 0.5412, 0.7532, 0.7415,
+                      0.6027, 0.0028, 0.9098, 0.8735, 0.0774, 0.9563, 0.9648,
+                      0.6625, 0.9251, 0.5024, 0.5896, 0.0673, 0.5722, 0.9050,
+                      0.9294, 0.0690, 0.0890, 0.6600, 0.8796, 0.8624, 0.1592,
+                      0.4507, 0.7277, 0.8208, 0.7799, 0.5597]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.1431, 0.4969, 0.0611,  ..., 0.8896, 0.3924, 0.7446])
+tensor([0.9952, 0.6015, 0.3584,  ..., 0.2924, 0.6333, 0.1169])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -317,13 +239,91 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.306395292282104 seconds
+Time: 10.521464109420776 seconds
 
-[16.56, 16.4, 16.72, 16.72, 16.84, 16.92, 17.04, 16.64, 16.64, 16.64]
-[16.68, 16.8, 19.52, 20.28, 22.4, 23.28, 23.28, 24.04, 21.56, 21.2, 20.08, 19.92, 19.76, 19.72]
-14.208932638168335
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 277466, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.306395292282104, 'TIME_S_1KI': 0.037144714279522914, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 274.4401516246796, 'W': 19.314621204373413}
-[16.56, 16.4, 16.72, 16.72, 16.84, 16.92, 17.04, 16.64, 16.64, 16.64, 16.24, 16.2, 16.2, 16.48, 16.12, 16.2, 16.2, 16.16, 16.0, 16.04]
-296.22
-14.811000000000002
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 277466, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.306395292282104, 'TIME_S_1KI': 0.037144714279522914, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 274.4401516246796, 'W': 19.314621204373413, 'J_1KI': 0.9890947057465764, 'W_1KI': 0.06961076746114267, 'W_D': 4.503621204373411, 'J_D': 63.99165032076835, 'W_D_1KI': 0.016231254295565625, 'J_D_1KI': 5.849817381432545e-05}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([ 480, 3870, 2034,  369, 3216, 3129, 1794, 4895, 3584,
+                           4220, 4163, 3480, 3145,  107, 1218, 2520, 4570, 3112,
+                           3208, 4572, 2316, 1989, 1370, 1245, 4779, 3089, 3540,
+                           2678, 4569, 4755,  859,  258, 2063, 4366, 1785,  749,
+                           3484, 1142, 3077, 1876, 2779, 3741, 1780, 4209,  903,
+                           4145, 4869, 3543,  555,  287, 4710, 1427, 2682, 4436,
+                           3617, 1035, 1414, 2588, 2709, 1384, 3260, 3179,  536,
+                             15, 3896, 1872, 4780, 2123, 1983, 2477, 1909, 4840,
+                           3095, 1054, 2351, 3706,  954, 3928,  480, 2991, 4476,
+                           2535,  477, 1274, 3243, 3326, 2861, 2962,  453, 3260,
+                           1392, 1651,  960, 3543, 3504, 2718,  253, 2211, 3840,
+                           2330,  152, 3581, 2501, 2846,  790, 3520,  918,  553,
+                           1271, 3381, 4105, 2861,  826, 2247, 3092, 3375, 1552,
+                           1605, 4712, 2614, 4915, 3555,  602, 4560, 1263, 4728,
+                           1252, 2651,   67, 2584, 2491, 2155, 4397, 4541, 1621,
+                           2025, 2217, 1801, 4944, 3649, 3685, 4261, 3348, 3593,
+                             30, 2557, 2851, 3992, 1896,  798, 1733, 1864,  481,
+                           3442,  898,  456, 2713, 3510, 2216, 2406, 3808, 3537,
+                           1111, 1588, 4505, 4130, 1525, 3898,  222,  830, 3914,
+                           3206, 1631, 1797, 3092, 1007, 1768, 4349, 2031, 1813,
+                           2243, 4219, 4840, 1072, 2818,  258, 3175, 1718, 4668,
+                             41, 3058, 4453, 4554, 2991, 3590, 1823, 2320,  143,
+                           4314, 2194,  450, 3196, 2031,  143, 3675, 2233, 2449,
+                           1113, 4197, 1626, 4436, 4210,  122, 4276, 2632, 3704,
+                           2110, 1916,  271, 2723, 3305, 2128, 3485, 1246,  974,
+                           3858, 4337,  707, 2983, 3640, 2751,  882, 4437, 3960,
+                           3005,  264, 3133, 3255,  587, 1077,  201, 2112, 2997,
+                           2326,  983, 3504, 1863, 4060, 2823, 3127]),
+       values=tensor([0.3121, 0.0043, 0.7946, 0.0923, 0.3670, 0.9349, 0.1161,
+                      0.1608, 0.7023, 0.7598, 0.7741, 0.6627, 0.1201, 0.6615,
+                      0.3268, 0.8452, 0.0925, 0.7134, 0.5197, 0.6435, 0.6396,
+                      0.6088, 0.3969, 0.4192, 0.2501, 0.6894, 0.2897, 0.9520,
+                      0.8264, 0.1624, 0.9753, 0.8596, 0.9817, 0.9575, 0.1043,
+                      0.7839, 0.5785, 0.1642, 0.0789, 0.7062, 0.3125, 0.9158,
+                      0.8766, 0.3530, 0.3168, 0.3900, 0.3047, 0.1421, 0.5430,
+                      0.3554, 0.7084, 0.8560, 0.8663, 0.9369, 0.2539, 0.5770,
+                      0.1520, 0.6019, 0.5215, 0.7355, 0.2025, 0.8182, 0.3900,
+                      0.0267, 0.1237, 0.8960, 0.4175, 0.2950, 0.9583, 0.1027,
+                      0.4492, 0.7678, 0.2303, 0.0424, 0.4082, 0.7925, 0.9663,
+                      0.0787, 0.8553, 0.9050, 0.4507, 0.5493, 0.5714, 0.6098,
+                      0.7273, 0.3019, 0.9842, 0.8281, 0.5926, 0.4952, 0.2582,
+                      0.6038, 0.0276, 0.2246, 0.2941, 0.2287, 0.2264, 0.9905,
+                      0.8927, 0.5476, 0.4812, 0.3524, 0.2207, 0.0681, 0.0174,
+                      0.1924, 0.6012, 0.6840, 0.5947, 0.7402, 0.1348, 0.2467,
+                      0.1018, 0.8994, 0.3732, 0.1658, 0.0191, 0.3792, 0.7095,
+                      0.8089, 0.8762, 0.7268, 0.0611, 0.7059, 0.0864, 0.6562,
+                      0.5520, 0.1621, 0.4042, 0.5695, 0.7688, 0.5407, 0.0872,
+                      0.4184, 0.1074, 0.3488, 0.2190, 0.1130, 0.0411, 0.0654,
+                      0.2423, 0.7003, 0.2734, 0.9698, 0.7339, 0.1093, 0.1258,
+                      0.8524, 0.6958, 0.4360, 0.8331, 0.2091, 0.0345, 0.6824,
+                      0.1548, 0.5799, 0.3430, 0.2655, 0.7925, 0.1523, 0.5878,
+                      0.1459, 0.9012, 0.5523, 0.4627, 0.1559, 0.8525, 0.1919,
+                      0.8298, 0.9748, 0.1770, 0.5065, 0.6725, 0.3035, 0.7169,
+                      0.9011, 0.5673, 0.1765, 0.2238, 0.7180, 0.7244, 0.3321,
+                      0.8196, 0.8541, 0.0808, 0.5003, 0.5951, 0.6927, 0.7943,
+                      0.3562, 0.3748, 0.2034, 0.8188, 0.1786, 0.5449, 0.7555,
+                      0.4598, 0.6698, 0.8264, 0.3421, 0.6903, 0.7656, 0.1821,
+                      0.0159, 0.4418, 0.7846, 0.9920, 0.4231, 0.7305, 0.3985,
+                      0.4712, 0.9639, 0.9006, 0.5264, 0.9895, 0.6470, 0.2125,
+                      0.0665, 0.6922, 0.8492, 0.6881, 0.5412, 0.7532, 0.7415,
+                      0.6027, 0.0028, 0.9098, 0.8735, 0.0774, 0.9563, 0.9648,
+                      0.6625, 0.9251, 0.5024, 0.5896, 0.0673, 0.5722, 0.9050,
+                      0.9294, 0.0690, 0.0890, 0.6600, 0.8796, 0.8624, 0.1592,
+                      0.4507, 0.7277, 0.8208, 0.7799, 0.5597]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.9952, 0.6015, 0.3584,  ..., 0.2924, 0.6333, 0.1169])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.521464109420776 seconds
+
+[21.0, 20.8, 20.8, 20.8, 21.08, 21.0, 21.24, 21.2, 21.24, 21.16]
+[21.04, 20.8, 21.56, 22.56, 23.36, 23.36, 24.24, 25.0, 24.44, 24.12, 23.96, 24.04, 23.84, 23.68]
+14.22146725654602
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 286286, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.521464109420776, 'TIME_S_1KI': 0.03675158446246332, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 308.88434463500977, 'W': 21.719583434179967}
+[21.0, 20.8, 20.8, 20.8, 21.08, 21.0, 21.24, 21.2, 21.24, 21.16, 21.04, 20.96, 20.72, 20.72, 20.68, 20.68, 20.48, 20.6, 20.8, 20.64]
+375.71999999999997
+18.785999999999998
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 286286, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.521464109420776, 'TIME_S_1KI': 0.03675158446246332, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 308.88434463500977, 'W': 21.719583434179967, 'J_1KI': 1.0789362547767258, 'W_1KI': 0.07586673268752216, 'W_D': 2.933583434179969, 'J_D': 41.71986075353625, 'W_D_1KI': 0.010247037697197798, 'J_D_1KI': 3.579301012692831e-05}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_5e-05.json
index ed92a63..a46871e 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_5e-05.json
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Altra", "CORES": 80, "ITERATIONS": 147819, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.450809717178345, "TIME_S_1KI": 0.07070004341240534, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 270.7087575721741, "W": 19.037186911173507, "J_1KI": 1.831352921966554, "W_1KI": 0.12878714448868891, "W_D": 4.033186911173505, "J_D": 57.35190933799741, "W_D_1KI": 0.027284631279967428, "J_D_1KI": 0.00018458135476472868}
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 149833, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.25239634513855, "TIME_S_1KI": 0.06842548934572858, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 312.4897583007812, "W": 21.925255553224016, "J_1KI": 2.085587008875089, "W_1KI": 0.14633128585307653, "W_D": 3.370255553224016, "J_D": 48.03457550048826, "W_D_1KI": 0.022493413021323846, "J_D_1KI": 0.0001501232239982103}
diff --git a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_5e-05.output
index 3732e5f..fb0df82 100644
--- a/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_5e-05.output
+++ b/pytorch/output_synthetic_maxcore/altra_max_csr_10_10_10_synthetic_5000_5e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.015123844146728516}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.014627218246459961}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([3446, 3211, 1459,  ..., 3404, 1400, 4328]),
-       values=tensor([0.9299, 0.4025, 0.7514,  ..., 0.4501, 0.7034, 0.4301]),
+       col_indices=tensor([2526, 1521, 4610,  ..., 1277, 4482,  183]),
+       values=tensor([0.9942, 0.8413, 0.7861,  ..., 0.9789, 0.3987, 0.5714]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.5652, 0.8868, 0.6802,  ..., 0.3723, 0.2839, 0.7363])
+tensor([0.9661, 0.6980, 0.2318,  ..., 0.3746, 0.4676, 0.9632])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 0.015123844146728516 seconds
+Time: 0.014627218246459961 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 69426 -ss 5000 -sd 5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.931497573852539}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 71783 -ss 5000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 5.030396461486816}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([2025, 3924,  898,  ..., 1281, 3893, 4108]),
-       values=tensor([0.0304, 0.7639, 0.3864,  ..., 0.2285, 0.7727, 0.3264]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([2921, 2514, 1087,  ..., 1030, 1452, 2007]),
+       values=tensor([0.2212, 0.1400, 0.6188,  ..., 0.0721, 0.9961, 0.8481]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.1740, 0.1105, 0.4664,  ..., 0.6286, 0.2183, 0.7582])
+tensor([0.5066, 0.6469, 0.5834,  ..., 0.4760, 0.1348, 0.4338])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 4.931497573852539 seconds
+Time: 5.030396461486816 seconds
 
-['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 147819 -ss 5000 -sd 5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.450809717178345}
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 149833 -ss 5000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.25239634513855}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    2,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([4384, 3481, 3445,  ..., 2544, 3259, 4103]),
-       values=tensor([0.0351, 0.6486, 0.3996,  ..., 0.8336, 0.2691, 0.7388]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([1919,  663,  244,  ..., 2667, 4054,  269]),
+       values=tensor([0.4507, 0.6975, 0.8378,  ..., 0.5863, 0.5474, 0.5550]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.9301, 0.0450, 0.3581,  ..., 0.7152, 0.3980, 0.2103])
+tensor([0.2739, 0.2250, 0.3283,  ..., 0.6236, 0.6925, 0.9095])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.450809717178345 seconds
+Time: 10.25239634513855 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    2,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([4384, 3481, 3445,  ..., 2544, 3259, 4103]),
-       values=tensor([0.0351, 0.6486, 0.3996,  ..., 0.8336, 0.2691, 0.7388]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([1919,  663,  244,  ..., 2667, 4054,  269]),
+       values=tensor([0.4507, 0.6975, 0.8378,  ..., 0.5863, 0.5474, 0.5550]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.9301, 0.0450, 0.3581,  ..., 0.7152, 0.3980, 0.2103])
+tensor([0.2739, 0.2250, 0.3283,  ..., 0.6236, 0.6925, 0.9095])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.450809717178345 seconds
+Time: 10.25239634513855 seconds
 
-[16.36, 16.36, 16.6, 16.8, 16.76, 16.88, 16.88, 16.64, 16.44, 16.48]
-[16.72, 16.76, 17.08, 20.92, 22.6, 23.2, 24.36, 21.56, 20.88, 20.88, 19.84, 19.92, 19.92, 20.04]
-14.219997882843018
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 147819, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.450809717178345, 'TIME_S_1KI': 0.07070004341240534, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 270.7087575721741, 'W': 19.037186911173507}
-[16.36, 16.36, 16.6, 16.8, 16.76, 16.88, 16.88, 16.64, 16.44, 16.48, 16.52, 16.72, 16.68, 16.76, 16.76, 16.76, 16.6, 16.68, 16.72, 16.72]
-300.08000000000004
-15.004000000000001
-{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 147819, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.450809717178345, 'TIME_S_1KI': 0.07070004341240534, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 270.7087575721741, 'W': 19.037186911173507, 'J_1KI': 1.831352921966554, 'W_1KI': 0.12878714448868891, 'W_D': 4.033186911173505, 'J_D': 57.35190933799741, 'W_D_1KI': 0.027284631279967428, 'J_D_1KI': 0.00018458135476472868}
+[20.48, 20.56, 20.64, 20.68, 20.56, 20.48, 20.32, 20.36, 20.32, 20.36]
+[20.48, 20.44, 21.72, 21.72, 22.84, 25.12, 25.88, 26.28, 25.44, 23.88, 23.52, 23.64, 23.8, 23.88]
+14.25250244140625
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..0697d74
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.0001.json
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..f122fb4
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.0001.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..b68fd96
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.001.json
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..913c21e
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+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.001.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..7551d81
--- /dev/null
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.01.output
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index 0000000..dfa1bc5
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+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_0.01.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..b03916f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_1e-05.json
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..4bfc826
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_1e-05.output
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+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '1e-05']
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..7140651
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_5e-05.json
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..8892bec
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_100000_5e-05.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..6ce7055
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.0001.json
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..268d6a0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.0001.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..d31d411
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.001.json
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..dea63e3
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.001.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..aee0012
--- /dev/null
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..69c322c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.01.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..c23ad92
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.05.json
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..f741113
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.05.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..5e7eb24
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.1.json
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..642d388
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.1.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..04a9ce8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.2.json
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..5c22281
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.2.output
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+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.2']
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..875cf7f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..55f6737
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.3.output
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+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 85.27317547798157}
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+                       [8240, 4030, 1475,  ..., 1704,  525, 7807]]),
+       values=tensor([0.6772, 0.6271, 0.5111,  ..., 0.8320, 0.2589, 0.8403]),
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+tensor([0.6345, 0.0086, 0.5938,  ..., 0.7719, 0.5005, 0.6066])
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+tensor(indices=tensor([[7517,  643, 4736,  ..., 4882, 5111, 8362],
+                       [8240, 4030, 1475,  ..., 1704,  525, 7807]]),
+       values=tensor([0.6772, 0.6271, 0.5111,  ..., 0.8320, 0.2589, 0.8403]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_coo)
+tensor([0.6345, 0.0086, 0.5938,  ..., 0.7719, 0.5005, 0.6066])
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+Shape: torch.Size([10000, 10000])
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..e24742f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 113.69686484336853, "TIME_S_1KI": 1136.9686484336853, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 8124.020613417625, "W": 65.66, "J_1KI": 81240.20613417625, "W_1KI": 656.5999999999999, "W_D": 29.878, "J_D": 3696.7634463553427, "W_D_1KI": 298.78, "J_D_1KI": 2987.7999999999997}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..cc667e8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.4.output
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+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 113.69686484336853}
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+       values=tensor([0.1173, 0.7739, 0.2172,  ..., 0.4492, 0.5296, 0.1803]),
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+tensor([0.6399, 0.8842, 0.3630,  ..., 0.0726, 0.5579, 0.6134])
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
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+Density: 0.4
+Time: 113.69686484336853 seconds
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+tensor(indices=tensor([[1855, 2019, 4074,  ..., 5861, 3349, 3567],
+                       [3365, 8705, 2715,  ...,  963, 6883, 6514]]),
+       values=tensor([0.1173, 0.7739, 0.2172,  ..., 0.4492, 0.5296, 0.1803]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_coo)
+tensor([0.6399, 0.8842, 0.3630,  ..., 0.0726, 0.5579, 0.6134])
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+Shape: torch.Size([10000, 10000])
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+Time: 113.69686484336853 seconds
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..9cf7c26
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.5.json
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+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 141.6028139591217, "TIME_S_1KI": 1416.028139591217, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 10349.819285047055, "W": 67.45, "J_1KI": 103498.19285047054, "W_1KI": 674.5, "W_D": 25.81225, "J_D": 3960.7431110519765, "W_D_1KI": 258.1225, "J_D_1KI": 2581.225}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..175a5e1
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_0.5.output
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+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.5']
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+                       [8299, 6800, 7151,  ..., 8242,  633, 3199]]),
+       values=tensor([0.5383, 0.1260, 0.9269,  ..., 0.1173, 0.5989, 0.2331]),
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+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 141.6028139591217 seconds
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+tensor(indices=tensor([[8121, 5450, 2511,  ..., 8644, 7632,  779],
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..b91d8a3
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 312340, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.412656784057617, "TIME_S_1KI": 0.033337570545103466, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 845.5558525276183, "W": 65.24, "J_1KI": 2.707164796464168, "W_1KI": 0.2088749439713133, "W_D": 29.389250000000004, "J_D": 380.90515540921695, "W_D_1KI": 0.09409377601331884, "J_D_1KI": 0.0003012543254572544}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..2e43e1e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,748 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '1e-05']
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..b05f320
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_5e-05.json
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..c7d537a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_10000_5e-05.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..04a5360
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_0.0001.json
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..858c65d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_0.0001.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..0181305
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_1e-05.json
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..b218011
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_1e-05.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..a0f2bb9
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_5e-05.json
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..fa2c60b
--- /dev/null
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..86e66b2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.0001.json
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..1e1071a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.0001.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..a959e5a
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..06367fa
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.001.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..3150087
--- /dev/null
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..b1dfd05
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+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.01.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.05.json
new file mode 100644
index 0000000..2422969
--- /dev/null
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_0.05.output
new file mode 100644
index 0000000..249675e
--- /dev/null
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..8124190
--- /dev/null
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..3b61f12
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..aad8fe3
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..d28b141
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+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_50000_5e-05.output
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..4bda082
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.0001.json
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..65a6260
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.0001.output
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new file mode 100644
index 0000000..fb07638
--- /dev/null
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..9952092
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new file mode 100644
index 0000000..47fdc5c
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..169ce51
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..9cb92be
--- /dev/null
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..b15a1ba
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..4b4104f
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..1a861e5
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new file mode 100644
index 0000000..9d14780
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.3.output
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index 0000000..e08568e
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..d9eb99d
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.4.output
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index 0000000..57dd3f2
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..dec2fd9
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..0976adc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_0.5.output
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+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 35.183215856552124}
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+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_coo)
+tensor([0.9021, 0.8660, 0.6641,  ..., 0.3317, 0.9796, 0.4533])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 35.183215856552124 seconds
+
+tensor(indices=tensor([[ 751, 4408, 3127,  ..., 4152, 3834, 3001],
+                       [ 478, 2081,  567,  ..., 3148, 1935, 2978]]),
+       values=tensor([0.8140, 0.5278, 0.2593,  ..., 0.8892, 0.4390, 0.9114]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_coo)
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+Rows: 5000
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+Density: 0.5
+Time: 35.183215856552124 seconds
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diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..a4695e6
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 914292, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.542762517929077, "TIME_S_1KI": 0.011531067227897736, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 848.832134795189, "W": 65.62, "J_1KI": 0.9284037646563559, "W_1KI": 0.0717713815717517, "W_D": 30.134749999999997, "J_D": 389.8101824751496, "W_D_1KI": 0.03295965621486352, "J_D_1KI": 3.604937614554598e-05}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..8d321bc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,429 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.009185791015625}
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+Time: 10.542762517929077 seconds
+
+[40.18, 39.23, 39.08, 39.62, 39.49, 39.4, 38.94, 39.42, 39.88, 38.9]
+[65.62]
+12.935570478439331
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 914292, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.542762517929077, 'TIME_S_1KI': 0.011531067227897736, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 848.832134795189, 'W': 65.62}
+[40.18, 39.23, 39.08, 39.62, 39.49, 39.4, 38.94, 39.42, 39.88, 38.9, 39.55, 39.39, 39.13, 38.98, 39.31, 40.14, 39.28, 39.41, 40.05, 39.28]
+709.7050000000002
+35.48525000000001
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 914292, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.542762517929077, 'TIME_S_1KI': 0.011531067227897736, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 848.832134795189, 'W': 65.62, 'J_1KI': 0.9284037646563559, 'W_1KI': 0.0717713815717517, 'W_D': 30.134749999999997, 'J_D': 389.8101824751496, 'W_D_1KI': 0.03295965621486352, 'J_D_1KI': 3.604937614554598e-05}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..5feedd0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 264773, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 12.908402919769287, "TIME_S_1KI": 0.04875271617487163, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 856.4205559635163, "W": 66.23, "J_1KI": 3.2345464075397277, "W_1KI": 0.25013879814029377, "W_D": 30.250000000000007, "J_D": 391.1629445552827, "W_D_1KI": 0.11424880935744962, "J_D_1KI": 0.00043149720461470623}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..5c2b9aa
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_coo_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,73 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.01278996467590332}
+
+tensor(indices=tensor([[1910, 2449,  350,  ..., 2036,  729, 2451],
+                       [ 742, 1036, 4684,  ...,  413, 1944, 2520]]),
+       values=tensor([0.8034, 0.1517, 0.3873,  ..., 0.4764, 0.2065, 0.1020]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.5684, 0.7732, 0.5466,  ..., 0.5826, 0.0795, 0.1639])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.01278996467590332 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '82095', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.2556071281433105}
+
+tensor(indices=tensor([[4274, 1817, 2675,  ..., 3996, 3418,  416],
+                       [2374,  458,  595,  ..., 1284,  853, 4526]]),
+       values=tensor([0.9593, 0.9999, 0.0625,  ..., 0.9089, 0.9906, 0.4203]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.5480, 0.4054, 0.8753,  ..., 0.8136, 0.5370, 0.8569])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 3.2556071281433105 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'coo', '264773', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 12.908402919769287}
+
+tensor(indices=tensor([[  28,  111,  112,  ..., 3928, 3959, 1587],
+                       [3034,  237, 3606,  ..., 4239,  259, 4218]]),
+       values=tensor([0.9704, 0.9398, 0.0070,  ..., 0.3428, 0.4681, 0.3499]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.6124, 0.9572, 0.4064,  ..., 0.0972, 0.7122, 0.2442])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 12.908402919769287 seconds
+
+tensor(indices=tensor([[  28,  111,  112,  ..., 3928, 3959, 1587],
+                       [3034,  237, 3606,  ..., 4239,  259, 4218]]),
+       values=tensor([0.9704, 0.9398, 0.0070,  ..., 0.3428, 0.4681, 0.3499]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.6124, 0.9572, 0.4064,  ..., 0.0972, 0.7122, 0.2442])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 12.908402919769287 seconds
+
+[39.99, 39.38, 39.0, 39.02, 38.95, 38.99, 44.47, 39.26, 39.49, 38.95]
+[66.23]
+12.93100643157959
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 264773, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 12.908402919769287, 'TIME_S_1KI': 0.04875271617487163, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.4205559635163, 'W': 66.23}
+[39.99, 39.38, 39.0, 39.02, 38.95, 38.99, 44.47, 39.26, 39.49, 38.95, 40.75, 39.61, 39.44, 44.89, 39.56, 40.82, 39.01, 38.87, 39.51, 38.97]
+719.5999999999999
+35.98
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 264773, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 12.908402919769287, 'TIME_S_1KI': 0.04875271617487163, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 856.4205559635163, 'W': 66.23, 'J_1KI': 3.2345464075397277, 'W_1KI': 0.25013879814029377, 'W_D': 30.250000000000007, 'J_D': 391.1629445552827, 'W_D_1KI': 0.11424880935744962, 'J_D_1KI': 0.00043149720461470623}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json
index 4ca7cf3..4ad22b3 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 67064, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.610118389129639, "TIME_S_1KI": 0.15820885108448107, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1755.3175721168518, "W": 134.25, "J_1KI": 26.173767924920252, "W_1KI": 2.0018191578193965, "W_D": 98.104, "J_D": 1282.7089392547607, "W_D_1KI": 1.4628414648693786, "J_D_1KI": 0.021812618765200086}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 70295, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.822261571884155, "TIME_S_1KI": 0.15395492669299604, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2022.7645127677915, "W": 148.89, "J_1KI": 28.775368273245487, "W_1KI": 2.1180738317092254, "W_D": 112.92999999999999, "J_D": 1534.2252429771422, "W_D_1KI": 1.6065153993882921, "J_D_1KI": 0.022853907097066535}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output
index 6ec2345..54ef64c 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.05684256553649902}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.05624699592590332}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      13,      23,  ...,  999979,
-                             999991, 1000000]),
-       col_indices=tensor([ 4525, 11074, 13753,  ..., 80507, 85385, 86427]),
-       values=tensor([0.4106, 0.9983, 0.2404,  ..., 0.2427, 0.2624, 0.7034]),
+tensor(crow_indices=tensor([      0,      11,      23,  ...,  999975,
+                             999988, 1000000]),
+       col_indices=tensor([ 2723,  3213,  5616,  ..., 63383, 68375, 91486]),
+       values=tensor([0.8396, 0.9858, 0.3390,  ..., 0.5841, 0.8115, 0.3849]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4963, 0.1898, 0.5953,  ..., 0.4144, 0.1558, 0.0288])
+tensor([0.5352, 0.8213, 0.5999,  ..., 0.4683, 0.8101, 0.1591])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 0.05684256553649902 seconds
+Time: 0.05624699592590332 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18472', '-ss', '100000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.8920857906341553}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18667', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.788297176361084}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       8,      17,  ...,  999981,
+tensor(crow_indices=tensor([      0,      12,      29,  ...,  999982,
                              999989, 1000000]),
-       col_indices=tensor([ 8653, 22699, 39303,  ..., 86578, 89246, 90775]),
-       values=tensor([0.9948, 0.4799, 0.6025,  ..., 0.7759, 0.3812, 0.6990]),
+       col_indices=tensor([ 5929,  8280, 11320,  ..., 91562, 91691, 92303]),
+       values=tensor([0.5085, 0.3313, 0.7584,  ..., 0.0068, 0.8631, 0.9800]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4256, 0.6072, 0.2987,  ..., 0.6512, 0.1573, 0.7068])
+tensor([0.4155, 0.0797, 0.8597,  ..., 0.6063, 0.7260, 0.5851])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 2.8920857906341553 seconds
+Time: 2.788297176361084 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '67064', '-ss', '100000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.610118389129639}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '70295', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.822261571884155}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       9,      22,  ...,  999983,
-                             999993, 1000000]),
-       col_indices=tensor([27755, 29395, 33124,  ..., 74386, 97777, 99456]),
-       values=tensor([0.7148, 0.9361, 0.5875,  ..., 0.1256, 0.4168, 0.2712]),
+tensor(crow_indices=tensor([      0,       5,      12,  ...,  999979,
+                             999990, 1000000]),
+       col_indices=tensor([27644, 31899, 46284,  ..., 52964, 73578, 99989]),
+       values=tensor([0.6537, 0.4454, 0.4070,  ..., 0.8265, 0.7302, 0.8086]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0632, 0.1399, 0.4829,  ..., 0.0512, 0.0510, 0.5050])
+tensor([0.4658, 0.0086, 0.8927,  ..., 0.0810, 0.5824, 0.7438])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.610118389129639 seconds
+Time: 10.822261571884155 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       9,      22,  ...,  999983,
-                             999993, 1000000]),
-       col_indices=tensor([27755, 29395, 33124,  ..., 74386, 97777, 99456]),
-       values=tensor([0.7148, 0.9361, 0.5875,  ..., 0.1256, 0.4168, 0.2712]),
+tensor(crow_indices=tensor([      0,       5,      12,  ...,  999979,
+                             999990, 1000000]),
+       col_indices=tensor([27644, 31899, 46284,  ..., 52964, 73578, 99989]),
+       values=tensor([0.6537, 0.4454, 0.4070,  ..., 0.8265, 0.7302, 0.8086]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0632, 0.1399, 0.4829,  ..., 0.0512, 0.0510, 0.5050])
+tensor([0.4658, 0.0086, 0.8927,  ..., 0.0810, 0.5824, 0.7438])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.610118389129639 seconds
+Time: 10.822261571884155 seconds
 
-[42.15, 40.1, 39.72, 39.74, 39.84, 40.12, 39.64, 39.58, 39.89, 39.55]
-[134.25]
-13.074991226196289
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 67064, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.610118389129639, 'TIME_S_1KI': 0.15820885108448107, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1755.3175721168518, 'W': 134.25}
-[42.15, 40.1, 39.72, 39.74, 39.84, 40.12, 39.64, 39.58, 39.89, 39.55, 42.7, 40.22, 40.9, 40.07, 39.72, 39.89, 39.69, 39.67, 39.56, 44.74]
-722.9200000000001
-36.146
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 67064, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.610118389129639, 'TIME_S_1KI': 0.15820885108448107, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1755.3175721168518, 'W': 134.25, 'J_1KI': 26.173767924920252, 'W_1KI': 2.0018191578193965, 'W_D': 98.104, 'J_D': 1282.7089392547607, 'W_D_1KI': 1.4628414648693786, 'J_D_1KI': 0.021812618765200086}
+[40.4, 39.87, 39.81, 39.7, 40.14, 40.04, 39.63, 39.79, 40.11, 39.75]
+[148.89]
+13.585630416870117
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 70295, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.822261571884155, 'TIME_S_1KI': 0.15395492669299604, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2022.7645127677915, 'W': 148.89}
+[40.4, 39.87, 39.81, 39.7, 40.14, 40.04, 39.63, 39.79, 40.11, 39.75, 41.06, 39.66, 39.86, 39.65, 39.62, 40.13, 39.77, 39.67, 41.05, 40.19]
+719.1999999999999
+35.959999999999994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 70295, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.822261571884155, 'TIME_S_1KI': 0.15395492669299604, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2022.7645127677915, 'W': 148.89, 'J_1KI': 28.775368273245487, 'W_1KI': 2.1180738317092254, 'W_D': 112.92999999999999, 'J_D': 1534.2252429771422, 'W_D_1KI': 1.6065153993882921, 'J_D_1KI': 0.022853907097066535}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json
index 97a1aaa..724eedf 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3865, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.6199471950531, "TIME_S_1KI": 2.7477224308028725, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1736.0991844940186, "W": 125.54, "J_1KI": 449.1847825340281, "W_1KI": 32.48124191461837, "W_D": 89.51725000000002, "J_D": 1237.938702589989, "W_D_1KI": 23.160996119016822, "J_D_1KI": 5.992495761711985}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3822, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.796124458312988, "TIME_S_1KI": 2.8247316740745654, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1752.425606701374, "W": 126.90999999999998, "J_1KI": 458.5101011777535, "W_1KI": 33.2051282051282, "W_D": 90.64599999999999, "J_D": 1251.677342566013, "W_D_1KI": 23.716902145473572, "J_D_1KI": 6.205364245283509}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output
index ab63507..3e9bc76 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.29607152938842773}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.27468323707580566}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      108,      205,  ...,  9999797,
-                             9999886, 10000000]),
-       col_indices=tensor([ 1353,  2200,  3779,  ..., 96854, 97028, 97339]),
-       values=tensor([0.4346, 0.2367, 0.4770,  ..., 0.1479, 0.4649, 0.9103]),
+tensor(crow_indices=tensor([       0,       99,      197,  ...,  9999798,
+                             9999907, 10000000]),
+       col_indices=tensor([  239,  1592,  1731,  ..., 97883, 98029, 99565]),
+       values=tensor([0.8693, 0.2201, 0.3788,  ..., 0.6951, 0.6639, 0.6763]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.4412, 0.7177, 0.2059,  ..., 0.3280, 0.0589, 0.7180])
+tensor([0.7538, 0.4490, 0.6434,  ..., 0.3147, 0.7801, 0.8891])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,39 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 0.29607152938842773 seconds
+Time: 0.27468323707580566 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3546', '-ss', '100000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.63291883468628}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3822', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.796124458312988}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       92,      201,  ...,  9999814,
-                             9999913, 10000000]),
-       col_indices=tensor([ 1097,  1389,  2328,  ..., 96293, 96542, 99036]),
-       values=tensor([0.4476, 0.1977, 0.6820,  ..., 0.8020, 0.1490, 0.2819]),
-       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.6506, 0.9195, 0.4022,  ..., 0.6497, 0.8706, 0.8621])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 10000000
-Density: 0.001
-Time: 9.63291883468628 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3865', '-ss', '100000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.6199471950531}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       98,      197,  ...,  9999799,
+tensor(crow_indices=tensor([       0,      103,      206,  ...,  9999818,
                              9999902, 10000000]),
-       col_indices=tensor([ 2919,  3313,  3728,  ..., 97238, 97697, 98577]),
-       values=tensor([0.4198, 0.7828, 0.7567,  ..., 0.6995, 0.0988, 0.1528]),
+       col_indices=tensor([  218,  1372,  3051,  ..., 94547, 95546, 98643]),
+       values=tensor([0.4032, 0.6777, 0.1457,  ..., 0.3618, 0.7288, 0.5858]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.5695, 0.4144, 0.5638,  ..., 0.3431, 0.2067, 0.0841])
+tensor([0.1082, 0.5340, 0.5447,  ..., 0.1866, 0.5966, 0.6176])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 10.6199471950531 seconds
+Time: 10.796124458312988 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       98,      197,  ...,  9999799,
+tensor(crow_indices=tensor([       0,      103,      206,  ...,  9999818,
                              9999902, 10000000]),
-       col_indices=tensor([ 2919,  3313,  3728,  ..., 97238, 97697, 98577]),
-       values=tensor([0.4198, 0.7828, 0.7567,  ..., 0.6995, 0.0988, 0.1528]),
+       col_indices=tensor([  218,  1372,  3051,  ..., 94547, 95546, 98643]),
+       values=tensor([0.4032, 0.6777, 0.1457,  ..., 0.3618, 0.7288, 0.5858]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.5695, 0.4144, 0.5638,  ..., 0.3431, 0.2067, 0.0841])
+tensor([0.1082, 0.5340, 0.5447,  ..., 0.1866, 0.5966, 0.6176])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 10.6199471950531 seconds
+Time: 10.796124458312988 seconds
 
-[41.63, 40.06, 40.05, 39.72, 39.88, 39.71, 40.32, 40.32, 40.26, 39.8]
-[125.54]
-13.829051971435547
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3865, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.6199471950531, 'TIME_S_1KI': 2.7477224308028725, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1736.0991844940186, 'W': 125.54}
-[41.63, 40.06, 40.05, 39.72, 39.88, 39.71, 40.32, 40.32, 40.26, 39.8, 40.39, 39.7, 39.76, 39.75, 39.78, 40.5, 39.95, 39.84, 40.11, 39.67]
-720.4549999999999
-36.022749999999995
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3865, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.6199471950531, 'TIME_S_1KI': 2.7477224308028725, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1736.0991844940186, 'W': 125.54, 'J_1KI': 449.1847825340281, 'W_1KI': 32.48124191461837, 'W_D': 89.51725000000002, 'J_D': 1237.938702589989, 'W_D_1KI': 23.160996119016822, 'J_D_1KI': 5.992495761711985}
+[40.82, 40.14, 39.68, 40.44, 39.72, 39.8, 39.86, 39.75, 39.61, 39.73]
+[126.91]
+13.808412313461304
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3822, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.796124458312988, 'TIME_S_1KI': 2.8247316740745654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1752.425606701374, 'W': 126.90999999999998}
+[40.82, 40.14, 39.68, 40.44, 39.72, 39.8, 39.86, 39.75, 39.61, 39.73, 41.68, 40.53, 39.66, 40.13, 39.66, 39.82, 39.76, 40.78, 45.04, 39.57]
+725.2800000000001
+36.264
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3822, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.796124458312988, 'TIME_S_1KI': 2.8247316740745654, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1752.425606701374, 'W': 126.90999999999998, 'J_1KI': 458.5101011777535, 'W_1KI': 33.2051282051282, 'W_D': 90.64599999999999, 'J_D': 1251.677342566013, 'W_D_1KI': 23.716902145473572, 'J_D_1KI': 6.205364245283509}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..2f44a1d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 492, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.48446536064148, "TIME_S_1KI": 21.30988894439325, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3692.9655099868774, "W": 98.84, "J_1KI": 7506.0274593229215, "W_1KI": 200.89430894308944, "W_D": 62.13425, "J_D": 2321.526125444174, "W_D_1KI": 126.28912601626016, "J_D_1KI": 256.68521548020357}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..935b6eb
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.1332833766937256}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       992,      1996,  ...,
+                             99998038,  99998977, 100000000]),
+       col_indices=tensor([  426,   448,   664,  ..., 99690, 99718, 99834]),
+       values=tensor([0.7644, 0.0512, 0.5135,  ..., 0.5410, 0.8615, 0.1187]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.1556, 0.5592, 0.0469,  ..., 0.1387, 0.8405, 0.8832])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 2.1332833766937256 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '492', '-ss', '100000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.48446536064148}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       996,      2006,  ...,
+                             99998009,  99999008, 100000000]),
+       col_indices=tensor([   32,   117,   446,  ..., 99626, 99652, 99845]),
+       values=tensor([0.3215, 0.2082, 0.6698,  ..., 0.0359, 0.4063, 0.2578]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.6233, 0.7317, 0.9818,  ..., 0.0719, 0.7892, 0.9884])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 10.48446536064148 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       996,      2006,  ...,
+                             99998009,  99999008, 100000000]),
+       col_indices=tensor([   32,   117,   446,  ..., 99626, 99652, 99845]),
+       values=tensor([0.3215, 0.2082, 0.6698,  ..., 0.0359, 0.4063, 0.2578]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.6233, 0.7317, 0.9818,  ..., 0.0719, 0.7892, 0.9884])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 10.48446536064148 seconds
+
+[40.62, 39.95, 39.79, 39.89, 39.69, 40.11, 39.73, 39.74, 40.0, 39.47]
+[98.84]
+37.36306667327881
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 492, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.48446536064148, 'TIME_S_1KI': 21.30988894439325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3692.9655099868774, 'W': 98.84}
+[40.62, 39.95, 39.79, 39.89, 39.69, 40.11, 39.73, 39.74, 40.0, 39.47, 40.79, 40.68, 40.03, 39.92, 43.59, 51.85, 39.71, 39.54, 39.71, 39.49]
+734.115
+36.70575
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 492, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.48446536064148, 'TIME_S_1KI': 21.30988894439325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3692.9655099868774, 'W': 98.84, 'J_1KI': 7506.0274593229215, 'W_1KI': 200.89430894308944, 'W_D': 62.13425, 'J_D': 2321.526125444174, 'W_D_1KI': 126.28912601626016, 'J_D_1KI': 256.68521548020357}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json
index 9be5a17..02781c1 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 102064, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.524274349212646, "TIME_S_1KI": 0.10311446101674092, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1601.1415306377412, "W": 113.64000000000001, "J_1KI": 15.687622772355983, "W_1KI": 1.1134190311961123, "W_D": 77.54950000000002, "J_D": 1092.6410166331532, "W_D_1KI": 0.7598124706066784, "J_D_1KI": 0.007444470828173288}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 104631, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.92940616607666, "TIME_S_1KI": 0.11401407007556709, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1682.5194715809823, "W": 114.23, "J_1KI": 16.08050646157432, "W_1KI": 1.0917414532977798, "W_D": 78.15350000000001, "J_D": 1151.1405543395283, "W_D_1KI": 0.7469440223260794, "J_D_1KI": 0.007138840518833609}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output
index a8ac003..a082364 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.051177263259887695}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.040479183197021484}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      1,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99999,  99999,
                             100000]),
-       col_indices=tensor([ 5338, 33433, 17911,  ..., 60039, 74427, 45774]),
-       values=tensor([0.9933, 0.3915, 0.2951,  ..., 0.3503, 0.7922, 0.2614]),
+       col_indices=tensor([  647, 35192, 49713,  ..., 72297, 77626,  4033]),
+       values=tensor([0.5259, 0.7735, 0.8729,  ..., 0.9147, 0.2198, 0.2516]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6274, 0.9288, 0.1155,  ..., 0.7548, 0.5951, 0.2372])
+tensor([0.2705, 0.4738, 0.5542,  ..., 0.7481, 0.0692, 0.4617])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 0.051177263259887695 seconds
+Time: 0.040479183197021484 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '20516', '-ss', '100000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.11061429977417}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '25939', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.6030373573303223}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      2,  ...,  99999, 100000,
+tensor(crow_indices=tensor([     0,      0,      0,  ...,  99995,  99999,
                             100000]),
-       col_indices=tensor([35938, 84023, 26382,  ..., 80961, 25218, 78065]),
-       values=tensor([0.1771, 0.7263, 0.1955,  ..., 0.1569, 0.5183, 0.0872]),
+       col_indices=tensor([63737, 56720, 10559,  ..., 75305, 77118, 60915]),
+       values=tensor([0.5479, 0.5972, 0.3396,  ..., 0.3516, 0.8713, 0.6246]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.4261, 0.9316, 0.9486,  ..., 0.4583, 0.3074, 0.5243])
+tensor([0.0931, 0.9309, 0.9267,  ..., 0.4971, 0.7820, 0.8195])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 2.11061429977417 seconds
+Time: 2.6030373573303223 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '102064', '-ss', '100000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.524274349212646}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '104631', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.92940616607666}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      3,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      1,      2,  ...,  99998,  99999,
                             100000]),
-       col_indices=tensor([ 7923, 87583, 82060,  ..., 33729, 87076,   106]),
-       values=tensor([0.1731, 0.5965, 0.6757,  ..., 0.8844, 0.0621, 0.1000]),
+       col_indices=tensor([91220, 70747, 49345,  ..., 32064,  6505, 95139]),
+       values=tensor([0.0238, 0.2992, 0.8012,  ..., 0.6072, 0.6775, 0.2288]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.3641, 0.2409, 0.5686,  ..., 0.5557, 0.7015, 0.9398])
+tensor([0.1871, 0.1293, 0.9330,  ..., 0.6487, 0.5534, 0.6200])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.524274349212646 seconds
+Time: 11.92940616607666 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      3,  ..., 100000, 100000,
+tensor(crow_indices=tensor([     0,      1,      2,  ...,  99998,  99999,
                             100000]),
-       col_indices=tensor([ 7923, 87583, 82060,  ..., 33729, 87076,   106]),
-       values=tensor([0.1731, 0.5965, 0.6757,  ..., 0.8844, 0.0621, 0.1000]),
+       col_indices=tensor([91220, 70747, 49345,  ..., 32064,  6505, 95139]),
+       values=tensor([0.0238, 0.2992, 0.8012,  ..., 0.6072, 0.6775, 0.2288]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.3641, 0.2409, 0.5686,  ..., 0.5557, 0.7015, 0.9398])
+tensor([0.1871, 0.1293, 0.9330,  ..., 0.6487, 0.5534, 0.6200])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.524274349212646 seconds
+Time: 11.92940616607666 seconds
 
-[40.73, 39.59, 39.74, 39.73, 39.63, 39.83, 40.08, 39.98, 40.09, 39.48]
-[113.64]
-14.089594602584839
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102064, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.524274349212646, 'TIME_S_1KI': 0.10311446101674092, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1601.1415306377412, 'W': 113.64000000000001}
-[40.73, 39.59, 39.74, 39.73, 39.63, 39.83, 40.08, 39.98, 40.09, 39.48, 40.32, 39.82, 39.63, 39.81, 39.64, 39.48, 39.85, 39.99, 44.93, 39.45]
-721.81
-36.0905
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102064, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.524274349212646, 'TIME_S_1KI': 0.10311446101674092, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1601.1415306377412, 'W': 113.64000000000001, 'J_1KI': 15.687622772355983, 'W_1KI': 1.1134190311961123, 'W_D': 77.54950000000002, 'J_D': 1092.6410166331532, 'W_D_1KI': 0.7598124706066784, 'J_D_1KI': 0.007444470828173288}
+[41.02, 39.64, 40.4, 39.6, 41.57, 39.92, 39.7, 39.85, 39.57, 40.18]
+[114.23]
+14.729225873947144
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 104631, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.92940616607666, 'TIME_S_1KI': 0.11401407007556709, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1682.5194715809823, 'W': 114.23}
+[41.02, 39.64, 40.4, 39.6, 41.57, 39.92, 39.7, 39.85, 39.57, 40.18, 40.26, 39.84, 40.07, 40.06, 40.35, 39.74, 39.79, 40.2, 40.74, 39.52]
+721.53
+36.076499999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 104631, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.92940616607666, 'TIME_S_1KI': 0.11401407007556709, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1682.5194715809823, 'W': 114.23, 'J_1KI': 16.08050646157432, 'W_1KI': 1.0917414532977798, 'W_D': 78.15350000000001, 'J_D': 1151.1405543395283, 'W_D_1KI': 0.7469440223260794, 'J_D_1KI': 0.007138840518833609}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.json
index 24bf6da..f79591c 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 83443, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.331598043441772, "TIME_S_1KI": 0.12381623435688761, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1753.1380189323427, "W": 133.96, "J_1KI": 21.010007057899916, "W_1KI": 1.6054072840142373, "W_D": 97.99700000000001, "J_D": 1282.489298606396, "W_D_1KI": 1.1744184652996656, "J_D_1KI": 0.014074499542198454}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 84017, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.68351411819458, "TIME_S_1KI": 0.1271589573323801, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1756.137734184265, "W": 134.64, "J_1KI": 20.902171396077758, "W_1KI": 1.6025328207386598, "W_D": 98.332, "J_D": 1282.5648817424774, "W_D_1KI": 1.170382184557887, "J_D_1KI": 0.013930302016947607}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.output
index 566b51a..94d6712 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.04202604293823242}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.048097848892211914}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,     10,  ..., 499989, 499993,
+tensor(crow_indices=tensor([     0,      2,      4,  ..., 499987, 499994,
                             500000]),
-       col_indices=tensor([44479,  4048, 15938,  ..., 81904, 89204, 96058]),
-       values=tensor([0.8024, 0.2371, 0.1804,  ..., 0.7304, 0.5867, 0.0881]),
+       col_indices=tensor([45986, 87496, 16093,  ..., 81459, 91507, 94938]),
+       values=tensor([0.5541, 0.7240, 0.1434,  ..., 0.0835, 0.6754, 0.2974]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.7637, 0.3596, 0.9771,  ..., 0.6123, 0.8042, 0.6339])
+tensor([0.9503, 0.0035, 0.0750,  ..., 0.9219, 0.8718, 0.0631])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 0.04202604293823242 seconds
+Time: 0.048097848892211914 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24984', '-ss', '100000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.1438426971435547}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21830', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.7281758785247803}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,     12,  ..., 499992, 499996,
+tensor(crow_indices=tensor([     0,      2,      6,  ..., 499994, 499997,
                             500000]),
-       col_indices=tensor([25849, 26475, 42516,  ..., 54532, 74351, 87242]),
-       values=tensor([0.9779, 0.7287, 0.9943,  ..., 0.8976, 0.9175, 0.6342]),
+       col_indices=tensor([83268, 88397, 15509,  ..., 59756, 77530, 84602]),
+       values=tensor([0.3296, 0.6421, 0.2508,  ..., 0.6584, 0.8479, 0.6996]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.3426, 0.5652, 0.2473,  ..., 0.4439, 0.3784, 0.0403])
+tensor([0.4117, 0.2597, 0.3803,  ..., 0.1721, 0.1242, 0.1947])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 3.1438426971435547 seconds
+Time: 2.7281758785247803 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '83443', '-ss', '100000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.331598043441772}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '84017', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.68351411819458}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,      9,  ..., 499992, 499996,
+tensor(crow_indices=tensor([     0,      7,     11,  ..., 499993, 499997,
                             500000]),
-       col_indices=tensor([44464, 48782, 50602,  ..., 44812, 48851, 96308]),
-       values=tensor([0.0768, 0.4231, 0.3229,  ..., 0.7263, 0.8571, 0.9151]),
+       col_indices=tensor([45345, 56173, 61145,  ..., 41289, 72782, 75243]),
+       values=tensor([0.0688, 0.1783, 0.1543,  ..., 0.1655, 0.2729, 0.4150]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.5899, 0.0490, 0.9717,  ..., 0.2037, 0.9811, 0.9760])
+tensor([0.3874, 0.1633, 0.6273,  ..., 0.4912, 0.7210, 0.5103])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.331598043441772 seconds
+Time: 10.68351411819458 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,      9,  ..., 499992, 499996,
+tensor(crow_indices=tensor([     0,      7,     11,  ..., 499993, 499997,
                             500000]),
-       col_indices=tensor([44464, 48782, 50602,  ..., 44812, 48851, 96308]),
-       values=tensor([0.0768, 0.4231, 0.3229,  ..., 0.7263, 0.8571, 0.9151]),
+       col_indices=tensor([45345, 56173, 61145,  ..., 41289, 72782, 75243]),
+       values=tensor([0.0688, 0.1783, 0.1543,  ..., 0.1655, 0.2729, 0.4150]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.5899, 0.0490, 0.9717,  ..., 0.2037, 0.9811, 0.9760])
+tensor([0.3874, 0.1633, 0.6273,  ..., 0.4912, 0.7210, 0.5103])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.331598043441772 seconds
+Time: 10.68351411819458 seconds
 
-[41.17, 39.77, 40.36, 40.06, 41.66, 39.61, 39.74, 39.58, 39.55, 39.78]
-[133.96]
-13.087026119232178
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 83443, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.331598043441772, 'TIME_S_1KI': 0.12381623435688761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1753.1380189323427, 'W': 133.96}
-[41.17, 39.77, 40.36, 40.06, 41.66, 39.61, 39.74, 39.58, 39.55, 39.78, 41.11, 39.76, 39.73, 39.7, 39.67, 40.13, 39.73, 39.66, 39.62, 39.8]
-719.26
-35.963
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 83443, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.331598043441772, 'TIME_S_1KI': 0.12381623435688761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1753.1380189323427, 'W': 133.96, 'J_1KI': 21.010007057899916, 'W_1KI': 1.6054072840142373, 'W_D': 97.99700000000001, 'J_D': 1282.489298606396, 'W_D_1KI': 1.1744184652996656, 'J_D_1KI': 0.014074499542198454}
+[47.53, 39.57, 39.66, 39.67, 39.71, 39.49, 40.16, 39.73, 39.99, 40.66]
+[134.64]
+13.043209552764893
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 84017, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.68351411819458, 'TIME_S_1KI': 0.1271589573323801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1756.137734184265, 'W': 134.64}
+[47.53, 39.57, 39.66, 39.67, 39.71, 39.49, 40.16, 39.73, 39.99, 40.66, 41.15, 39.61, 39.86, 39.81, 39.78, 40.03, 39.61, 39.49, 45.38, 39.88]
+726.16
+36.308
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 84017, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.68351411819458, 'TIME_S_1KI': 0.1271589573323801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1756.137734184265, 'W': 134.64, 'J_1KI': 20.902171396077758, 'W_1KI': 1.6025328207386598, 'W_D': 98.332, 'J_D': 1282.5648817424774, 'W_D_1KI': 1.170382184557887, 'J_D_1KI': 0.013930302016947607}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json
index 846867c..b907b96 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 288187, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.643323421478271, "TIME_S_1KI": 0.03693200394701451, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1243.6877346038818, "W": 98.76, "J_1KI": 4.315558073764194, "W_1KI": 0.34269415344897586, "W_D": 63.04600000000001, "J_D": 793.9402279853822, "W_D_1KI": 0.21876767515536788, "J_D_1KI": 0.0007591170842382477}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 289973, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.402179718017578, "TIME_S_1KI": 0.035872925127572494, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1257.0382731056213, "W": 99.11, "J_1KI": 4.3350183400027635, "W_1KI": 0.3417904425584451, "W_D": 62.7475, "J_D": 795.8430939531327, "W_D_1KI": 0.21639083638821546, "J_D_1KI": 0.0007462447758522879}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output
index c5c4dec..3a689e9 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.02071547508239746}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.01844310760498047}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9999, 10000]),
-       col_indices=tensor([ 662, 2214, 9373,  ..., 9890, 1994, 4209]),
-       values=tensor([0.5116, 0.1051, 0.5373,  ..., 0.4151, 0.7725, 0.9175]),
+tensor(crow_indices=tensor([    0,     2,     3,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([5664, 7936, 1380,  ..., 8294, 3730, 2763]),
+       values=tensor([0.9034, 0.8968, 0.1086,  ..., 0.0542, 0.5046, 0.8853]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.1401, 0.0502, 0.3458,  ..., 0.2506, 0.9913, 0.9973])
+tensor([0.8522, 0.7110, 0.6403,  ..., 0.2419, 0.5994, 0.8952])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 0.02071547508239746 seconds
+Time: 0.01844310760498047 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '50686', '-ss', '10000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.8467259407043457}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '56931', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.0614843368530273}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     5,  ...,  9998, 10000, 10000]),
-       col_indices=tensor([2329, 7525, 8810,  ..., 9177, 1519, 2359]),
-       values=tensor([0.1835, 0.9536, 0.7906,  ..., 0.4035, 0.0564, 0.3832]),
+tensor(crow_indices=tensor([    0,     0,     1,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([3038, 3101,   58,  ..., 2158, 2755, 7454]),
+       values=tensor([0.2769, 0.6754, 0.9765,  ..., 0.0751, 0.2423, 0.1071]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.5918, 0.9817, 0.1058,  ..., 0.3816, 0.0120, 0.7112])
+tensor([0.8739, 0.5769, 0.3937,  ..., 0.8916, 0.1494, 0.8792])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 1.8467259407043457 seconds
+Time: 2.0614843368530273 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '288187', '-ss', '10000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.643323421478271}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '289973', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.402179718017578}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9998, 10000]),
-       col_indices=tensor([5118, 9103, 6912,  ..., 6081, 2494, 7728]),
-       values=tensor([0.1845, 0.4117, 0.0579,  ..., 0.8363, 0.9429, 0.5429]),
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9996,  9999, 10000]),
+       col_indices=tensor([9316, 1893, 9256,  ..., 5002, 7518, 9571]),
+       values=tensor([0.4065, 0.7850, 0.3297,  ..., 0.8532, 0.1303, 0.8202]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.5505, 0.3516, 0.4265,  ..., 0.6375, 0.7561, 0.2541])
+tensor([0.1843, 0.6409, 0.5987,  ..., 0.8529, 0.6169, 0.0022])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.643323421478271 seconds
+Time: 10.402179718017578 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9998, 10000]),
-       col_indices=tensor([5118, 9103, 6912,  ..., 6081, 2494, 7728]),
-       values=tensor([0.1845, 0.4117, 0.0579,  ..., 0.8363, 0.9429, 0.5429]),
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9996,  9999, 10000]),
+       col_indices=tensor([9316, 1893, 9256,  ..., 5002, 7518, 9571]),
+       values=tensor([0.4065, 0.7850, 0.3297,  ..., 0.8532, 0.1303, 0.8202]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.5505, 0.3516, 0.4265,  ..., 0.6375, 0.7561, 0.2541])
+tensor([0.1843, 0.6409, 0.5987,  ..., 0.8529, 0.6169, 0.0022])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.643323421478271 seconds
+Time: 10.402179718017578 seconds
 
-[40.51, 40.09, 39.52, 39.33, 39.47, 39.48, 39.43, 39.77, 39.53, 39.82]
-[98.76]
-12.59303092956543
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 288187, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.643323421478271, 'TIME_S_1KI': 0.03693200394701451, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1243.6877346038818, 'W': 98.76}
-[40.51, 40.09, 39.52, 39.33, 39.47, 39.48, 39.43, 39.77, 39.53, 39.82, 41.15, 40.14, 40.36, 39.64, 39.34, 39.28, 39.84, 39.18, 39.56, 39.16]
-714.28
-35.714
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 288187, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.643323421478271, 'TIME_S_1KI': 0.03693200394701451, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1243.6877346038818, 'W': 98.76, 'J_1KI': 4.315558073764194, 'W_1KI': 0.34269415344897586, 'W_D': 63.04600000000001, 'J_D': 793.9402279853822, 'W_D_1KI': 0.21876767515536788, 'J_D_1KI': 0.0007591170842382477}
+[40.09, 39.43, 40.32, 39.57, 45.24, 39.33, 39.99, 39.89, 39.71, 39.43]
+[99.11]
+12.683263778686523
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 289973, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.402179718017578, 'TIME_S_1KI': 0.035872925127572494, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1257.0382731056213, 'W': 99.11}
+[40.09, 39.43, 40.32, 39.57, 45.24, 39.33, 39.99, 39.89, 39.71, 39.43, 40.98, 45.17, 40.3, 39.23, 39.55, 39.48, 39.54, 39.24, 41.38, 39.26]
+727.25
+36.3625
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 289973, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.402179718017578, 'TIME_S_1KI': 0.035872925127572494, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1257.0382731056213, 'W': 99.11, 'J_1KI': 4.3350183400027635, 'W_1KI': 0.3417904425584451, 'W_D': 62.7475, 'J_D': 795.8430939531327, 'W_D_1KI': 0.21639083638821546, 'J_D_1KI': 0.0007462447758522879}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json
index d5b0fd0..965ed1f 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 192082, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.38179612159729, "TIME_S_1KI": 0.054048771470503694, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1536.0875409460068, "W": 107.47, "J_1KI": 7.997040539696623, "W_1KI": 0.5595006299392968, "W_D": 71.52775, "J_D": 1022.3586638773679, "W_D_1KI": 0.3723813267250445, "J_D_1KI": 0.0019386581081259277}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 195124, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.008607864379883, "TIME_S_1KI": 0.056418522910456334, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1502.995405883789, "W": 107.68, "J_1KI": 7.7027705760633705, "W_1KI": 0.551854205530842, "W_D": 71.108, "J_D": 992.5241207427979, "W_D_1KI": 0.36442467354092783, "J_D_1KI": 0.0018676568415004193}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output
index 41b527b..f366c5f 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.02809286117553711}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.024670839309692383}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     18,  ...,  99983,  99993,
+tensor(crow_indices=tensor([     0,      9,     26,  ...,  99973,  99990,
                             100000]),
-       col_indices=tensor([2653, 3722, 5304,  ..., 7707, 8674, 8869]),
-       values=tensor([0.5856, 0.9425, 0.9349,  ..., 0.4089, 0.4268, 0.7151]),
+       col_indices=tensor([ 472, 3265, 3392,  ..., 3557, 6998, 8496]),
+       values=tensor([0.9970, 0.8966, 0.8111,  ..., 0.5588, 0.4205, 0.6657]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.5412, 0.5320, 0.4895,  ..., 0.9332, 0.4774, 0.7844])
+tensor([0.8047, 0.1107, 0.7030,  ..., 0.4801, 0.5633, 0.4095])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 0.02809286117553711 seconds
+Time: 0.024670839309692383 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '37376', '-ss', '10000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.043123722076416}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '42560', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.2902352809906006}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     18,     27,  ...,  99987,  99996,
+tensor(crow_indices=tensor([     0,     13,     29,  ...,  99985,  99992,
                             100000]),
-       col_indices=tensor([ 496, 1705, 2513,  ..., 6230, 8377, 9882]),
-       values=tensor([0.7106, 0.5928, 0.5041,  ..., 0.9691, 0.8218, 0.7424]),
+       col_indices=tensor([ 661, 1907, 2234,  ..., 7098, 7592, 9213]),
+       values=tensor([0.9203, 0.6464, 0.5146,  ..., 0.9440, 0.8063, 0.5593]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6869, 0.6587, 0.3624,  ..., 0.7168, 0.6886, 0.1198])
+tensor([0.7724, 0.0256, 0.1655,  ..., 0.1624, 0.5094, 0.8407])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 2.043123722076416 seconds
+Time: 2.2902352809906006 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '192082', '-ss', '10000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.38179612159729}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '195124', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 11.008607864379883}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     14,     28,  ...,  99972,  99987,
+tensor(crow_indices=tensor([     0,      6,     18,  ...,  99982,  99991,
                             100000]),
-       col_indices=tensor([  27, 2567, 2642,  ..., 7209, 7267, 7735]),
-       values=tensor([0.8851, 0.6027, 0.9664,  ..., 0.7310, 0.7426, 0.3698]),
+       col_indices=tensor([  16, 1284, 1794,  ..., 7682, 8352, 8990]),
+       values=tensor([0.6866, 0.8260, 0.6116,  ..., 0.7861, 0.8522, 0.8329]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8025, 0.3022, 0.3457,  ..., 0.3811, 0.1140, 0.9144])
+tensor([0.3102, 0.5794, 0.7585,  ..., 0.2279, 0.2498, 0.6683])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.38179612159729 seconds
+Time: 11.008607864379883 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     14,     28,  ...,  99972,  99987,
+tensor(crow_indices=tensor([     0,      6,     18,  ...,  99982,  99991,
                             100000]),
-       col_indices=tensor([  27, 2567, 2642,  ..., 7209, 7267, 7735]),
-       values=tensor([0.8851, 0.6027, 0.9664,  ..., 0.7310, 0.7426, 0.3698]),
+       col_indices=tensor([  16, 1284, 1794,  ..., 7682, 8352, 8990]),
+       values=tensor([0.6866, 0.8260, 0.6116,  ..., 0.7861, 0.8522, 0.8329]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.8025, 0.3022, 0.3457,  ..., 0.3811, 0.1140, 0.9144])
+tensor([0.3102, 0.5794, 0.7585,  ..., 0.2279, 0.2498, 0.6683])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.38179612159729 seconds
+Time: 11.008607864379883 seconds
 
-[40.11, 39.55, 39.33, 44.82, 39.9, 39.64, 39.98, 39.29, 39.99, 39.21]
-[107.47]
-14.293175220489502
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 192082, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.38179612159729, 'TIME_S_1KI': 0.054048771470503694, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1536.0875409460068, 'W': 107.47}
-[40.11, 39.55, 39.33, 44.82, 39.9, 39.64, 39.98, 39.29, 39.99, 39.21, 40.63, 39.47, 39.85, 39.28, 39.66, 39.35, 39.46, 39.87, 39.78, 39.3]
-718.845
-35.94225
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 192082, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.38179612159729, 'TIME_S_1KI': 0.054048771470503694, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1536.0875409460068, 'W': 107.47, 'J_1KI': 7.997040539696623, 'W_1KI': 0.5595006299392968, 'W_D': 71.52775, 'J_D': 1022.3586638773679, 'W_D_1KI': 0.3723813267250445, 'J_D_1KI': 0.0019386581081259277}
+[40.48, 39.36, 39.67, 39.94, 39.35, 39.97, 40.11, 39.3, 39.28, 39.21]
+[107.68]
+13.95798110961914
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 195124, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.008607864379883, 'TIME_S_1KI': 0.056418522910456334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.995405883789, 'W': 107.68}
+[40.48, 39.36, 39.67, 39.94, 39.35, 39.97, 40.11, 39.3, 39.28, 39.21, 40.79, 39.88, 40.47, 39.31, 39.8, 39.64, 39.75, 55.65, 39.37, 40.7]
+731.44
+36.572
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 195124, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 11.008607864379883, 'TIME_S_1KI': 0.056418522910456334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1502.995405883789, 'W': 107.68, 'J_1KI': 7.7027705760633705, 'W_1KI': 0.551854205530842, 'W_D': 71.108, 'J_D': 992.5241207427979, 'W_D_1KI': 0.36442467354092783, 'J_D_1KI': 0.0018676568415004193}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json
index c0a8393..b099625 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 102052, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.32213807106018, "TIME_S_1KI": 0.10114586750931075, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1720.3125169992445, "W": 134.17, "J_1KI": 16.857215115815904, "W_1KI": 1.3147219064790499, "W_D": 98.29849999999999, "J_D": 1260.3722139990327, "W_D_1KI": 0.9632197311174694, "J_D_1KI": 0.0094385189032794}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 106323, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.322136163711548, "TIME_S_1KI": 0.09708281523011528, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1775.71419588089, "W": 134.46, "J_1KI": 16.701129538113953, "W_1KI": 1.2646370023419204, "W_D": 98.46650000000001, "J_D": 1300.3745490756037, "W_D_1KI": 0.9261072392614957, "J_D_1KI": 0.00871031892686903}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output
index 3b630d1..c1541bb 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.04350447654724121}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.07525515556335449}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      98,     200,  ...,  999802,
-                             999895, 1000000]),
-       col_indices=tensor([  47,   93,  107,  ..., 9931, 9947, 9964]),
-       values=tensor([0.2387, 0.2735, 0.7135,  ..., 0.1692, 0.6802, 0.4186]),
+tensor(crow_indices=tensor([      0,      96,     190,  ...,  999807,
+                             999911, 1000000]),
+       col_indices=tensor([  14,   77,  173,  ..., 9899, 9902, 9995]),
+       values=tensor([0.5533, 0.3727, 0.7903,  ..., 0.3886, 0.8573, 0.3671]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2660, 0.3479, 0.7430,  ..., 0.3350, 0.7379, 0.6869])
+tensor([0.6975, 0.9655, 0.7366,  ..., 0.8197, 0.4964, 0.3947])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 0.04350447654724121 seconds
+Time: 0.07525515556335449 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24135', '-ss', '10000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.483203411102295}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '13952', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 1.451746940612793}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     121,     211,  ...,  999813,
-                             999902, 1000000]),
-       col_indices=tensor([ 152,  193,  233,  ..., 9824, 9889, 9990]),
-       values=tensor([0.9787, 0.2142, 0.0572,  ..., 0.5889, 0.8836, 0.8390]),
+tensor(crow_indices=tensor([      0,      72,     172,  ...,  999809,
+                             999904, 1000000]),
+       col_indices=tensor([ 276,  344,  376,  ..., 9517, 9819, 9830]),
+       values=tensor([0.1929, 0.5167, 0.0076,  ..., 0.1328, 0.6457, 0.0820]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.2484, 0.0072, 0.5266,  ..., 0.8378, 0.3257, 0.7895])
+tensor([0.0756, 0.3906, 0.9571,  ..., 0.7269, 0.5864, 0.5417])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 2.483203411102295 seconds
+Time: 1.451746940612793 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '102052', '-ss', '10000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.32213807106018}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100910', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.965423345565796}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     109,     197,  ...,  999808,
-                             999909, 1000000]),
-       col_indices=tensor([  12,  158,  312,  ..., 9915, 9965, 9970]),
-       values=tensor([0.1097, 0.7996, 0.8802,  ..., 0.2965, 0.2793, 0.1775]),
+tensor(crow_indices=tensor([      0,     105,     193,  ...,  999820,
+                             999910, 1000000]),
+       col_indices=tensor([  57,  196,  279,  ..., 9777, 9791, 9801]),
+       values=tensor([0.4514, 0.6194, 0.2391,  ..., 0.7664, 0.6835, 0.8970]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4138, 0.6115, 0.5428,  ..., 0.5829, 0.0748, 0.9104])
+tensor([0.6512, 0.1898, 0.7873,  ..., 0.0404, 0.8525, 0.9858])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.32213807106018 seconds
+Time: 9.965423345565796 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '106323', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.322136163711548}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     109,     197,  ...,  999808,
-                             999909, 1000000]),
-       col_indices=tensor([  12,  158,  312,  ..., 9915, 9965, 9970]),
-       values=tensor([0.1097, 0.7996, 0.8802,  ..., 0.2965, 0.2793, 0.1775]),
+tensor(crow_indices=tensor([      0,     106,     209,  ...,  999783,
+                             999891, 1000000]),
+       col_indices=tensor([  14,   49,   70,  ..., 9716, 9833, 9895]),
+       values=tensor([0.0681, 0.7835, 0.1986,  ..., 0.8581, 0.4299, 0.1908]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.4138, 0.6115, 0.5428,  ..., 0.5829, 0.0748, 0.9104])
+tensor([0.6587, 0.6689, 0.7198,  ..., 0.8761, 0.4647, 0.5939])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +76,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.32213807106018 seconds
+Time: 10.322136163711548 seconds
 
-[40.44, 39.51, 39.85, 40.81, 39.86, 41.16, 39.43, 39.73, 39.64, 39.53]
-[134.17]
-12.821886539459229
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102052, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.32213807106018, 'TIME_S_1KI': 0.10114586750931075, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1720.3125169992445, 'W': 134.17}
-[40.44, 39.51, 39.85, 40.81, 39.86, 41.16, 39.43, 39.73, 39.64, 39.53, 40.32, 40.34, 39.64, 39.51, 39.49, 39.33, 39.71, 39.95, 39.67, 39.31]
-717.43
-35.8715
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102052, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.32213807106018, 'TIME_S_1KI': 0.10114586750931075, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1720.3125169992445, 'W': 134.17, 'J_1KI': 16.857215115815904, 'W_1KI': 1.3147219064790499, 'W_D': 98.29849999999999, 'J_D': 1260.3722139990327, 'W_D_1KI': 0.9632197311174694, 'J_D_1KI': 0.0094385189032794}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     106,     209,  ...,  999783,
+                             999891, 1000000]),
+       col_indices=tensor([  14,   49,   70,  ..., 9716, 9833, 9895]),
+       values=tensor([0.0681, 0.7835, 0.1986,  ..., 0.8581, 0.4299, 0.1908]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6587, 0.6689, 0.7198,  ..., 0.8761, 0.4647, 0.5939])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.322136163711548 seconds
+
+[40.63, 41.42, 40.27, 39.92, 39.54, 39.44, 39.67, 39.68, 39.88, 39.61]
+[134.46]
+13.206263542175293
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 106323, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.322136163711548, 'TIME_S_1KI': 0.09708281523011528, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1775.71419588089, 'W': 134.46}
+[40.63, 41.42, 40.27, 39.92, 39.54, 39.44, 39.67, 39.68, 39.88, 39.61, 40.19, 39.89, 39.99, 40.22, 39.94, 39.79, 40.86, 39.77, 39.58, 39.59]
+719.87
+35.9935
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 106323, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.322136163711548, 'TIME_S_1KI': 0.09708281523011528, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1775.71419588089, 'W': 134.46, 'J_1KI': 16.701129538113953, 'W_1KI': 1.2646370023419204, 'W_D': 98.46650000000001, 'J_D': 1300.3745490756037, 'W_D_1KI': 0.9261072392614957, 'J_D_1KI': 0.00871031892686903}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json
index 0c8793e..c4d1519 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 27901, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.459260940551758, "TIME_S_1KI": 0.3748704684617669, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2101.511001520157, "W": 151.74, "J_1KI": 75.32027531343526, "W_1KI": 5.43851474857532, "W_D": 115.6735, "J_D": 1602.0108925421239, "W_D_1KI": 4.145854987276442, "J_D_1KI": 0.14859162708420637}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 27006, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.041618347167969, "TIME_S_1KI": 0.3718291619331989, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2050.3459711432456, "W": 151.98999999999998, "J_1KI": 75.92186814571745, "W_1KI": 5.628008590683551, "W_D": 115.43924999999999, "J_D": 1557.2761441496014, "W_D_1KI": 4.274577871584092, "J_D_1KI": 0.1582825250531027}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output
index d46dec8..be67e6c 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.07326960563659668}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.07368612289428711}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     503,     989,  ..., 4998955,
-                            4999450, 5000000]),
-       col_indices=tensor([  38,   72,   81,  ..., 9956, 9978, 9983]),
-       values=tensor([0.2927, 0.3163, 0.4567,  ..., 0.1935, 0.9639, 0.3715]),
+tensor(crow_indices=tensor([      0,     504,    1002,  ..., 4999005,
+                            4999503, 5000000]),
+       col_indices=tensor([  24,   56,   60,  ..., 9903, 9934, 9970]),
+       values=tensor([0.3302, 0.5478, 0.2112,  ..., 0.1196, 0.3028, 0.5060]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.4283, 0.0472, 0.5653,  ..., 0.2916, 0.5894, 0.9993])
+tensor([0.9097, 0.8385, 0.6941,  ..., 0.2367, 0.9571, 0.7158])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 0.07326960563659668 seconds
+Time: 0.07368612289428711 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '14330', '-ss', '10000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 5.392660140991211}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '14249', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 5.53993821144104}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     524,    1025,  ..., 4998972,
-                            4999489, 5000000]),
-       col_indices=tensor([   6,   15,   16,  ..., 9973, 9985, 9996]),
-       values=tensor([0.1466, 0.4320, 0.8734,  ..., 0.2839, 0.7163, 0.2149]),
+tensor(crow_indices=tensor([      0,     503,     985,  ..., 4999025,
+                            4999514, 5000000]),
+       col_indices=tensor([  21,   39,   75,  ..., 9920, 9938, 9983]),
+       values=tensor([0.8595, 0.9918, 0.9127,  ..., 0.5649, 0.2080, 0.0100]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.6711, 0.1737, 0.7087,  ..., 0.1819, 0.7746, 0.6924])
+tensor([0.2539, 0.1414, 0.6201,  ..., 0.7647, 0.9191, 0.5645])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 5.392660140991211 seconds
+Time: 5.53993821144104 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27901', '-ss', '10000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.459260940551758}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27006', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.041618347167969}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     478,     963,  ..., 4998976,
-                            4999469, 5000000]),
-       col_indices=tensor([   4,    6,    8,  ..., 9977, 9981, 9998]),
-       values=tensor([0.4938, 0.7817, 0.2868,  ..., 0.2355, 0.4075, 0.9137]),
+tensor(crow_indices=tensor([      0,     520,    1016,  ..., 4998997,
+                            4999496, 5000000]),
+       col_indices=tensor([  56,   89,  123,  ..., 9981, 9985, 9989]),
+       values=tensor([0.4302, 0.2299, 0.3814,  ..., 0.0183, 0.5254, 0.1358]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.7828, 0.6669, 0.8649,  ..., 0.0217, 0.0077, 0.7398])
+tensor([0.6789, 0.6658, 0.3096,  ..., 0.0338, 0.4560, 0.9370])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.459260940551758 seconds
+Time: 10.041618347167969 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     478,     963,  ..., 4998976,
-                            4999469, 5000000]),
-       col_indices=tensor([   4,    6,    8,  ..., 9977, 9981, 9998]),
-       values=tensor([0.4938, 0.7817, 0.2868,  ..., 0.2355, 0.4075, 0.9137]),
+tensor(crow_indices=tensor([      0,     520,    1016,  ..., 4998997,
+                            4999496, 5000000]),
+       col_indices=tensor([  56,   89,  123,  ..., 9981, 9985, 9989]),
+       values=tensor([0.4302, 0.2299, 0.3814,  ..., 0.0183, 0.5254, 0.1358]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.7828, 0.6669, 0.8649,  ..., 0.0217, 0.0077, 0.7398])
+tensor([0.6789, 0.6658, 0.3096,  ..., 0.0338, 0.4560, 0.9370])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.459260940551758 seconds
+Time: 10.041618347167969 seconds
 
-[40.28, 39.74, 40.09, 39.52, 39.81, 39.49, 40.09, 39.86, 39.96, 39.9]
-[151.74]
-13.849420070648193
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27901, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.459260940551758, 'TIME_S_1KI': 0.3748704684617669, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2101.511001520157, 'W': 151.74}
-[40.28, 39.74, 40.09, 39.52, 39.81, 39.49, 40.09, 39.86, 39.96, 39.9, 41.6, 42.09, 41.06, 39.67, 39.75, 39.6, 39.57, 40.11, 40.26, 39.54]
-721.33
-36.066500000000005
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27901, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.459260940551758, 'TIME_S_1KI': 0.3748704684617669, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2101.511001520157, 'W': 151.74, 'J_1KI': 75.32027531343526, 'W_1KI': 5.43851474857532, 'W_D': 115.6735, 'J_D': 1602.0108925421239, 'W_D_1KI': 4.145854987276442, 'J_D_1KI': 0.14859162708420637}
+[41.81, 39.96, 40.78, 39.7, 39.78, 45.53, 39.97, 39.66, 39.66, 39.55]
+[151.99]
+13.490005731582642
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27006, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.041618347167969, 'TIME_S_1KI': 0.3718291619331989, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2050.3459711432456, 'W': 151.98999999999998}
+[41.81, 39.96, 40.78, 39.7, 39.78, 45.53, 39.97, 39.66, 39.66, 39.55, 40.64, 44.96, 39.69, 39.49, 39.83, 39.47, 40.23, 39.93, 41.25, 40.25]
+731.015
+36.55075
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27006, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.041618347167969, 'TIME_S_1KI': 0.3718291619331989, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2050.3459711432456, 'W': 151.98999999999998, 'J_1KI': 75.92186814571745, 'W_1KI': 5.628008590683551, 'W_D': 115.43924999999999, 'J_D': 1557.2761441496014, 'W_D_1KI': 4.274577871584092, 'J_D_1KI': 0.1582825250531027}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json
index 2bdc411..56c87c7 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4895, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.465762615203857, "TIME_S_1KI": 2.3423416987137604, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1961.564714949131, "W": 125.13, "J_1KI": 400.72823594466416, "W_1KI": 25.562819203268642, "W_D": 88.61599999999999, "J_D": 1389.1634202823636, "W_D_1KI": 18.10337078651685, "J_D_1KI": 3.698339282230205}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 5118, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 12.526283264160156, "TIME_S_1KI": 2.4474957530598194, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1992.3463458251954, "W": 126.08, "J_1KI": 389.28220903188657, "W_1KI": 24.634622899570147, "W_D": 89.46600000000001, "J_D": 1413.7631517734528, "W_D_1KI": 17.480656506447833, "J_D_1KI": 3.415524913334864}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.output
index c73c74f..e71985c 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.26529860496520996}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.26447367668151855}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      948,     1895,  ...,  9998021,
-                             9999002, 10000000]),
-       col_indices=tensor([   3,    4,   24,  ..., 9958, 9984, 9986]),
-       values=tensor([0.2249, 0.5337, 0.8362,  ..., 0.6636, 0.7975, 0.6242]),
+tensor(crow_indices=tensor([       0,      982,     1983,  ...,  9997965,
+                             9999026, 10000000]),
+       col_indices=tensor([   1,    6,   14,  ..., 9961, 9972, 9977]),
+       values=tensor([0.8398, 0.8694, 0.5639,  ..., 0.7105, 0.5707, 0.6304]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1264, 0.7394, 0.5519,  ..., 0.0745, 0.0081, 0.2644])
+tensor([0.9072, 0.8563, 0.2325,  ..., 0.0740, 0.0762, 0.0501])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 0.26529860496520996 seconds
+Time: 0.26447367668151855 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3957', '-ss', '10000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.487387418746948}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3970', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.143938302993774}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      978,     1933,  ...,  9998004,
-                             9999014, 10000000]),
-       col_indices=tensor([   3,    5,    6,  ..., 9972, 9982, 9998]),
-       values=tensor([0.7080, 0.9187, 0.9413,  ..., 0.1315, 0.2244, 0.9797]),
+tensor(crow_indices=tensor([       0,      962,     1945,  ...,  9998016,
+                             9999016, 10000000]),
+       col_indices=tensor([  14,   21,   22,  ..., 9984, 9990, 9991]),
+       values=tensor([0.7193, 0.5646, 0.3307,  ..., 0.4459, 0.1754, 0.3663]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.5306, 0.8726, 0.4027,  ..., 0.7037, 0.0033, 0.8016])
+tensor([0.1647, 0.5409, 0.1205,  ..., 0.9066, 0.8390, 0.9270])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 8.487387418746948 seconds
+Time: 8.143938302993774 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4895', '-ss', '10000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.465762615203857}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '5118', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 12.526283264160156}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      986,     1965,  ...,  9997996,
-                             9999022, 10000000]),
-       col_indices=tensor([   5,   25,   37,  ..., 9984, 9993, 9998]),
-       values=tensor([0.8800, 0.4752, 0.0446,  ..., 0.6391, 0.5084, 0.8692]),
+tensor(crow_indices=tensor([       0,      996,     2041,  ...,  9997995,
+                             9998945, 10000000]),
+       col_indices=tensor([   1,    3,    9,  ..., 9952, 9968, 9996]),
+       values=tensor([0.0713, 0.2648, 0.4521,  ..., 0.1066, 0.1213, 0.7606]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.6122, 0.5951, 0.3953,  ..., 0.4999, 0.2315, 0.6538])
+tensor([0.7059, 0.0653, 0.1181,  ..., 0.5566, 0.2736, 0.8378])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 11.465762615203857 seconds
+Time: 12.526283264160156 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      986,     1965,  ...,  9997996,
-                             9999022, 10000000]),
-       col_indices=tensor([   5,   25,   37,  ..., 9984, 9993, 9998]),
-       values=tensor([0.8800, 0.4752, 0.0446,  ..., 0.6391, 0.5084, 0.8692]),
+tensor(crow_indices=tensor([       0,      996,     2041,  ...,  9997995,
+                             9998945, 10000000]),
+       col_indices=tensor([   1,    3,    9,  ..., 9952, 9968, 9996]),
+       values=tensor([0.0713, 0.2648, 0.4521,  ..., 0.1066, 0.1213, 0.7606]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.6122, 0.5951, 0.3953,  ..., 0.4999, 0.2315, 0.6538])
+tensor([0.7059, 0.0653, 0.1181,  ..., 0.5566, 0.2736, 0.8378])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 11.465762615203857 seconds
+Time: 12.526283264160156 seconds
 
-[40.6, 45.25, 40.43, 39.72, 40.94, 40.35, 40.49, 39.74, 42.24, 39.59]
-[125.13]
-15.676214456558228
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4895, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.465762615203857, 'TIME_S_1KI': 2.3423416987137604, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1961.564714949131, 'W': 125.13}
-[40.6, 45.25, 40.43, 39.72, 40.94, 40.35, 40.49, 39.74, 42.24, 39.59, 41.13, 40.62, 40.17, 40.53, 40.27, 40.01, 39.62, 39.57, 39.92, 39.5]
-730.2800000000001
-36.514
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4895, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.465762615203857, 'TIME_S_1KI': 2.3423416987137604, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1961.564714949131, 'W': 125.13, 'J_1KI': 400.72823594466416, 'W_1KI': 25.562819203268642, 'W_D': 88.61599999999999, 'J_D': 1389.1634202823636, 'W_D_1KI': 18.10337078651685, 'J_D_1KI': 3.698339282230205}
+[41.69, 40.21, 40.3, 40.12, 39.85, 39.74, 39.78, 45.25, 40.47, 39.68]
+[126.08]
+15.802239418029785
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 5118, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 12.526283264160156, 'TIME_S_1KI': 2.4474957530598194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1992.3463458251954, 'W': 126.08}
+[41.69, 40.21, 40.3, 40.12, 39.85, 39.74, 39.78, 45.25, 40.47, 39.68, 40.53, 45.71, 40.42, 40.02, 40.48, 39.61, 40.13, 39.7, 39.72, 39.64]
+732.28
+36.614
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 5118, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 12.526283264160156, 'TIME_S_1KI': 2.4474957530598194, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1992.3463458251954, 'W': 126.08, 'J_1KI': 389.28220903188657, 'W_1KI': 24.634622899570147, 'W_D': 89.46600000000001, 'J_D': 1413.7631517734528, 'W_D_1KI': 17.480656506447833, 'J_D_1KI': 3.415524913334864}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.json
index 0c05e29..ae8bf07 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2082, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.557694435119629, "TIME_S_1KI": 5.070938729644394, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1859.5419220256806, "W": 120.04, "J_1KI": 893.1517396857256, "W_1KI": 57.656099903938525, "W_D": 83.7395, "J_D": 1297.2101864334345, "W_D_1KI": 40.22070124879924, "J_D_1KI": 19.31830031162307}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2093, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.486551761627197, "TIME_S_1KI": 5.010297067189296, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1924.8615674543382, "W": 120.01, "J_1KI": 919.6663007426365, "W_1KI": 57.33874820831343, "W_D": 72.8605, "J_D": 1168.62241676116, "W_D_1KI": 34.81151457238414, "J_D_1KI": 16.632352877393284}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.output
index 68bbca5..616267b 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.5041141510009766}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.5015337467193604}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2037,     4062,  ..., 19995956,
-                            19997954, 20000000]),
-       col_indices=tensor([   0,    3,    5,  ..., 9996, 9997, 9998]),
-       values=tensor([0.3088, 0.0777, 0.1762,  ..., 0.6057, 0.6562, 0.8467]),
+tensor(crow_indices=tensor([       0,     2065,     4088,  ..., 19995990,
+                            19997962, 20000000]),
+       col_indices=tensor([   0,    2,    8,  ..., 9965, 9993, 9996]),
+       values=tensor([0.1160, 0.3460, 0.9869,  ..., 0.8626, 0.3191, 0.7812]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.5661, 0.3739, 0.3594,  ..., 0.8068, 0.7143, 0.9609])
+tensor([0.3538, 0.1952, 0.7791,  ..., 0.6959, 0.6589, 0.4364])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 0.5041141510009766 seconds
+Time: 0.5015337467193604 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2082', '-ss', '10000', '-sd', '0.2']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.557694435119629}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2093', '-ss', '10000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.486551761627197}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1900,     3860,  ..., 19996021,
-                            19998015, 20000000]),
-       col_indices=tensor([   0,    3,   11,  ..., 9989, 9992, 9996]),
-       values=tensor([0.1071, 0.4523, 0.1080,  ..., 0.2881, 0.4034, 0.8495]),
+tensor(crow_indices=tensor([       0,     2091,     4151,  ..., 19995949,
+                            19997994, 20000000]),
+       col_indices=tensor([   0,    6,   11,  ..., 9981, 9985, 9986]),
+       values=tensor([0.3936, 0.6857, 0.7214,  ..., 0.8889, 0.2946, 0.7271]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.3748, 0.6654, 0.9133,  ..., 0.7126, 0.6760, 0.9288])
+tensor([0.6947, 0.8976, 0.6132,  ..., 0.3430, 0.6838, 0.4582])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 10.557694435119629 seconds
+Time: 10.486551761627197 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1900,     3860,  ..., 19996021,
-                            19998015, 20000000]),
-       col_indices=tensor([   0,    3,   11,  ..., 9989, 9992, 9996]),
-       values=tensor([0.1071, 0.4523, 0.1080,  ..., 0.2881, 0.4034, 0.8495]),
+tensor(crow_indices=tensor([       0,     2091,     4151,  ..., 19995949,
+                            19997994, 20000000]),
+       col_indices=tensor([   0,    6,   11,  ..., 9981, 9985, 9986]),
+       values=tensor([0.3936, 0.6857, 0.7214,  ..., 0.8889, 0.2946, 0.7271]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.3748, 0.6654, 0.9133,  ..., 0.7126, 0.6760, 0.9288])
+tensor([0.6947, 0.8976, 0.6132,  ..., 0.3430, 0.6838, 0.4582])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 10.557694435119629 seconds
+Time: 10.486551761627197 seconds
 
-[40.83, 40.15, 39.72, 39.76, 39.84, 39.84, 40.49, 39.96, 40.13, 39.73]
-[120.04]
-15.491019010543823
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2082, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.557694435119629, 'TIME_S_1KI': 5.070938729644394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1859.5419220256806, 'W': 120.04}
-[40.83, 40.15, 39.72, 39.76, 39.84, 39.84, 40.49, 39.96, 40.13, 39.73, 41.31, 40.05, 40.38, 40.21, 39.71, 39.65, 45.13, 39.78, 40.38, 39.79]
-726.01
-36.3005
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2082, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.557694435119629, 'TIME_S_1KI': 5.070938729644394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1859.5419220256806, 'W': 120.04, 'J_1KI': 893.1517396857256, 'W_1KI': 57.656099903938525, 'W_D': 83.7395, 'J_D': 1297.2101864334345, 'W_D_1KI': 40.22070124879924, 'J_D_1KI': 19.31830031162307}
+[40.85, 39.59, 39.61, 40.83, 40.02, 40.07, 39.94, 39.43, 39.49, 39.51]
+[120.01]
+16.03917646408081
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2093, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.486551761627197, 'TIME_S_1KI': 5.010297067189296, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1924.8615674543382, 'W': 120.01}
+[40.85, 39.59, 39.61, 40.83, 40.02, 40.07, 39.94, 39.43, 39.49, 39.51, 72.32, 77.64, 65.79, 65.84, 68.15, 63.72, 61.71, 61.72, 62.84, 40.52]
+942.9899999999999
+47.149499999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2093, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.486551761627197, 'TIME_S_1KI': 5.010297067189296, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1924.8615674543382, 'W': 120.01, 'J_1KI': 919.6663007426365, 'W_1KI': 57.33874820831343, 'W_D': 72.8605, 'J_D': 1168.62241676116, 'W_D_1KI': 34.81151457238414, 'J_D_1KI': 16.632352877393284}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.json
index b94bf6c..7250125 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1470, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.845632314682007, "TIME_S_1KI": 7.377981166450344, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2161.157882115841, "W": 117.03, "J_1KI": 1470.1754300107762, "W_1KI": 79.61224489795917, "W_D": 80.6345, "J_D": 1489.0531081386805, "W_D_1KI": 54.853401360544225, "J_D_1KI": 37.315239020778385}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1381, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.009110450744629, "TIME_S_1KI": 7.247726611690536, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2082.98861492157, "W": 115.78, "J_1KI": 1508.3190549757928, "W_1KI": 83.83779869659668, "W_D": 79.56150000000001, "J_D": 1431.3845110216143, "W_D_1KI": 57.6115133960898, "J_D_1KI": 41.71724358876886}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.output
index 4e7bca2..6cad927 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.7518825531005859}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 2.4347083568573}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2974,     5950,  ..., 29994087,
-                            29997108, 30000000]),
-       col_indices=tensor([   6,    8,   10,  ..., 9985, 9992, 9996]),
-       values=tensor([0.7151, 0.6737, 0.4043,  ..., 0.5812, 0.5679, 0.6733]),
+tensor(crow_indices=tensor([       0,     2970,     6030,  ..., 29993988,
+                            29996992, 30000000]),
+       col_indices=tensor([   5,    8,   12,  ..., 9995, 9996, 9999]),
+       values=tensor([0.4708, 0.4086, 0.5052,  ..., 0.9718, 0.1326, 0.4183]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.6135, 0.6008, 0.5882,  ..., 0.6628, 0.8539, 0.9204])
+tensor([0.4875, 0.8736, 0.6610,  ..., 0.0907, 0.9780, 0.5227])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 0.7518825531005859 seconds
+Time: 2.4347083568573 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1396', '-ss', '10000', '-sd', '0.3']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.968559503555298}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '431', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 8.85914397239685}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     3001,     6027,  ..., 29994039,
-                            29997052, 30000000]),
-       col_indices=tensor([   7,   11,   16,  ..., 9989, 9996, 9999]),
-       values=tensor([0.2908, 0.3192, 0.9662,  ..., 0.5726, 0.8523, 0.1200]),
+tensor(crow_indices=tensor([       0,     2975,     5968,  ..., 29993983,
+                            29997045, 30000000]),
+       col_indices=tensor([   1,    6,   11,  ..., 9991, 9994, 9995]),
+       values=tensor([0.5155, 0.2760, 0.9456,  ..., 0.6815, 0.0937, 0.4855]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.6009, 0.9845, 0.3791,  ..., 0.1987, 0.1714, 0.4278])
+tensor([0.7624, 0.6706, 0.0728,  ..., 0.7684, 0.7487, 0.4885])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 9.968559503555298 seconds
+Time: 8.85914397239685 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1470', '-ss', '10000', '-sd', '0.3']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.845632314682007}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '510', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 3.8749990463256836}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2929,     5851,  ..., 29993945,
-                            29997029, 30000000]),
-       col_indices=tensor([   6,    7,   11,  ..., 9986, 9997, 9998]),
-       values=tensor([0.6210, 0.5427, 0.8130,  ..., 0.4194, 0.0441, 0.7442]),
+tensor(crow_indices=tensor([       0,     3028,     6043,  ..., 29994061,
+                            29997045, 30000000]),
+       col_indices=tensor([   2,    7,   10,  ..., 9994, 9997, 9998]),
+       values=tensor([0.0847, 0.6146, 0.4143,  ..., 0.8875, 0.3135, 0.7316]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.0141, 0.0033, 0.5199,  ..., 0.4699, 0.7276, 0.5761])
+tensor([0.4991, 0.4556, 0.0586,  ..., 0.4688, 0.0146, 0.1894])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 10.845632314682007 seconds
+Time: 3.8749990463256836 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1381', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.009110450744629}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2929,     5851,  ..., 29993945,
-                            29997029, 30000000]),
-       col_indices=tensor([   6,    7,   11,  ..., 9986, 9997, 9998]),
-       values=tensor([0.6210, 0.5427, 0.8130,  ..., 0.4194, 0.0441, 0.7442]),
+tensor(crow_indices=tensor([       0,     2980,     6008,  ..., 29994009,
+                            29997042, 30000000]),
+       col_indices=tensor([   6,   10,   12,  ..., 9993, 9994, 9997]),
+       values=tensor([0.7122, 0.9908, 0.6087,  ..., 0.3080, 0.7079, 0.9704]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.0141, 0.0033, 0.5199,  ..., 0.4699, 0.7276, 0.5761])
+tensor([0.8824, 0.6919, 0.2244,  ..., 0.2825, 0.9269, 0.1789])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +76,30 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 10.845632314682007 seconds
+Time: 10.009110450744629 seconds
 
-[46.28, 39.97, 40.2, 40.69, 40.27, 40.47, 40.33, 41.03, 39.84, 40.37]
-[117.03]
-18.466699838638306
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1470, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.845632314682007, 'TIME_S_1KI': 7.377981166450344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2161.157882115841, 'W': 117.03}
-[46.28, 39.97, 40.2, 40.69, 40.27, 40.47, 40.33, 41.03, 39.84, 40.37, 41.81, 40.41, 40.59, 39.82, 40.04, 39.71, 39.78, 40.7, 39.78, 40.1]
-727.9100000000001
-36.395500000000006
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1470, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.845632314682007, 'TIME_S_1KI': 7.377981166450344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2161.157882115841, 'W': 117.03, 'J_1KI': 1470.1754300107762, 'W_1KI': 79.61224489795917, 'W_D': 80.6345, 'J_D': 1489.0531081386805, 'W_D_1KI': 54.853401360544225, 'J_D_1KI': 37.315239020778385}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2980,     6008,  ..., 29994009,
+                            29997042, 30000000]),
+       col_indices=tensor([   6,   10,   12,  ..., 9993, 9994, 9997]),
+       values=tensor([0.7122, 0.9908, 0.6087,  ..., 0.3080, 0.7079, 0.9704]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.8824, 0.6919, 0.2244,  ..., 0.2825, 0.9269, 0.1789])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.009110450744629 seconds
+
+[43.41, 40.29, 39.95, 39.78, 39.85, 40.23, 40.43, 39.73, 41.31, 41.06]
+[115.78]
+17.99091911315918
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1381, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.009110450744629, 'TIME_S_1KI': 7.247726611690536, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2082.98861492157, 'W': 115.78}
+[43.41, 40.29, 39.95, 39.78, 39.85, 40.23, 40.43, 39.73, 41.31, 41.06, 41.55, 39.88, 39.85, 39.74, 39.98, 39.72, 40.61, 40.3, 39.85, 39.72]
+724.3699999999999
+36.21849999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1381, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.009110450744629, 'TIME_S_1KI': 7.247726611690536, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2082.98861492157, 'W': 115.78, 'J_1KI': 1508.3190549757928, 'W_1KI': 83.83779869659668, 'W_D': 79.56150000000001, 'J_D': 1431.3845110216143, 'W_D_1KI': 57.6115133960898, 'J_D_1KI': 41.71724358876886}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..46ccf1c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1065, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.481358289718628, "TIME_S_1KI": 9.841650976261622, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2347.078887729645, "W": 111.31000000000002, "J_1KI": 2203.829941530183, "W_1KI": 104.51643192488265, "W_D": 75.20475000000002, "J_D": 1585.7648098282818, "W_D_1KI": 70.61478873239437, "J_D_1KI": 66.30496594591021}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..e35280e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.4.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.9852290153503418}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3986,     7986,  ..., 39992018,
+                            39996031, 40000000]),
+       col_indices=tensor([   5,    6,    9,  ..., 9992, 9994, 9996]),
+       values=tensor([0.6541, 0.7210, 0.2819,  ..., 0.6753, 0.1107, 0.4939]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.0543, 0.0386, 0.2201,  ..., 0.7560, 0.6831, 0.4533])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 0.9852290153503418 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1065', '-ss', '10000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.481358289718628}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3991,     7976,  ..., 39991944,
+                            39995981, 40000000]),
+       col_indices=tensor([   3,    5,    7,  ..., 9995, 9997, 9998]),
+       values=tensor([0.0964, 0.7406, 0.8990,  ..., 0.2597, 0.1012, 0.1175]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.6206, 0.0983, 0.0503,  ..., 0.3920, 0.3679, 0.5560])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 10.481358289718628 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3991,     7976,  ..., 39991944,
+                            39995981, 40000000]),
+       col_indices=tensor([   3,    5,    7,  ..., 9995, 9997, 9998]),
+       values=tensor([0.0964, 0.7406, 0.8990,  ..., 0.2597, 0.1012, 0.1175]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.6206, 0.0983, 0.0503,  ..., 0.3920, 0.3679, 0.5560])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 10.481358289718628 seconds
+
+[40.72, 39.99, 40.29, 39.74, 40.59, 40.22, 39.97, 39.63, 41.44, 39.71]
+[111.31]
+21.085966110229492
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1065, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.481358289718628, 'TIME_S_1KI': 9.841650976261622, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2347.078887729645, 'W': 111.31000000000002}
+[40.72, 39.99, 40.29, 39.74, 40.59, 40.22, 39.97, 39.63, 41.44, 39.71, 40.5, 39.72, 40.2, 39.73, 39.88, 39.9, 40.11, 40.46, 39.97, 39.6]
+722.105
+36.10525
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1065, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.481358289718628, 'TIME_S_1KI': 9.841650976261622, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2347.078887729645, 'W': 111.31000000000002, 'J_1KI': 2203.829941530183, 'W_1KI': 104.51643192488265, 'W_D': 75.20475000000002, 'J_D': 1585.7648098282818, 'W_D_1KI': 70.61478873239437, 'J_D_1KI': 66.30496594591021}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..44f00fc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 959, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.287067413330078, "TIME_S_1KI": 10.72686904413981, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2562.4177262830735, "W": 110.66000000000001, "J_1KI": 2671.9684319948633, "W_1KI": 115.3910323253389, "W_D": 74.4205, "J_D": 1723.2641279491188, "W_D_1KI": 77.6021897810219, "J_D_1KI": 80.91990592390188}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..3d7d465
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.5.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 1.0947494506835938}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4881,     9921,  ..., 49990115,
+                            49995061, 50000000]),
+       col_indices=tensor([   4,    7,    9,  ..., 9993, 9997, 9999]),
+       values=tensor([0.4030, 0.2804, 0.0595,  ..., 0.7877, 0.0532, 0.8389]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.3752, 0.3058, 0.8535,  ..., 0.2048, 0.2544, 0.0959])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 1.0947494506835938 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '959', '-ss', '10000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.287067413330078}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4969,     9907,  ..., 49989771,
+                            49994882, 50000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9995, 9997, 9999]),
+       values=tensor([0.7090, 0.6777, 0.6702,  ..., 0.9888, 0.6596, 0.6666]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.5679, 0.7359, 0.9695,  ..., 0.6090, 0.0343, 0.3884])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 10.287067413330078 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4969,     9907,  ..., 49989771,
+                            49994882, 50000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9995, 9997, 9999]),
+       values=tensor([0.7090, 0.6777, 0.6702,  ..., 0.9888, 0.6596, 0.6666]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.5679, 0.7359, 0.9695,  ..., 0.6090, 0.0343, 0.3884])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 10.287067413330078 seconds
+
+[40.95, 40.36, 40.36, 39.82, 40.17, 39.71, 39.88, 39.61, 39.9, 40.59]
+[110.66]
+23.1557719707489
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 959, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.287067413330078, 'TIME_S_1KI': 10.72686904413981, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2562.4177262830735, 'W': 110.66000000000001}
+[40.95, 40.36, 40.36, 39.82, 40.17, 39.71, 39.88, 39.61, 39.9, 40.59, 40.84, 40.11, 40.32, 45.02, 39.83, 39.6, 39.79, 39.52, 39.81, 39.58]
+724.7900000000001
+36.23950000000001
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 959, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.287067413330078, 'TIME_S_1KI': 10.72686904413981, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2562.4177262830735, 'W': 110.66000000000001, 'J_1KI': 2671.9684319948633, 'W_1KI': 115.3910323253389, 'W_D': 74.4205, 'J_D': 1723.2641279491188, 'W_D_1KI': 77.6021897810219, 'J_D_1KI': 80.91990592390188}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json
index b4516b2..11ac4bd 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 366482, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.093939065933228, "TIME_S_1KI": 0.0302714432521467, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1264.2006668257713, "W": 98.79, "J_1KI": 3.4495573229402026, "W_1KI": 0.26956303447372587, "W_D": 62.66125, "J_D": 801.866525297463, "W_D_1KI": 0.17098043014390885, "J_D_1KI": 0.0004665452331735497}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 349528, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.3000328540802, "TIME_S_1KI": 0.029468405547138424, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1246.5216499328615, "W": 97.68, "J_1KI": 3.566299838447453, "W_1KI": 0.27946258954933517, "W_D": 61.658750000000005, "J_D": 786.844459283352, "W_D_1KI": 0.17640575290105515, "J_D_1KI": 0.0005046970568911651}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output
index b43f8ba..270e7ce 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output
@@ -1,373 +1,266 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.019162416458129883}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.020120859146118164}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
-       col_indices=tensor([2508, 9046,  543, 4312, 1967, 3077, 2314, 3402, 8716,
-                           3244, 7898, 5856, 5314, 5976, 2342, 2492, 8104, 2147,
-                           1979, 1751, 5860, 9783, 5705, 5222, 9824,   95, 1929,
-                           8803, 8548,  677, 1693, 5788, 5523, 9109, 5789, 1810,
-                           1126, 4820, 9808, 8419, 2599, 1229, 2963, 1212, 2144,
-                           3318, 3062, 2566, 7505, 6545, 1590, 1285,  717, 2005,
-                           5957, 3726, 9706,  857, 1431, 3655, 7849, 8981, 2848,
-                           7191, 9483, 8286, 6981,  629,  346, 7910, 8938, 1876,
-                           6475,  566, 6786, 4287, 8002, 5447, 9857, 7286, 3753,
-                           6095, 8322, 7342, 5732, 7297, 8559, 2193, 1470, 9896,
-                           4441, 3719, 1687, 3912, 4005, 7511, 4689, 9996, 5681,
-                           3721, 5011, 1299,  193, 7161, 1139, 8246, 6024, 9167,
-                           5912, 3347, 8644, 5448, 9677, 2358, 2275, 4573, 5804,
-                           6640,  677, 4332, 7242, 5152, 3155, 2691, 5318, 3888,
-                           7771, 2370, 9442, 9350, 2217, 3939, 1845, 8407, 8949,
-                           3238, 6161, 7398,  563, 6153, 7042, 5316, 6185, 8259,
-                           7444, 4523, 6325, 9519, 7963, 8735, 3571, 4162, 6945,
-                           7239, 9555, 8362, 6689,  825, 2005, 7392, 6469, 6550,
-                           1091, 7670, 3483, 3477, 6190, 5573, 9375, 3852,  897,
-                           8739, 4969, 3239, 4179, 9098, 2727, 7551, 1657, 3410,
-                           9763, 2836, 8969, 3741, 1805,  806, 7323, 1341, 9366,
-                           4238, 7055, 1855, 6557, 2199, 3076,  961,   56, 4684,
-                           8459, 6449,  916, 1241, 3555, 6490, 6076, 4659, 6608,
-                           1972, 7464, 3684, 7276,  202, 4934, 2937, 6629, 8676,
-                           2854, 9198, 3221, 1881, 9527, 3491,  900,  551, 3477,
-                           7557, 5144, 2172, 5733, 2720, 3420, 7237, 7166, 9810,
-                            146, 2108, 2851,  228,  520, 2516,  469,  991, 7850,
-                           1010, 9739, 3913, 4444, 2689, 1467,  901, 7088,  235,
-                           7464, 8041, 9413, 9292, 9837, 5114,   68,  289, 3415,
-                           4247, 7541, 9998, 3514, 9674, 2670, 3572, 2167, 8523,
-                           1517, 6861,  179, 4531, 7528, 4118, 3477, 1329, 9307,
-                           6276,  937, 7241, 4841, 2423, 4555, 3917, 1683, 3585,
-                           9720, 9352, 5341, 6902, 9150, 1840, 8548, 9758, 2673,
-                            665, 8946, 4675, 2273, 3021, 7214, 5707, 6328, 3736,
-                           1078, 7132, 2785, 1506, 6694, 7725, 9559,  241, 6974,
-                            365, 3095, 3437, 5609, 2248, 2372, 9786, 6037, 6108,
-                           7705, 1458, 6266,  400, 8324, 6775, 3201, 1369, 6843,
-                           7707, 9241, 1615, 9217, 1863, 2989, 2014, 2316, 4769,
-                           5768, 5885, 5440, 3265, 6889, 6583, 7730,  647, 9106,
-                           4087, 6087, 4403, 9274, 4471, 7401, 2762, 3931, 6965,
-                           3164,  938,  527, 5512, 7910, 4668, 5448, 6709, 6557,
-                           7876, 2822, 1072, 7234, 7990,  680, 6351,  365, 8734,
-                           3355, 2213, 5195, 7229, 9067, 2511, 3762, 8881, 3507,
-                           8462,  498, 1315, 6134, 3211, 3461, 4219, 3233, 6852,
-                           4989,  108, 8007, 9484, 5952, 2542, 8868,  678, 6135,
-                           2042, 6083, 8113, 8376, 7391,  510, 8516,  672, 6513,
-                           3857, 4529, 1142, 2241, 2953, 8752, 7337,  485, 9766,
-                           7738, 1630, 4671, 1054, 3709, 3629, 1425, 6145, 3112,
-                           1557, 5971, 4707, 2420, 7428, 3073, 7043, 4518, 3258,
-                           8324, 4576, 9820, 1738, 4288, 4568, 5120, 2665, 5670,
-                           1160, 3431, 2936, 6123, 3465, 6501, 3805, 5189, 2086,
-                           6398, 7063, 7290, 1186, 1870, 6160, 4041, 9566, 9903,
-                           8671, 3136,  161, 9574,  903, 9086, 6744, 9121,  576,
-                           5200, 5674, 3097, 5935, 1757, 3232, 3580, 6609, 4322,
-                           3945, 8444, 3852, 1569, 5504, 6603, 1547,  878, 7861,
-                           8802, 3561, 6575, 4591,  301, 5462, 5293, 1550, 7795,
-                           7430, 4734,  185, 8890, 5875, 1566, 7507, 6627, 9171,
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-                           9024, 5315, 5066, 2631, 6631, 4897, 3673,  328,  228,
-                           3166, 2519, 3745, 6596, 6669, 3088, 9711, 8165,  899,
-                           9589,   36, 3957,  496,  115, 6166, 3688, 1766, 2307,
-                            361, 8690, 3683, 5585, 8112, 1232, 4580, 3944, 1072,
-                           3051, 3242, 8587, 3766, 5601,  892,  958, 4286, 1119,
-                           8408, 8953, 1339, 9703, 8899, 4918, 8984,   90, 9279,
-                           2372, 4341, 1706, 6214, 6370, 5149, 5092, 1777, 9441,
-                           9154,   96, 8248, 9257,  324, 4153, 3647, 3530, 8044,
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-                           5900, 5405, 9630, 5275,  397,   43, 1118, 3111, 7822,
-                           1550, 6255, 9674, 8999, 4005,  803, 8997, 3496, 9830,
-                           8865, 2267, 8584, 1911, 3802, 5884, 3569, 5747, 6116,
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-                           5244, 8142, 3664, 9159, 2387, 8232, 3360, 3745, 9932,
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        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4331, 0.6450, 0.9910,  ..., 0.2595, 0.5081, 0.5389])
+tensor([0.6468, 0.9076, 0.6761,  ..., 0.7586, 0.6739, 0.5784])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -375,378 +268,271 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.019162416458129883 seconds
+Time: 0.020120859146118164 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '54794', '-ss', '10000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.6940038204193115}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52184', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.5676329135894775}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.2228, 0.3385, 0.5201, 0.1909, 0.4456, 0.3074, 0.5579,
+                      0.6458, 0.4935, 0.8701, 0.1571, 0.2568, 0.9694, 0.3103,
+                      0.9109, 0.1790, 0.3725, 0.9182, 0.0234, 0.2294, 0.3142,
+                      0.7794, 0.8060, 0.6996, 0.6655, 0.9955, 0.7639, 0.7232,
+                      0.0896, 0.2239, 0.4701, 0.0235, 0.3167, 0.6004]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.8612, 0.4581, 0.7630,  ..., 0.0492, 0.2811, 0.3451])
+tensor([0.9688, 0.6616, 0.6383,  ..., 0.6766, 0.6207, 0.5367])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -754,271 +540,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 1.6940038204193115 seconds
+Time: 1.5676329135894775 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '339631', '-ss', '10000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.730679035186768}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '349528', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.3000328540802}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 1000, 1000, 1000]),
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+                      2.9276e-01, 1.8871e-01, 8.4606e-01, 6.1973e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4250, 0.1408, 0.2235,  ..., 0.5768, 0.6841, 0.8384])
+tensor([0.7175, 0.8461, 0.0931,  ..., 0.1554, 0.1137, 0.6137])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1026,378 +919,375 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 9.730679035186768 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '366482', '-ss', '10000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.093939065933228}
+Time: 10.3000328540802 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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-                           2915, 7211, 1515, 5525, 1719, 3439, 4040, 8857, 5364,
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+                      4.9750e-01, 6.9949e-01, 1.8679e-01, 4.7306e-01,
+                      6.2149e-01, 6.3191e-01, 6.8227e-01, 9.5340e-01,
+                      6.9214e-01, 4.8327e-01, 6.2025e-01, 7.2734e-01,
+                      3.4948e-01, 9.1951e-01, 1.5345e-01, 9.4844e-01,
+                      7.9645e-01, 4.2942e-01, 3.0837e-01, 3.2774e-01,
+                      4.7750e-01, 3.2074e-01, 2.3827e-01, 9.4556e-01,
+                      8.5400e-01, 4.2681e-02, 9.5386e-01, 3.3202e-01,
+                      2.9276e-01, 1.8871e-01, 8.4606e-01, 6.1973e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.7740, 0.9703, 0.1840,  ..., 0.7477, 0.1526, 0.5369])
+tensor([0.7175, 0.8461, 0.0931,  ..., 0.1554, 0.1137, 0.6137])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1405,389 +1295,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 11.093939065933228 seconds
+Time: 10.3000328540802 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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-       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.7740, 0.9703, 0.1840,  ..., 0.7477, 0.1526, 0.5369])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 1000
-Density: 1e-05
-Time: 11.093939065933228 seconds
-
-[40.33, 40.0, 39.88, 40.14, 39.62, 39.53, 39.86, 39.53, 39.55, 39.82]
-[98.79]
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-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 366482, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.093939065933228, 'TIME_S_1KI': 0.0302714432521467, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1264.2006668257713, 'W': 98.79}
-[40.33, 40.0, 39.88, 40.14, 39.62, 39.53, 39.86, 39.53, 39.55, 39.82, 40.52, 39.54, 40.01, 39.91, 40.14, 39.87, 40.07, 39.9, 44.97, 39.44]
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-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 366482, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.093939065933228, 'TIME_S_1KI': 0.0302714432521467, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1264.2006668257713, 'W': 98.79, 'J_1KI': 3.4495573229402026, 'W_1KI': 0.26956303447372587, 'W_D': 62.66125, 'J_D': 801.866525297463, 'W_D_1KI': 0.17098043014390885, 'J_D_1KI': 0.0004665452331735497}
+[46.41, 39.72, 39.82, 39.5, 39.71, 39.86, 41.37, 39.83, 39.54, 39.79]
+[97.68]
+12.76127815246582
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 349528, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.3000328540802, 'TIME_S_1KI': 0.029468405547138424, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1246.5216499328615, 'W': 97.68}
+[46.41, 39.72, 39.82, 39.5, 39.71, 39.86, 41.37, 39.83, 39.54, 39.79, 40.31, 39.5, 40.19, 39.88, 40.21, 39.51, 39.5, 39.4, 39.78, 39.7]
+720.4250000000001
+36.02125
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 349528, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.3000328540802, 'TIME_S_1KI': 0.029468405547138424, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1246.5216499328615, 'W': 97.68, 'J_1KI': 3.566299838447453, 'W_1KI': 0.27946258954933517, 'W_D': 61.658750000000005, 'J_D': 786.844459283352, 'W_D_1KI': 0.17640575290105515, 'J_D_1KI': 0.0005046970568911651}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.json
index c87bd7a..0f29e81 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 305580, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.080953121185303, "TIME_S_1KI": 0.032989571049104334, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1230.7398206591606, "W": 98.25, "J_1KI": 4.027553572416914, "W_1KI": 0.32151973296681724, "W_D": 62.38325, "J_D": 781.450889741838, "W_D_1KI": 0.20414703187381372, "J_D_1KI": 0.0006680641137306555}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 316836, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.895925283432007, "TIME_S_1KI": 0.03438979561486702, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1245.413042907715, "W": 97.92, "J_1KI": 3.9307813597814483, "W_1KI": 0.3090557891148733, "W_D": 62.073, "J_D": 789.4865585417748, "W_D_1KI": 0.19591523690489715, "J_D_1KI": 0.0006183490414753915}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.output
index 9e7563d..5bc678d 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.018548250198364258}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.01957559585571289}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    2,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([1572, 8127, 3303,  ..., 3635, 8012, 8701]),
-       values=tensor([0.7029, 0.2681, 0.5472,  ..., 0.1372, 0.6564, 0.9870]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 4998, 4999, 5000]),
+       col_indices=tensor([1720, 7937, 2049,  ..., 9577, 9983, 1353]),
+       values=tensor([0.0345, 0.4506, 0.1189,  ..., 0.6520, 0.2135, 0.0491]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.6228, 0.5154, 0.0077,  ..., 0.6369, 0.2601, 0.0192])
+tensor([0.9951, 0.3552, 0.9985,  ..., 0.5681, 0.6856, 0.6031])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 0.018548250198364258 seconds
+Time: 0.01957559585571289 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '56609', '-ss', '10000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.9451327323913574}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '53638', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.7775709629058838}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 4999, 5000]),
-       col_indices=tensor([3842,  387, 3686,  ..., 4115, 6419, 2917]),
-       values=tensor([0.9231, 0.0215, 0.0697,  ..., 0.2708, 0.2879, 0.7516]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 4996, 4998, 5000]),
+       col_indices=tensor([4959,  918, 4053,  ..., 9398,   67, 9521]),
+       values=tensor([0.3778, 0.3237, 0.9504,  ..., 0.9486, 0.3785, 0.3072]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.2403, 0.0214, 0.1380,  ..., 0.6094, 0.2095, 0.9923])
+tensor([0.9157, 0.0973, 0.6254,  ..., 0.8224, 0.9770, 0.1355])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 1.9451327323913574 seconds
+Time: 1.7775709629058838 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '305580', '-ss', '10000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.080953121185303}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '316836', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.895925283432007}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    2,  ..., 4999, 4999, 5000]),
-       col_indices=tensor([1481, 9557, 9045,  ...,  186, 1024,  519]),
-       values=tensor([0.0681, 0.8562, 0.9064,  ..., 0.7770, 0.2010, 0.9088]),
+tensor(crow_indices=tensor([   0,    0,    3,  ..., 4997, 4997, 5000]),
+       col_indices=tensor([2376, 3852, 7380,  ...,  878, 7696, 9514]),
+       values=tensor([0.3306, 0.8481, 0.4988,  ..., 0.7577, 0.2140, 0.3496]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.5005, 0.6529, 0.0782,  ..., 0.6202, 0.1736, 0.9901])
+tensor([0.0988, 0.8059, 0.5662,  ..., 0.3195, 0.3665, 0.4368])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.080953121185303 seconds
+Time: 10.895925283432007 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    2,  ..., 4999, 4999, 5000]),
-       col_indices=tensor([1481, 9557, 9045,  ...,  186, 1024,  519]),
-       values=tensor([0.0681, 0.8562, 0.9064,  ..., 0.7770, 0.2010, 0.9088]),
+tensor(crow_indices=tensor([   0,    0,    3,  ..., 4997, 4997, 5000]),
+       col_indices=tensor([2376, 3852, 7380,  ...,  878, 7696, 9514]),
+       values=tensor([0.3306, 0.8481, 0.4988,  ..., 0.7577, 0.2140, 0.3496]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.5005, 0.6529, 0.0782,  ..., 0.6202, 0.1736, 0.9901])
+tensor([0.0988, 0.8059, 0.5662,  ..., 0.3195, 0.3665, 0.4368])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.080953121185303 seconds
+Time: 10.895925283432007 seconds
 
-[40.69, 39.86, 40.65, 39.74, 39.56, 39.52, 39.78, 39.47, 39.45, 39.45]
-[98.25]
-12.52661395072937
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 305580, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.080953121185303, 'TIME_S_1KI': 0.032989571049104334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1230.7398206591606, 'W': 98.25}
-[40.69, 39.86, 40.65, 39.74, 39.56, 39.52, 39.78, 39.47, 39.45, 39.45, 40.13, 40.88, 39.46, 41.14, 39.99, 39.46, 39.46, 39.44, 39.59, 39.5]
-717.335
-35.86675
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 305580, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.080953121185303, 'TIME_S_1KI': 0.032989571049104334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1230.7398206591606, 'W': 98.25, 'J_1KI': 4.027553572416914, 'W_1KI': 0.32151973296681724, 'W_D': 62.38325, 'J_D': 781.450889741838, 'W_D_1KI': 0.20414703187381372, 'J_D_1KI': 0.0006680641137306555}
+[41.11, 39.47, 41.15, 40.05, 39.56, 39.44, 39.86, 39.66, 39.7, 39.8]
+[97.92]
+12.718678951263428
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 316836, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.895925283432007, 'TIME_S_1KI': 0.03438979561486702, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1245.413042907715, 'W': 97.92}
+[41.11, 39.47, 41.15, 40.05, 39.56, 39.44, 39.86, 39.66, 39.7, 39.8, 40.79, 39.83, 39.98, 39.4, 39.49, 39.43, 39.88, 39.48, 39.95, 39.52]
+716.94
+35.847
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 316836, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.895925283432007, 'TIME_S_1KI': 0.03438979561486702, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1245.413042907715, 'W': 97.92, 'J_1KI': 3.9307813597814483, 'W_1KI': 0.3090557891148733, 'W_D': 62.073, 'J_D': 789.4865585417748, 'W_D_1KI': 0.19591523690489715, 'J_D_1KI': 0.0006183490414753915}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.json
index 0a007ba..a1deb3a 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1273, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.290857076644897, "TIME_S_1KI": 8.083941144261507, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2248.191835269928, "W": 120.44000000000001, "J_1KI": 1766.0580009975868, "W_1KI": 94.61115475255303, "W_D": 84.17750000000001, "J_D": 1571.2983079826834, "W_D_1KI": 66.12529457973291, "J_D_1KI": 51.944457643152326}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1257, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.0600004196167, "TIME_S_1KI": 8.003182513617103, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2214.012642827034, "W": 119.14000000000001, "J_1KI": 1761.3465734503056, "W_1KI": 94.78122513922038, "W_D": 83.14675000000001, "J_D": 1545.1397994794252, "W_D_1KI": 66.14697692919651, "J_D_1KI": 52.622893340649576}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.output
index 1b9241c..fb8d5a0 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.824350118637085}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.8350811004638672}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       55,      100,  ..., 24999899,
-                            24999953, 25000000]),
-       col_indices=tensor([  1283,  31647,  40047,  ..., 487577, 491974,
-                           492635]),
-       values=tensor([0.2687, 0.0076, 0.0743,  ..., 0.5051, 0.4444, 0.1527]),
+tensor(crow_indices=tensor([       0,       56,       98,  ..., 24999894,
+                            24999946, 25000000]),
+       col_indices=tensor([ 21390,  30692,  42474,  ..., 464465, 482205,
+                           499540]),
+       values=tensor([0.3993, 0.3433, 0.4515,  ..., 0.0260, 0.6112, 0.7953]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.2422, 0.7782, 0.2817,  ..., 0.4809, 0.1219, 0.8722])
+tensor([0.3970, 0.7382, 0.9549,  ..., 0.7739, 0.4106, 0.9250])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 0.824350118637085 seconds
+Time: 0.8350811004638672 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1273', '-ss', '500000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.290857076644897}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1257', '-ss', '500000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.0600004196167}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       45,      101,  ..., 24999883,
-                            24999939, 25000000]),
-       col_indices=tensor([  9313,  23523,  43031,  ..., 488537, 498363,
-                           498593]),
-       values=tensor([0.9134, 0.4019, 0.3601,  ..., 0.2723, 0.3306, 0.0527]),
+tensor(crow_indices=tensor([       0,       45,       99,  ..., 24999907,
+                            24999959, 25000000]),
+       col_indices=tensor([  7707,  18388,  21123,  ..., 463120, 474527,
+                           488339]),
+       values=tensor([0.7704, 0.9806, 0.4404,  ..., 0.7048, 0.0947, 0.7119]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.9993, 0.9766, 0.6194,  ..., 0.0672, 0.4807, 0.1643])
+tensor([0.2271, 0.4870, 0.9892,  ..., 0.5903, 0.4477, 0.1543])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 10.290857076644897 seconds
+Time: 10.0600004196167 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       45,      101,  ..., 24999883,
-                            24999939, 25000000]),
-       col_indices=tensor([  9313,  23523,  43031,  ..., 488537, 498363,
-                           498593]),
-       values=tensor([0.9134, 0.4019, 0.3601,  ..., 0.2723, 0.3306, 0.0527]),
+tensor(crow_indices=tensor([       0,       45,       99,  ..., 24999907,
+                            24999959, 25000000]),
+       col_indices=tensor([  7707,  18388,  21123,  ..., 463120, 474527,
+                           488339]),
+       values=tensor([0.7704, 0.9806, 0.4404,  ..., 0.7048, 0.0947, 0.7119]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.9993, 0.9766, 0.6194,  ..., 0.0672, 0.4807, 0.1643])
+tensor([0.2271, 0.4870, 0.9892,  ..., 0.5903, 0.4477, 0.1543])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 10.290857076644897 seconds
+Time: 10.0600004196167 seconds
 
-[40.99, 40.04, 40.78, 39.98, 40.28, 39.98, 39.98, 39.94, 40.19, 39.9]
-[120.44]
-18.66648817062378
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1273, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.290857076644897, 'TIME_S_1KI': 8.083941144261507, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2248.191835269928, 'W': 120.44000000000001}
-[40.99, 40.04, 40.78, 39.98, 40.28, 39.98, 39.98, 39.94, 40.19, 39.9, 41.12, 40.02, 40.17, 39.62, 39.57, 39.49, 39.52, 45.4, 39.55, 39.47]
-725.25
-36.2625
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1273, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.290857076644897, 'TIME_S_1KI': 8.083941144261507, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2248.191835269928, 'W': 120.44000000000001, 'J_1KI': 1766.0580009975868, 'W_1KI': 94.61115475255303, 'W_D': 84.17750000000001, 'J_D': 1571.2983079826834, 'W_D_1KI': 66.12529457973291, 'J_D_1KI': 51.944457643152326}
+[40.4, 39.62, 39.65, 40.0, 39.69, 39.61, 40.44, 39.99, 40.11, 40.12]
+[119.14]
+18.583285570144653
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1257, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.0600004196167, 'TIME_S_1KI': 8.003182513617103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2214.012642827034, 'W': 119.14000000000001}
+[40.4, 39.62, 39.65, 40.0, 39.69, 39.61, 40.44, 39.99, 40.11, 40.12, 41.13, 40.27, 39.63, 40.2, 39.68, 40.25, 40.02, 40.19, 39.95, 39.48]
+719.865
+35.99325
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1257, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.0600004196167, 'TIME_S_1KI': 8.003182513617103, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2214.012642827034, 'W': 119.14000000000001, 'J_1KI': 1761.3465734503056, 'W_1KI': 94.78122513922038, 'W_D': 83.14675000000001, 'J_D': 1545.1397994794252, 'W_D_1KI': 66.14697692919651, 'J_D_1KI': 52.622893340649576}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json
index 22e8b8e..f01e1a9 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 21531, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.381713628768921, "TIME_S_1KI": 0.48217517202029264, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2048.753864378929, "W": 153.79, "J_1KI": 95.15367908499043, "W_1KI": 7.142724443825182, "W_D": 117.79275, "J_D": 1569.2070470012427, "W_D_1KI": 5.4708443639403646, "J_D_1KI": 0.25409151288562376}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 21626, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.539944410324097, "TIME_S_1KI": 0.4873737357959908, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2051.971848464012, "W": 154.45, "J_1KI": 94.88448388347416, "W_1KI": 7.141866272079903, "W_D": 118.43974999999999, "J_D": 1573.5515230761764, "W_D_1KI": 5.476729399796541, "J_D_1KI": 0.2532474521315334}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output
index da8bd5c..fa5a239 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08279204368591309}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.07795858383178711}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       5,      10,  ..., 2499988,
-                            2499995, 2500000]),
-       col_indices=tensor([111354, 133493, 148601,  ..., 214459, 291734,
-                           295580]),
-       values=tensor([0.7692, 0.5972, 0.6345,  ..., 0.2595, 0.9828, 0.2512]),
+tensor(crow_indices=tensor([      0,       2,       4,  ..., 2499985,
+                            2499990, 2500000]),
+       col_indices=tensor([187524, 340597,  50465,  ..., 396691, 431780,
+                           493873]),
+       values=tensor([0.8920, 0.9244, 0.9041,  ..., 0.0928, 0.3980, 0.2410]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1544, 0.9362, 0.6152,  ..., 0.8648, 0.4518, 0.0330])
+tensor([0.0594, 0.5344, 0.6686,  ..., 0.0375, 0.0136, 0.3063])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,41 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 0.08279204368591309 seconds
+Time: 0.07795858383178711 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12682', '-ss', '500000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.184589385986328}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '13468', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.538946628570557}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       6,      12,  ..., 2499985,
+tensor(crow_indices=tensor([      0,       8,       9,  ..., 2499991,
+                            2499997, 2500000]),
+       col_indices=tensor([ 29252,  48539, 177292,  ...,  25321, 399663,
+                           441240]),
+       values=tensor([0.5601, 0.9391, 0.4233,  ..., 0.6965, 0.5428, 0.9275]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4893, 0.1531, 0.9914,  ..., 0.7153, 0.0010, 0.4806])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 6.538946628570557 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21626', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.539944410324097}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,      10,  ..., 2499985,
                             2499992, 2500000]),
-       col_indices=tensor([135614, 168986, 215859,  ..., 402290, 443216,
-                           486549]),
-       values=tensor([0.4455, 0.2288, 0.5445,  ..., 0.6029, 0.8332, 0.9959]),
+       col_indices=tensor([ 98671, 180976, 345489,  ..., 382848, 425676,
+                           484883]),
+       values=tensor([0.5764, 0.7272, 0.4446,  ..., 0.6586, 0.9064, 0.2568]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.5969, 0.6605, 0.1157,  ..., 0.5750, 0.9019, 0.4949])
+tensor([0.7158, 0.9003, 0.8372,  ..., 0.1054, 0.6378, 0.8680])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,20 +59,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 6.184589385986328 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21531', '-ss', '500000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.381713628768921}
+Time: 10.539944410324097 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       4,       6,  ..., 2499988,
-                            2499995, 2500000]),
-       col_indices=tensor([192037, 290494, 298239,  ..., 203209, 269872,
-                           299833]),
-       values=tensor([0.2087, 0.5501, 0.3490,  ..., 0.1907, 0.4204, 0.3032]),
+tensor(crow_indices=tensor([      0,       4,      10,  ..., 2499985,
+                            2499992, 2500000]),
+       col_indices=tensor([ 98671, 180976, 345489,  ..., 382848, 425676,
+                           484883]),
+       values=tensor([0.5764, 0.7272, 0.4446,  ..., 0.6586, 0.9064, 0.2568]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3717, 0.6256, 0.6803,  ..., 0.1727, 0.9290, 0.7130])
+tensor([0.7158, 0.9003, 0.8372,  ..., 0.1054, 0.6378, 0.8680])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -59,31 +77,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.381713628768921 seconds
+Time: 10.539944410324097 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       4,       6,  ..., 2499988,
-                            2499995, 2500000]),
-       col_indices=tensor([192037, 290494, 298239,  ..., 203209, 269872,
-                           299833]),
-       values=tensor([0.2087, 0.5501, 0.3490,  ..., 0.1907, 0.4204, 0.3032]),
-       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3717, 0.6256, 0.6803,  ..., 0.1727, 0.9290, 0.7130])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 2500000
-Density: 1e-05
-Time: 10.381713628768921 seconds
-
-[40.84, 39.87, 40.71, 40.09, 39.75, 39.79, 39.67, 39.6, 39.76, 40.21]
-[153.79]
-13.321762561798096
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21531, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.381713628768921, 'TIME_S_1KI': 0.48217517202029264, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2048.753864378929, 'W': 153.79}
-[40.84, 39.87, 40.71, 40.09, 39.75, 39.79, 39.67, 39.6, 39.76, 40.21, 41.58, 40.07, 40.12, 40.08, 39.68, 39.76, 39.59, 39.74, 40.65, 39.4]
-719.9449999999999
-35.997249999999994
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21531, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.381713628768921, 'TIME_S_1KI': 0.48217517202029264, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2048.753864378929, 'W': 153.79, 'J_1KI': 95.15367908499043, 'W_1KI': 7.142724443825182, 'W_D': 117.79275, 'J_D': 1569.2070470012427, 'W_D_1KI': 5.4708443639403646, 'J_D_1KI': 0.25409151288562376}
+[40.43, 40.12, 40.51, 39.58, 39.78, 40.16, 39.92, 39.88, 39.89, 39.65]
+[154.45]
+13.285670757293701
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21626, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.539944410324097, 'TIME_S_1KI': 0.4873737357959908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2051.971848464012, 'W': 154.45}
+[40.43, 40.12, 40.51, 39.58, 39.78, 40.16, 39.92, 39.88, 39.89, 39.65, 40.17, 39.98, 41.76, 39.72, 40.03, 40.16, 39.55, 39.35, 39.92, 39.54]
+720.2049999999999
+36.01025
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21626, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.539944410324097, 'TIME_S_1KI': 0.4873737357959908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2051.971848464012, 'W': 154.45, 'J_1KI': 94.88448388347416, 'W_1KI': 7.141866272079903, 'W_D': 118.43974999999999, 'J_D': 1573.5515230761764, 'W_D_1KI': 5.476729399796541, 'J_D_1KI': 0.2532474521315334}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.json
index 2972d33..1829510 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2288, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.664037466049194, "TIME_S_1KI": 4.660855535860661, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2026.948439025879, "W": 124.48, "J_1KI": 885.9040380357864, "W_1KI": 54.40559440559441, "W_D": 87.53675000000001, "J_D": 1425.389450272322, "W_D_1KI": 38.25906905594406, "J_D_1KI": 16.721621090884643}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2237, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.043452024459839, "TIME_S_1KI": 4.489696926446061, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1969.1841315150261, "W": 123.27, "J_1KI": 880.2790038064488, "W_1KI": 55.10505140813589, "W_D": 87.08525, "J_D": 1391.1486362376809, "W_D_1KI": 38.92948144836835, "J_D_1KI": 17.402539762346155}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.output
index 16454d4..6208bf5 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.45888853073120117}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.4693167209625244}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       24,       46,  ..., 12499954,
-                            12499981, 12500000]),
-       col_indices=tensor([ 49072, 112972, 116125,  ..., 361100, 370525,
-                           412609]),
-       values=tensor([0.2354, 0.3643, 0.0075,  ..., 0.8603, 0.9033, 0.4787]),
+tensor(crow_indices=tensor([       0,       25,       50,  ..., 12499953,
+                            12499973, 12500000]),
+       col_indices=tensor([  4880,  16891,  23384,  ..., 468600, 493050,
+                           493066]),
+       values=tensor([0.6197, 0.4687, 0.7888,  ..., 0.4796, 0.1972, 0.6492]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.8534, 0.6179, 0.0838,  ..., 0.4832, 0.1451, 0.6650])
+tensor([0.1250, 0.0763, 0.3340,  ..., 0.1944, 0.6244, 0.7099])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 0.45888853073120117 seconds
+Time: 0.4693167209625244 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2288', '-ss', '500000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.664037466049194}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2237', '-ss', '500000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.043452024459839}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       27,       48,  ..., 12499952,
-                            12499972, 12500000]),
-       col_indices=tensor([  3842,   7633,   8971,  ..., 455163, 462741,
-                           476944]),
-       values=tensor([0.8075, 0.4724, 0.8976,  ..., 0.5541, 0.2969, 0.9431]),
+tensor(crow_indices=tensor([       0,       22,       43,  ..., 12499965,
+                            12499980, 12500000]),
+       col_indices=tensor([  4985,  20152,  29473,  ..., 475854, 476409,
+                           499068]),
+       values=tensor([0.8596, 0.1215, 0.4132,  ..., 0.1501, 0.7884, 0.5323]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.0930, 0.1654, 0.5776,  ..., 0.1397, 0.2168, 0.6873])
+tensor([0.6345, 0.7237, 0.9663,  ..., 0.0286, 0.9659, 0.0785])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 10.664037466049194 seconds
+Time: 10.043452024459839 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       27,       48,  ..., 12499952,
-                            12499972, 12500000]),
-       col_indices=tensor([  3842,   7633,   8971,  ..., 455163, 462741,
-                           476944]),
-       values=tensor([0.8075, 0.4724, 0.8976,  ..., 0.5541, 0.2969, 0.9431]),
+tensor(crow_indices=tensor([       0,       22,       43,  ..., 12499965,
+                            12499980, 12500000]),
+       col_indices=tensor([  4985,  20152,  29473,  ..., 475854, 476409,
+                           499068]),
+       values=tensor([0.8596, 0.1215, 0.4132,  ..., 0.1501, 0.7884, 0.5323]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.0930, 0.1654, 0.5776,  ..., 0.1397, 0.2168, 0.6873])
+tensor([0.6345, 0.7237, 0.9663,  ..., 0.0286, 0.9659, 0.0785])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 10.664037466049194 seconds
+Time: 10.043452024459839 seconds
 
-[40.58, 39.64, 39.65, 39.68, 39.54, 40.08, 40.01, 55.98, 39.52, 39.5]
-[124.48]
-16.283326148986816
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2288, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.664037466049194, 'TIME_S_1KI': 4.660855535860661, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2026.948439025879, 'W': 124.48}
-[40.58, 39.64, 39.65, 39.68, 39.54, 40.08, 40.01, 55.98, 39.52, 39.5, 40.22, 40.13, 39.62, 39.51, 44.89, 40.37, 40.29, 40.46, 39.51, 39.67]
-738.865
-36.94325
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2288, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.664037466049194, 'TIME_S_1KI': 4.660855535860661, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2026.948439025879, 'W': 124.48, 'J_1KI': 885.9040380357864, 'W_1KI': 54.40559440559441, 'W_D': 87.53675000000001, 'J_D': 1425.389450272322, 'W_D_1KI': 38.25906905594406, 'J_D_1KI': 16.721621090884643}
+[41.8, 39.96, 40.04, 39.89, 39.75, 39.54, 39.48, 39.64, 39.96, 39.86]
+[123.27]
+15.974560976028442
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2237, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.043452024459839, 'TIME_S_1KI': 4.489696926446061, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1969.1841315150261, 'W': 123.27}
+[41.8, 39.96, 40.04, 39.89, 39.75, 39.54, 39.48, 39.64, 39.96, 39.86, 41.14, 39.7, 39.69, 39.51, 40.0, 40.34, 39.59, 40.7, 43.54, 41.93]
+723.695
+36.18475
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2237, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.043452024459839, 'TIME_S_1KI': 4.489696926446061, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1969.1841315150261, 'W': 123.27, 'J_1KI': 880.2790038064488, 'W_1KI': 55.10505140813589, 'W_D': 87.08525, 'J_D': 1391.1486362376809, 'W_D_1KI': 38.92948144836835, 'J_D_1KI': 17.402539762346155}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json
index f1d4670..c8d1576 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 94004, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.384105920791626, "TIME_S_1KI": 0.12110235650388947, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1783.8070621061327, "W": 114.34, "J_1KI": 18.975863389920992, "W_1KI": 1.2163312199480873, "W_D": 78.24925, "J_D": 1220.758831157148, "W_D_1KI": 0.8324034083656016, "J_D_1KI": 0.008854978600544674}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 99811, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.109006404876709, "TIME_S_1KI": 0.11130042184605614, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1630.7188550758362, "W": 118.48, "J_1KI": 16.338067498330208, "W_1KI": 1.1870435122381302, "W_D": 82.57724999999999, "J_D": 1136.5654842615722, "W_D_1KI": 0.8273361653525162, "J_D_1KI": 0.008289027916286945}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output
index 0b0a270..944fbec 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.04173541069030762}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.03710818290710449}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,      9,  ..., 249994, 249996,
+tensor(crow_indices=tensor([     0,      7,     11,  ..., 249988, 249993,
                             250000]),
-       col_indices=tensor([ 2875, 11250, 41033,  ..., 32140, 46339, 48534]),
-       values=tensor([0.9791, 0.8918, 0.3698,  ..., 0.3708, 0.0646, 0.7857]),
+       col_indices=tensor([ 1823,  2401, 20507,  ..., 29927, 35717, 41362]),
+       values=tensor([0.7021, 0.2937, 0.0368,  ..., 0.8013, 0.6342, 0.2053]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.2100, 0.1946, 0.2511,  ..., 0.6374, 0.0985, 0.4430])
+tensor([0.5957, 0.0820, 0.5341,  ..., 0.0547, 0.1765, 0.4646])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 0.04173541069030762 seconds
+Time: 0.03710818290710449 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '25158', '-ss', '50000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.8100624084472656}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28295', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.9765748977661133}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      2,      7,  ..., 249990, 249993,
+tensor(crow_indices=tensor([     0,      6,     14,  ..., 249988, 249993,
                             250000]),
-       col_indices=tensor([41615, 42906, 15488,  ..., 31340, 31947, 35417]),
-       values=tensor([0.0772, 0.0729, 0.2688,  ..., 0.4463, 0.5032, 0.2162]),
+       col_indices=tensor([ 5249, 20961, 36115,  ..., 24265, 39085, 41781]),
+       values=tensor([0.9608, 0.3689, 0.1001,  ..., 0.3784, 0.3303, 0.4761]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.5388, 0.5198, 0.1392,  ..., 0.7254, 0.7688, 0.9922])
+tensor([0.0189, 0.3335, 0.9964,  ..., 0.1565, 0.8638, 0.1091])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 2.8100624084472656 seconds
+Time: 2.9765748977661133 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '94004', '-ss', '50000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.384105920791626}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '99811', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.109006404876709}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     13,  ..., 249985, 249990,
+tensor(crow_indices=tensor([     0,      6,     13,  ..., 249992, 249998,
                             250000]),
-       col_indices=tensor([ 2939, 14473, 20084,  ..., 45023, 47616, 49448]),
-       values=tensor([0.6894, 0.8051, 0.8240,  ..., 0.7425, 0.1769, 0.4023]),
+       col_indices=tensor([12128, 14735, 23784,  ..., 48408, 17109, 32963]),
+       values=tensor([0.9788, 0.4966, 0.1177,  ..., 0.0710, 0.0367, 0.3291]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4931, 0.5539, 0.3107,  ..., 0.8523, 0.9706, 0.6879])
+tensor([0.8833, 0.9491, 0.8566,  ..., 0.2783, 0.1791, 0.8533])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 11.384105920791626 seconds
+Time: 11.109006404876709 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     13,  ..., 249985, 249990,
+tensor(crow_indices=tensor([     0,      6,     13,  ..., 249992, 249998,
                             250000]),
-       col_indices=tensor([ 2939, 14473, 20084,  ..., 45023, 47616, 49448]),
-       values=tensor([0.6894, 0.8051, 0.8240,  ..., 0.7425, 0.1769, 0.4023]),
+       col_indices=tensor([12128, 14735, 23784,  ..., 48408, 17109, 32963]),
+       values=tensor([0.9788, 0.4966, 0.1177,  ..., 0.0710, 0.0367, 0.3291]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4931, 0.5539, 0.3107,  ..., 0.8523, 0.9706, 0.6879])
+tensor([0.8833, 0.9491, 0.8566,  ..., 0.2783, 0.1791, 0.8533])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 11.384105920791626 seconds
+Time: 11.109006404876709 seconds
 
-[40.36, 39.91, 39.67, 39.83, 39.58, 39.72, 40.16, 39.72, 39.91, 39.98]
-[114.34]
-15.600901365280151
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 94004, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.384105920791626, 'TIME_S_1KI': 0.12110235650388947, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1783.8070621061327, 'W': 114.34}
-[40.36, 39.91, 39.67, 39.83, 39.58, 39.72, 40.16, 39.72, 39.91, 39.98, 40.89, 39.87, 39.99, 39.51, 39.5, 39.56, 39.75, 45.01, 39.69, 39.64]
-721.815
-36.09075
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 94004, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.384105920791626, 'TIME_S_1KI': 0.12110235650388947, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1783.8070621061327, 'W': 114.34, 'J_1KI': 18.975863389920992, 'W_1KI': 1.2163312199480873, 'W_D': 78.24925, 'J_D': 1220.758831157148, 'W_D_1KI': 0.8324034083656016, 'J_D_1KI': 0.008854978600544674}
+[40.97, 40.76, 39.98, 39.9, 39.51, 39.39, 40.3, 39.56, 39.43, 40.13]
+[118.48]
+13.763663530349731
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 99811, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.109006404876709, 'TIME_S_1KI': 0.11130042184605614, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1630.7188550758362, 'W': 118.48}
+[40.97, 40.76, 39.98, 39.9, 39.51, 39.39, 40.3, 39.56, 39.43, 40.13, 40.86, 39.49, 40.21, 40.32, 39.93, 39.79, 39.93, 39.38, 39.42, 39.55]
+718.0550000000001
+35.902750000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 99811, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.109006404876709, 'TIME_S_1KI': 0.11130042184605614, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1630.7188550758362, 'W': 118.48, 'J_1KI': 16.338067498330208, 'W_1KI': 1.1870435122381302, 'W_D': 82.57724999999999, 'J_D': 1136.5654842615722, 'W_D_1KI': 0.8273361653525162, 'J_D_1KI': 0.008289027916286945}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json
index ca129d7..0ed93bf 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 46418, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.356630086898804, "TIME_S_1KI": 0.22311668074666732, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1954.5975055408476, "W": 147.82, "J_1KI": 42.10861100307742, "W_1KI": 3.1845404799862123, "W_D": 111.69725, "J_D": 1476.952822525859, "W_D_1KI": 2.4063348270067646, "J_D_1KI": 0.051840553815476}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 45970, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.176043510437012, "TIME_S_1KI": 0.22136270416439008, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1947.0663536453249, "W": 147.36, "J_1KI": 42.355152352519575, "W_1KI": 3.2055688492495107, "W_D": 110.93175000000002, "J_D": 1465.7402142779233, "W_D_1KI": 2.413133565368719, "J_D_1KI": 0.052493660329969966}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output
index 0dda590..40eeb90 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06997370719909668}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.07438278198242188}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      52,     102,  ..., 2499883,
-                            2499945, 2500000]),
-       col_indices=tensor([  266,   347,  3014,  ..., 46062, 47055, 47354]),
-       values=tensor([0.8937, 0.7241, 0.1967,  ..., 0.6923, 0.3348, 0.4624]),
+tensor(crow_indices=tensor([      0,      43,      95,  ..., 2499906,
+                            2499957, 2500000]),
+       col_indices=tensor([   93,   772,  1278,  ..., 48633, 48867, 48966]),
+       values=tensor([0.9058, 0.1037, 0.2766,  ..., 0.4493, 0.5334, 0.4126]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3743, 0.1265, 0.3703,  ..., 0.6234, 0.9781, 0.6963])
+tensor([0.1532, 0.4835, 0.1070,  ..., 0.8056, 0.9315, 0.2414])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,39 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 0.06997370719909668 seconds
+Time: 0.07438278198242188 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15005', '-ss', '50000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.3941891193389893}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '14116', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.2241876125335693}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      54,      97,  ..., 2499911,
-                            2499955, 2500000]),
-       col_indices=tensor([ 2370,  4930,  5051,  ..., 41423, 44524, 44646]),
-       values=tensor([0.0412, 0.5807, 0.8088,  ..., 0.8046, 0.7553, 0.5801]),
-       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6465, 0.4915, 0.1151,  ..., 0.6682, 0.4745, 0.9594])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 2500000
-Density: 0.001
-Time: 3.3941891193389893 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '46418', '-ss', '50000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.356630086898804}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      46,      86,  ..., 2499896,
+tensor(crow_indices=tensor([      0,      34,      92,  ..., 2499900,
                             2499949, 2500000]),
-       col_indices=tensor([ 1254,  3268,  3363,  ..., 48004, 48805, 49373]),
-       values=tensor([0.4618, 0.8696, 0.7740,  ..., 0.9354, 0.3130, 0.0156]),
+       col_indices=tensor([ 1404,  3513,  5830,  ..., 44159, 44569, 48306]),
+       values=tensor([0.1373, 0.2439, 0.5744,  ..., 0.3908, 0.2364, 0.3591]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3463, 0.0749, 0.0037,  ..., 0.8223, 0.0446, 0.2738])
+tensor([0.1362, 0.8781, 0.3760,  ..., 0.5068, 0.4476, 0.2011])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.356630086898804 seconds
+Time: 3.2241876125335693 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '45970', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.176043510437012}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      46,      86,  ..., 2499896,
-                            2499949, 2500000]),
-       col_indices=tensor([ 1254,  3268,  3363,  ..., 48004, 48805, 49373]),
-       values=tensor([0.4618, 0.8696, 0.7740,  ..., 0.9354, 0.3130, 0.0156]),
+tensor(crow_indices=tensor([      0,      44,      88,  ..., 2499900,
+                            2499948, 2500000]),
+       col_indices=tensor([ 1615,  2373,  2611,  ..., 46930, 47083, 48076]),
+       values=tensor([0.9230, 0.1594, 0.5959,  ..., 0.9087, 0.9174, 0.2268]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.3463, 0.0749, 0.0037,  ..., 0.8223, 0.0446, 0.2738])
+tensor([0.0874, 0.4273, 0.5493,  ..., 0.5645, 0.1367, 0.9834])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +56,30 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.356630086898804 seconds
+Time: 10.176043510437012 seconds
 
-[40.8, 41.43, 39.77, 40.05, 40.2, 40.01, 40.25, 39.8, 39.91, 39.83]
-[147.82]
-13.222821712493896
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46418, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.356630086898804, 'TIME_S_1KI': 0.22311668074666732, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1954.5975055408476, 'W': 147.82}
-[40.8, 41.43, 39.77, 40.05, 40.2, 40.01, 40.25, 39.8, 39.91, 39.83, 40.83, 39.7, 40.25, 40.38, 40.19, 39.67, 41.03, 39.72, 39.6, 39.53]
-722.4549999999999
-36.122749999999996
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46418, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.356630086898804, 'TIME_S_1KI': 0.22311668074666732, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1954.5975055408476, 'W': 147.82, 'J_1KI': 42.10861100307742, 'W_1KI': 3.1845404799862123, 'W_D': 111.69725, 'J_D': 1476.952822525859, 'W_D_1KI': 2.4063348270067646, 'J_D_1KI': 0.051840553815476}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      44,      88,  ..., 2499900,
+                            2499948, 2500000]),
+       col_indices=tensor([ 1615,  2373,  2611,  ..., 46930, 47083, 48076]),
+       values=tensor([0.9230, 0.1594, 0.5959,  ..., 0.9087, 0.9174, 0.2268]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0874, 0.4273, 0.5493,  ..., 0.5645, 0.1367, 0.9834])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.176043510437012 seconds
+
+[41.09, 39.77, 39.56, 40.76, 39.72, 40.02, 40.67, 40.29, 44.66, 39.48]
+[147.36]
+13.212990999221802
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 45970, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.176043510437012, 'TIME_S_1KI': 0.22136270416439008, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1947.0663536453249, 'W': 147.36}
+[41.09, 39.77, 39.56, 40.76, 39.72, 40.02, 40.67, 40.29, 44.66, 39.48, 40.16, 39.91, 39.65, 39.38, 45.28, 39.52, 39.9, 39.57, 39.84, 39.4]
+728.5649999999999
+36.42825
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 45970, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.176043510437012, 'TIME_S_1KI': 0.22136270416439008, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1947.0663536453249, 'W': 147.36, 'J_1KI': 42.355152352519575, 'W_1KI': 3.2055688492495107, 'W_D': 110.93175000000002, 'J_D': 1465.7402142779233, 'W_D_1KI': 2.413133565368719, 'J_D_1KI': 0.052493660329969966}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.json
index ce3aa00..4012a55 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1681, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.72816252708435, "TIME_S_1KI": 6.382012211233998, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2210.374014186859, "W": 116.29999999999998, "J_1KI": 1314.9161297958708, "W_1KI": 69.18500892325996, "W_D": 80.01049999999998, "J_D": 1520.6631991581912, "W_D_1KI": 47.596966091612124, "J_D_1KI": 28.314673463183894}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1683, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.336533784866333, "TIME_S_1KI": 6.141731304139236, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2269.7345922088625, "W": 116.64000000000001, "J_1KI": 1348.6242377949272, "W_1KI": 69.30481283422462, "W_D": 80.63875000000002, "J_D": 1569.1749000984432, "W_D_1KI": 47.91369578134285, "J_D_1KI": 28.469219121415836}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.output
index fde54b7..c3a231d 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.output
@@ -1,14 +1,54 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.6244547367095947}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.6703436374664307}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      524,     1064,  ..., 24999000,
+tensor(crow_indices=tensor([       0,      490,     1012,  ..., 24999072,
+                            24999522, 25000000]),
+       col_indices=tensor([  139,   180,   438,  ..., 49771, 49774, 49863]),
+       values=tensor([0.3902, 0.9074, 0.1633,  ..., 0.6016, 0.0459, 0.5003]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.5558, 0.4609, 0.2036,  ..., 0.5663, 0.8384, 0.9866])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 0.6703436374664307 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1566', '-ss', '50000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.764458417892456}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      488,      992,  ..., 24998975,
+                            24999476, 25000000]),
+       col_indices=tensor([   69,    85,   277,  ..., 49779, 49916, 49923]),
+       values=tensor([0.9712, 0.8967, 0.3986,  ..., 0.6840, 0.3057, 0.8093]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.2667, 0.3205, 0.1054,  ..., 0.7906, 0.2246, 0.0319])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 9.764458417892456 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1683', '-ss', '50000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.336533784866333}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      528,     1023,  ..., 24999023,
                             24999502, 25000000]),
-       col_indices=tensor([   60,    76,   165,  ..., 49872, 49944, 49977]),
-       values=tensor([0.3464, 0.5127, 0.2524,  ..., 0.4585, 0.6152, 0.8409]),
+       col_indices=tensor([   37,    51,   116,  ..., 49907, 49981, 49995]),
+       values=tensor([0.5431, 0.8645, 0.1060,  ..., 0.5554, 0.9629, 0.3371]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.3169, 0.2467, 0.7317,  ..., 0.4966, 0.9013, 0.2021])
+tensor([0.4196, 0.0024, 0.2413,  ..., 0.5179, 0.0396, 0.4997])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 0.6244547367095947 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1681', '-ss', '50000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.72816252708435}
+Time: 10.336533784866333 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      503,      984,  ..., 24998995,
-                            24999487, 25000000]),
-       col_indices=tensor([   80,   111,   167,  ..., 49695, 49904, 49943]),
-       values=tensor([0.9741, 0.4832, 0.1000,  ..., 0.9253, 0.4991, 0.7681]),
+tensor(crow_indices=tensor([       0,      528,     1023,  ..., 24999023,
+                            24999502, 25000000]),
+       col_indices=tensor([   37,    51,   116,  ..., 49907, 49981, 49995]),
+       values=tensor([0.5431, 0.8645, 0.1060,  ..., 0.5554, 0.9629, 0.3371]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.5734, 0.0323, 0.0030,  ..., 0.5787, 0.7337, 0.7260])
+tensor([0.4196, 0.0024, 0.2413,  ..., 0.5179, 0.0396, 0.4997])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,30 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 10.72816252708435 seconds
+Time: 10.336533784866333 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      503,      984,  ..., 24998995,
-                            24999487, 25000000]),
-       col_indices=tensor([   80,   111,   167,  ..., 49695, 49904, 49943]),
-       values=tensor([0.9741, 0.4832, 0.1000,  ..., 0.9253, 0.4991, 0.7681]),
-       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.5734, 0.0323, 0.0030,  ..., 0.5787, 0.7337, 0.7260])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 25000000
-Density: 0.01
-Time: 10.72816252708435 seconds
-
-[40.52, 40.33, 46.14, 39.67, 40.04, 40.05, 39.67, 39.99, 39.82, 39.93]
-[116.3]
-19.0057954788208
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1681, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.72816252708435, 'TIME_S_1KI': 6.382012211233998, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2210.374014186859, 'W': 116.29999999999998}
-[40.52, 40.33, 46.14, 39.67, 40.04, 40.05, 39.67, 39.99, 39.82, 39.93, 42.61, 39.87, 39.87, 39.75, 39.67, 39.77, 39.75, 39.54, 40.39, 39.88]
-725.7900000000001
-36.289500000000004
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1681, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.72816252708435, 'TIME_S_1KI': 6.382012211233998, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2210.374014186859, 'W': 116.29999999999998, 'J_1KI': 1314.9161297958708, 'W_1KI': 69.18500892325996, 'W_D': 80.01049999999998, 'J_D': 1520.6631991581912, 'W_D_1KI': 47.596966091612124, 'J_D_1KI': 28.314673463183894}
+[40.99, 40.03, 39.7, 40.56, 41.71, 40.15, 39.61, 39.6, 39.59, 39.5]
+[116.64]
+19.45931577682495
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1683, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.336533784866333, 'TIME_S_1KI': 6.141731304139236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2269.7345922088625, 'W': 116.64000000000001}
+[40.99, 40.03, 39.7, 40.56, 41.71, 40.15, 39.61, 39.6, 39.59, 39.5, 41.03, 39.74, 40.04, 40.09, 40.48, 39.46, 39.52, 39.69, 39.52, 39.55]
+720.0250000000001
+36.001250000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1683, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.336533784866333, 'TIME_S_1KI': 6.141731304139236, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2269.7345922088625, 'W': 116.64000000000001, 'J_1KI': 1348.6242377949272, 'W_1KI': 69.30481283422462, 'W_D': 80.63875000000002, 'J_D': 1569.1749000984432, 'W_D_1KI': 47.91369578134285, 'J_D_1KI': 28.469219121415836}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.05.json
new file mode 100644
index 0000000..f7df380
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 370, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 13.602883338928223, "TIME_S_1KI": 36.76454956467087, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3923.328741321564, "W": 95.93, "J_1KI": 10603.591192760983, "W_1KI": 259.27027027027026, "W_D": 59.633500000000005, "J_D": 2438.8806889982225, "W_D_1KI": 161.17162162162163, "J_D_1KI": 435.5989773557341}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.05.output
new file mode 100644
index 0000000..a0994fc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.831510305404663}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2449,      4912,  ...,
+                            124994935, 124997480, 125000000]),
+       col_indices=tensor([    8,    35,    55,  ..., 49956, 49985, 49993]),
+       values=tensor([0.2569, 0.7242, 0.2453,  ..., 0.3891, 0.2871, 0.8384]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.0743, 0.9130, 0.1660,  ..., 0.7346, 0.0952, 0.8944])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 2.831510305404663 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '370', '-ss', '50000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 13.602883338928223}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2444,      4961,  ...,
+                            124995104, 124997530, 125000000]),
+       col_indices=tensor([   73,    77,    95,  ..., 49959, 49981, 49999]),
+       values=tensor([0.2571, 0.0881, 0.2055,  ..., 0.2189, 0.9227, 0.1207]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.4151, 0.6477, 0.9996,  ..., 0.9228, 0.9960, 0.2452])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 13.602883338928223 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2444,      4961,  ...,
+                            124995104, 124997530, 125000000]),
+       col_indices=tensor([   73,    77,    95,  ..., 49959, 49981, 49999]),
+       values=tensor([0.2571, 0.0881, 0.2055,  ..., 0.2189, 0.9227, 0.1207]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.4151, 0.6477, 0.9996,  ..., 0.9228, 0.9960, 0.2452])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 13.602883338928223 seconds
+
+[40.88, 40.42, 39.82, 39.83, 39.92, 39.86, 40.1, 40.12, 41.06, 40.17]
+[95.93]
+40.89782905578613
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 370, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 13.602883338928223, 'TIME_S_1KI': 36.76454956467087, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3923.328741321564, 'W': 95.93}
+[40.88, 40.42, 39.82, 39.83, 39.92, 39.86, 40.1, 40.12, 41.06, 40.17, 41.33, 39.79, 39.98, 39.62, 40.05, 39.91, 39.89, 41.64, 40.15, 45.16]
+725.9300000000001
+36.2965
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 370, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 13.602883338928223, 'TIME_S_1KI': 36.76454956467087, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3923.328741321564, 'W': 95.93, 'J_1KI': 10603.591192760983, 'W_1KI': 259.27027027027026, 'W_D': 59.633500000000005, 'J_D': 2438.8806889982225, 'W_D_1KI': 161.17162162162163, 'J_D_1KI': 435.5989773557341}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json
index 73ac2d2..0959bd4 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 128261, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.553161382675171, "TIME_S_1KI": 0.08227880168309283, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1360.8756747579575, "W": 104.26, "J_1KI": 10.610206335191194, "W_1KI": 0.8128737496199158, "W_D": 68.19500000000001, "J_D": 890.1296435844899, "W_D_1KI": 0.5316892898075019, "J_D_1KI": 0.004145369908292481}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 126053, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.525958776473999, "TIME_S_1KI": 0.08350423057344132, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1333.1147343921662, "W": 104.19, "J_1KI": 10.57582710758305, "W_1KI": 0.8265570831316986, "W_D": 68.29400000000001, "J_D": 873.8241450290682, "W_D_1KI": 0.5417879780727155, "J_D_1KI": 0.004298096658331936}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output
index b70442e..4772a3e 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output
@@ -1,32 +1,13 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.0555570125579834}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([42051, 34515, 37611,  ..., 41473, 46289, 26191]),
-       values=tensor([0.0144, 0.4378, 0.1715,  ..., 0.0832, 0.5030, 0.6687]),
-       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1124, 0.4102, 0.7912,  ..., 0.3553, 0.2259, 0.3847])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 25000
-Density: 1e-05
-Time: 0.0555570125579834 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18899', '-ss', '50000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.547147274017334}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.041290998458862305}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
-       col_indices=tensor([ 9684, 40954, 42907,  ..., 26506, 37971, 35337]),
-       values=tensor([0.0766, 0.5354, 0.2778,  ..., 0.4912, 0.6494, 0.7856]),
+       col_indices=tensor([39202, 12250, 26322,  ..., 37318, 47951,  8445]),
+       values=tensor([0.5740, 0.6345, 0.0638,  ..., 0.9586, 0.6580, 0.7048]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.2325, 0.6288, 0.8060,  ..., 0.4059, 0.0257, 0.5351])
+tensor([0.6102, 0.5871, 0.5346,  ..., 0.2772, 0.0260, 0.5471])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,18 +15,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 1.547147274017334 seconds
+Time: 0.041290998458862305 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '128261', '-ss', '50000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.553161382675171}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '25429', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.1181838512420654}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     2,  ..., 24999, 24999, 25000]),
-       col_indices=tensor([43490, 45422, 41208,  ..., 48729, 34812, 29106]),
-       values=tensor([0.6729, 0.4582, 0.1719,  ..., 0.9792, 0.1938, 0.4197]),
+tensor(crow_indices=tensor([    0,     3,     3,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([14352, 21301, 37384,  ...,  4637,  8775, 16739]),
+       values=tensor([0.2683, 0.5206, 0.7101,  ..., 0.1299, 0.1065, 0.5952]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.3715, 0.8721, 0.7070,  ..., 0.0207, 0.1985, 0.3006])
+tensor([0.5496, 0.8003, 0.5774,  ..., 0.1083, 0.3526, 0.4589])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,15 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.553161382675171 seconds
+Time: 2.1181838512420654 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '126053', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.525958776473999}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     2,  ..., 24999, 24999, 25000]),
-       col_indices=tensor([43490, 45422, 41208,  ..., 48729, 34812, 29106]),
-       values=tensor([0.6729, 0.4582, 0.1719,  ..., 0.9792, 0.1938, 0.4197]),
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 24997, 25000, 25000]),
+       col_indices=tensor([20587, 37923, 20731,  ...,   134,  7495, 29489]),
+       values=tensor([0.6053, 0.5048, 0.9193,  ..., 0.2382, 0.3077, 0.8468]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.3715, 0.8721, 0.7070,  ..., 0.0207, 0.1985, 0.3006])
+tensor([0.0915, 0.4356, 0.4649,  ..., 0.6897, 0.4993, 0.9107])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -69,13 +53,29 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.553161382675171 seconds
+Time: 10.525958776473999 seconds
 
-[42.38, 39.84, 39.75, 39.86, 39.86, 40.05, 40.23, 39.88, 39.93, 39.73]
-[104.26]
-13.052711248397827
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 128261, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.553161382675171, 'TIME_S_1KI': 0.08227880168309283, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1360.8756747579575, 'W': 104.26}
-[42.38, 39.84, 39.75, 39.86, 39.86, 40.05, 40.23, 39.88, 39.93, 39.73, 40.22, 39.71, 39.72, 39.44, 39.86, 40.03, 39.57, 39.83, 40.02, 45.11]
-721.3
-36.065
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 128261, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.553161382675171, 'TIME_S_1KI': 0.08227880168309283, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1360.8756747579575, 'W': 104.26, 'J_1KI': 10.610206335191194, 'W_1KI': 0.8128737496199158, 'W_D': 68.19500000000001, 'J_D': 890.1296435844899, 'W_D_1KI': 0.5316892898075019, 'J_D_1KI': 0.004145369908292481}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 24997, 25000, 25000]),
+       col_indices=tensor([20587, 37923, 20731,  ...,   134,  7495, 29489]),
+       values=tensor([0.6053, 0.5048, 0.9193,  ..., 0.2382, 0.3077, 0.8468]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0915, 0.4356, 0.4649,  ..., 0.6897, 0.4993, 0.9107])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.525958776473999 seconds
+
+[40.82, 39.94, 40.07, 39.92, 39.76, 39.88, 39.51, 39.48, 40.97, 39.53]
+[104.19]
+12.795035362243652
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 126053, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.525958776473999, 'TIME_S_1KI': 0.08350423057344132, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1333.1147343921662, 'W': 104.19}
+[40.82, 39.94, 40.07, 39.92, 39.76, 39.88, 39.51, 39.48, 40.97, 39.53, 40.7, 39.51, 40.31, 39.76, 39.51, 39.78, 40.09, 39.51, 39.68, 39.43]
+717.9199999999998
+35.895999999999994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 126053, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.525958776473999, 'TIME_S_1KI': 0.08350423057344132, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1333.1147343921662, 'W': 104.19, 'J_1KI': 10.57582710758305, 'W_1KI': 0.8265570831316986, 'W_D': 68.29400000000001, 'J_D': 873.8241450290682, 'W_D_1KI': 0.5417879780727155, 'J_D_1KI': 0.004298096658331936}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.json
index b465c9c..cab8894 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 110115, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.965436697006226, "TIME_S_1KI": 0.1086630949190049, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1446.9478559374809, "W": 111.69, "J_1KI": 13.14033379591773, "W_1KI": 1.0143032284429914, "W_D": 75.71975, "J_D": 980.9520092633368, "W_D_1KI": 0.6876424646959997, "J_D_1KI": 0.006244766514062568}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 109904, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.213285207748413, "TIME_S_1KI": 0.09292914914605849, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1450.8303464508058, "W": 111.68, "J_1KI": 13.200887560514683, "W_1KI": 1.0161595574319406, "W_D": 75.4555, "J_D": 980.2393374518156, "W_D_1KI": 0.686558269034794, "J_D_1KI": 0.0062468906412395725}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.output
index b1f3c1f..57edacd 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.05198168754577637}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.029732704162597656}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      2,  ..., 124998, 124999,
+tensor(crow_indices=tensor([     0,      4,      6,  ..., 124997, 124998,
                             125000]),
-       col_indices=tensor([  927,  8914,  5646,  ..., 41839,  2622, 37662]),
-       values=tensor([0.2093, 0.3505, 0.4434,  ..., 0.7585, 0.2953, 0.8139]),
+       col_indices=tensor([15960, 34782, 36176,  ..., 12559, 34529, 38985]),
+       values=tensor([0.0791, 0.6156, 0.0818,  ..., 0.8529, 0.2215, 0.9416]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.7158, 0.3261, 0.8838,  ..., 0.1644, 0.9864, 0.1779])
+tensor([0.3698, 0.6501, 0.4173,  ..., 0.1077, 0.8642, 0.5336])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 0.05198168754577637 seconds
+Time: 0.029732704162597656 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '20199', '-ss', '50000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.9260566234588623}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '35314', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.3738198280334473}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,      9,  ..., 124993, 124995,
+tensor(crow_indices=tensor([     0,      3,      5,  ..., 124994, 124995,
                             125000]),
-       col_indices=tensor([ 5280, 12669, 18309,  ..., 32915, 33761, 44585]),
-       values=tensor([0.1104, 0.6442, 0.1166,  ..., 0.0611, 0.5204, 0.6774]),
+       col_indices=tensor([21922, 29755, 43390,  ..., 22404, 26277, 33750]),
+       values=tensor([0.2859, 0.5691, 0.3202,  ..., 0.6287, 0.7111, 0.2865]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.3981, 0.9235, 0.5295,  ..., 0.9056, 0.3690, 0.2596])
+tensor([0.8597, 0.0062, 0.6406,  ..., 0.7576, 0.3863, 0.5631])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 1.9260566234588623 seconds
+Time: 3.3738198280334473 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '110115', '-ss', '50000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.965436697006226}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '109904', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.213285207748413}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,      7,  ..., 124990, 124994,
+tensor(crow_indices=tensor([     0,      4,      5,  ..., 124997, 124999,
                             125000]),
-       col_indices=tensor([  332, 15911, 38702,  ..., 27905, 36936, 47310]),
-       values=tensor([0.9967, 0.4995, 0.1475,  ..., 0.0565, 0.7404, 0.0608]),
+       col_indices=tensor([ 7419, 31029, 43158,  ...,  3143, 44044, 12484]),
+       values=tensor([0.5434, 0.7479, 0.4321,  ..., 0.0308, 0.7117, 0.2391]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.3103, 0.3240, 0.6987,  ..., 0.1758, 0.7445, 0.7079])
+tensor([0.4874, 0.6180, 0.9672,  ..., 0.1180, 0.9110, 0.1759])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 11.965436697006226 seconds
+Time: 10.213285207748413 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,      7,  ..., 124990, 124994,
+tensor(crow_indices=tensor([     0,      4,      5,  ..., 124997, 124999,
                             125000]),
-       col_indices=tensor([  332, 15911, 38702,  ..., 27905, 36936, 47310]),
-       values=tensor([0.9967, 0.4995, 0.1475,  ..., 0.0565, 0.7404, 0.0608]),
+       col_indices=tensor([ 7419, 31029, 43158,  ...,  3143, 44044, 12484]),
+       values=tensor([0.5434, 0.7479, 0.4321,  ..., 0.0308, 0.7117, 0.2391]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.3103, 0.3240, 0.6987,  ..., 0.1758, 0.7445, 0.7079])
+tensor([0.4874, 0.6180, 0.9672,  ..., 0.1180, 0.9110, 0.1759])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 11.965436697006226 seconds
+Time: 10.213285207748413 seconds
 
-[40.59, 40.33, 40.2, 40.19, 39.75, 39.57, 39.56, 39.52, 39.58, 39.6]
-[111.69]
-12.955034971237183
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 110115, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.965436697006226, 'TIME_S_1KI': 0.1086630949190049, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1446.9478559374809, 'W': 111.69}
-[40.59, 40.33, 40.2, 40.19, 39.75, 39.57, 39.56, 39.52, 39.58, 39.6, 40.22, 41.59, 39.67, 40.02, 39.96, 39.85, 39.52, 40.16, 39.94, 39.58]
-719.405
-35.97025
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 110115, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.965436697006226, 'TIME_S_1KI': 0.1086630949190049, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1446.9478559374809, 'W': 111.69, 'J_1KI': 13.14033379591773, 'W_1KI': 1.0143032284429914, 'W_D': 75.71975, 'J_D': 980.9520092633368, 'W_D_1KI': 0.6876424646959997, 'J_D_1KI': 0.006244766514062568}
+[40.83, 39.5, 39.52, 39.34, 39.66, 39.79, 39.96, 39.74, 39.52, 44.63]
+[111.68]
+12.990959405899048
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 109904, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.213285207748413, 'TIME_S_1KI': 0.09292914914605849, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1450.8303464508058, 'W': 111.68}
+[40.83, 39.5, 39.52, 39.34, 39.66, 39.79, 39.96, 39.74, 39.52, 44.63, 40.65, 40.28, 39.58, 39.41, 40.46, 40.44, 44.79, 39.34, 40.26, 39.69]
+724.49
+36.2245
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 109904, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.213285207748413, 'TIME_S_1KI': 0.09292914914605849, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1450.8303464508058, 'W': 111.68, 'J_1KI': 13.200887560514683, 'W_1KI': 1.0161595574319406, 'W_D': 75.4555, 'J_D': 980.2393374518156, 'W_D_1KI': 0.686558269034794, 'J_D_1KI': 0.0062468906412395725}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json
index 2c10823..3b6d222 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 447788, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.585598468780518, "TIME_S_1KI": 0.023639754680296294, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1210.6447257900238, "W": 96.76, "J_1KI": 2.7036113647306848, "W_1KI": 0.21608439708076144, "W_D": 60.779250000000005, "J_D": 760.4596780691744, "W_D_1KI": 0.13573219916567664, "J_D_1KI": 0.00030311709819306603}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 449815, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.705693244934082, "TIME_S_1KI": 0.023800213965594924, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1273.803324151039, "W": 97.05, "J_1KI": 2.8318382538399987, "W_1KI": 0.21575536609495014, "W_D": 60.271499999999996, "J_D": 791.0771463325024, "W_D_1KI": 0.13399175216477885, "J_D_1KI": 0.0002978819118188118}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.output
index 38edffa..40f219b 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.01688361167907715}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.01718902587890625}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 2499, 2499, 2500]),
-       col_indices=tensor([2619, 4724, 4043,  ...,  721, 4005, 3452]),
-       values=tensor([0.3560, 0.4737, 0.9490,  ..., 0.6650, 0.5511, 0.5102]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([1031, 2134, 3925,  ..., 1116, 1736, 1811]),
+       values=tensor([0.7566, 0.0335, 0.3769,  ..., 0.8150, 0.7471, 0.5019]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.9035, 0.6347, 0.7264,  ..., 0.8885, 0.4271, 0.9746])
+tensor([0.4116, 0.1385, 0.3532,  ..., 0.9353, 0.1525, 0.3623])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.01688361167907715 seconds
+Time: 0.01718902587890625 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '62190', '-ss', '5000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.4582650661468506}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '61085', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.4259023666381836}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([ 785, 2078,  964,  ..., 3093, 2409, 4914]),
-       values=tensor([0.2674, 0.7127, 0.0446,  ..., 0.5887, 0.3242, 0.2984]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([2306, 3269, 4125,  ..., 3228, 4729, 1910]),
+       values=tensor([0.0516, 0.6989, 0.3045,  ..., 0.6684, 0.0411, 0.3500]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.2053, 0.2435, 0.6452,  ..., 0.2463, 0.9693, 0.2980])
+tensor([0.4004, 0.2061, 0.1055,  ..., 0.1168, 0.0661, 0.6525])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 1.4582650661468506 seconds
+Time: 1.4259023666381836 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '447788', '-ss', '5000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.585598468780518}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '449815', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.705693244934082}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([3170,   56,  953,  ..., 2101, 4088, 4138]),
-       values=tensor([0.7441, 0.4324, 0.6982,  ..., 0.2565, 0.3946, 0.1156]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([4152, 2644, 2174,  ..., 3211, 4021,   16]),
+       values=tensor([0.0598, 0.0855, 0.2128,  ..., 0.5120, 0.7970, 0.1348]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.7808, 0.5836, 0.6876,  ..., 0.2450, 0.1275, 0.2911])
+tensor([0.4898, 0.1566, 0.9091,  ..., 0.4315, 0.1963, 0.8990])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.585598468780518 seconds
+Time: 10.705693244934082 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([3170,   56,  953,  ..., 2101, 4088, 4138]),
-       values=tensor([0.7441, 0.4324, 0.6982,  ..., 0.2565, 0.3946, 0.1156]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([4152, 2644, 2174,  ..., 3211, 4021,   16]),
+       values=tensor([0.0598, 0.0855, 0.2128,  ..., 0.5120, 0.7970, 0.1348]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.7808, 0.5836, 0.6876,  ..., 0.2450, 0.1275, 0.2911])
+tensor([0.4898, 0.1566, 0.9091,  ..., 0.4315, 0.1963, 0.8990])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.585598468780518 seconds
+Time: 10.705693244934082 seconds
 
-[40.68, 39.36, 44.27, 39.07, 39.18, 39.33, 39.3, 39.56, 39.47, 39.52]
-[96.76]
-12.511830568313599
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 447788, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.585598468780518, 'TIME_S_1KI': 0.023639754680296294, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1210.6447257900238, 'W': 96.76}
-[40.68, 39.36, 44.27, 39.07, 39.18, 39.33, 39.3, 39.56, 39.47, 39.52, 45.98, 39.49, 39.82, 39.41, 39.22, 39.42, 41.09, 39.62, 39.26, 39.31]
-719.615
-35.98075
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 447788, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.585598468780518, 'TIME_S_1KI': 0.023639754680296294, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1210.6447257900238, 'W': 96.76, 'J_1KI': 2.7036113647306848, 'W_1KI': 0.21608439708076144, 'W_D': 60.779250000000005, 'J_D': 760.4596780691744, 'W_D_1KI': 0.13573219916567664, 'J_D_1KI': 0.00030311709819306603}
+[41.07, 39.76, 54.14, 40.27, 39.53, 40.02, 39.52, 39.39, 39.7, 39.29]
+[97.05]
+13.125227451324463
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 449815, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.705693244934082, 'TIME_S_1KI': 0.023800213965594924, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1273.803324151039, 'W': 97.05}
+[41.07, 39.76, 54.14, 40.27, 39.53, 40.02, 39.52, 39.39, 39.7, 39.29, 40.12, 40.22, 39.78, 39.17, 40.41, 39.73, 39.72, 45.19, 39.21, 39.14]
+735.57
+36.7785
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 449815, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.705693244934082, 'TIME_S_1KI': 0.023800213965594924, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1273.803324151039, 'W': 97.05, 'J_1KI': 2.8318382538399987, 'W_1KI': 0.21575536609495014, 'W_D': 60.271499999999996, 'J_D': 791.0771463325024, 'W_D_1KI': 0.13399175216477885, 'J_D_1KI': 0.0002978819118188118}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json
index f972143..434a764 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 251242, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.495816707611084, "TIME_S_1KI": 0.041775725028502735, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1274.650104198456, "W": 98.84, "J_1KI": 5.073395786526361, "W_1KI": 0.393405561172097, "W_D": 63.200750000000006, "J_D": 815.0429236434699, "W_D_1KI": 0.25155328328862214, "J_D_1KI": 0.0010012389779122206}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 255321, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.671942234039307, "TIME_S_1KI": 0.041798137380157946, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1286.0644676828385, "W": 98.73, "J_1KI": 5.037049313150264, "W_1KI": 0.38668969649970036, "W_D": 62.91625, "J_D": 819.5518440681695, "W_D_1KI": 0.24642019262027018, "J_D_1KI": 0.000965138757173402}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.output
index 489fd07..72f6dea 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.020148515701293945}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.02073979377746582}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     7,    10,  ..., 24991, 24995, 25000]),
-       col_indices=tensor([ 119,  931, 3406,  ..., 3461, 3840, 3846]),
-       values=tensor([0.6773, 0.1678, 0.7190,  ..., 0.1084, 0.6735, 0.7339]),
+tensor(crow_indices=tensor([    0,     8,    13,  ..., 24986, 24997, 25000]),
+       col_indices=tensor([ 210,  616, 1664,  ..., 1859, 2406, 3744]),
+       values=tensor([0.8494, 0.3277, 0.6011,  ..., 0.0442, 0.0395, 0.0883]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0928, 0.4745, 0.9490,  ..., 0.8279, 0.2614, 0.8062])
+tensor([0.9625, 0.8000, 0.6179,  ..., 0.0228, 0.2614, 0.9913])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.020148515701293945 seconds
+Time: 0.02073979377746582 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52113', '-ss', '5000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.177922248840332}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '50627', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.082019805908203}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     6,    11,  ..., 24987, 24992, 25000]),
-       col_indices=tensor([1164, 1818, 2007,  ..., 3806, 4515, 4674]),
-       values=tensor([0.7293, 0.4677, 0.0557,  ..., 0.9608, 0.8022, 0.8772]),
+tensor(crow_indices=tensor([    0,     3,     7,  ..., 24989, 24993, 25000]),
+       col_indices=tensor([ 617,  730, 1271,  ..., 3299, 4296, 4682]),
+       values=tensor([0.5887, 0.2909, 0.9734,  ..., 0.1489, 0.9540, 0.8941]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.5594, 0.0674, 0.3236,  ..., 0.9684, 0.1982, 0.3579])
+tensor([0.9697, 0.5962, 0.4255,  ..., 0.8171, 0.4802, 0.2715])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 2.177922248840332 seconds
+Time: 2.082019805908203 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '251242', '-ss', '5000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.495816707611084}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '255321', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.671942234039307}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     8,  ..., 24992, 24997, 25000]),
-       col_indices=tensor([1486, 3242, 3522,  ..., 1754, 2627, 4146]),
-       values=tensor([0.1836, 0.4006, 0.2197,  ..., 0.0536, 0.9699, 0.8761]),
+tensor(crow_indices=tensor([    0,     7,    12,  ..., 24991, 24996, 25000]),
+       col_indices=tensor([ 914,  959, 3594,  ..., 2558, 2872, 4517]),
+       values=tensor([0.7861, 0.8355, 0.9023,  ..., 0.3734, 0.5279, 0.9813]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8727, 0.2072, 0.9866,  ..., 0.7187, 0.7974, 0.6886])
+tensor([0.7691, 0.3774, 0.5048,  ..., 0.0036, 0.9716, 0.0233])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.495816707611084 seconds
+Time: 10.671942234039307 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     8,  ..., 24992, 24997, 25000]),
-       col_indices=tensor([1486, 3242, 3522,  ..., 1754, 2627, 4146]),
-       values=tensor([0.1836, 0.4006, 0.2197,  ..., 0.0536, 0.9699, 0.8761]),
+tensor(crow_indices=tensor([    0,     7,    12,  ..., 24991, 24996, 25000]),
+       col_indices=tensor([ 914,  959, 3594,  ..., 2558, 2872, 4517]),
+       values=tensor([0.7861, 0.8355, 0.9023,  ..., 0.3734, 0.5279, 0.9813]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8727, 0.2072, 0.9866,  ..., 0.7187, 0.7974, 0.6886])
+tensor([0.7691, 0.3774, 0.5048,  ..., 0.0036, 0.9716, 0.0233])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.495816707611084 seconds
+Time: 10.671942234039307 seconds
 
-[40.75, 39.26, 39.42, 39.22, 39.47, 40.61, 39.23, 39.53, 39.63, 39.38]
-[98.84]
-12.896095752716064
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 251242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.495816707611084, 'TIME_S_1KI': 0.041775725028502735, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.650104198456, 'W': 98.84}
-[40.75, 39.26, 39.42, 39.22, 39.47, 40.61, 39.23, 39.53, 39.63, 39.38, 40.47, 39.12, 40.14, 40.63, 39.59, 39.14, 39.11, 39.22, 39.39, 39.55]
-712.785
-35.63925
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 251242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.495816707611084, 'TIME_S_1KI': 0.041775725028502735, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.650104198456, 'W': 98.84, 'J_1KI': 5.073395786526361, 'W_1KI': 0.393405561172097, 'W_D': 63.200750000000006, 'J_D': 815.0429236434699, 'W_D_1KI': 0.25155328328862214, 'J_D_1KI': 0.0010012389779122206}
+[40.31, 39.66, 39.89, 39.32, 41.78, 39.24, 39.51, 39.33, 39.28, 39.57]
+[98.73]
+13.026075839996338
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 255321, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.671942234039307, 'TIME_S_1KI': 0.041798137380157946, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1286.0644676828385, 'W': 98.73}
+[40.31, 39.66, 39.89, 39.32, 41.78, 39.24, 39.51, 39.33, 39.28, 39.57, 40.91, 39.42, 39.75, 40.05, 39.94, 40.05, 40.16, 39.13, 39.34, 40.06]
+716.2750000000001
+35.813750000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 255321, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.671942234039307, 'TIME_S_1KI': 0.041798137380157946, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1286.0644676828385, 'W': 98.73, 'J_1KI': 5.037049313150264, 'W_1KI': 0.38668969649970036, 'W_D': 62.91625, 'J_D': 819.5518440681695, 'W_D_1KI': 0.24642019262027018, 'J_D_1KI': 0.000965138757173402}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json
index 7edf7f8..71c10d6 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 151851, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.677409887313843, "TIME_S_1KI": 0.07031504492768466, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1443.7484180927277, "W": 117.47, "J_1KI": 9.507664869462351, "W_1KI": 0.7735872664651534, "W_D": 81.37549999999999, "J_D": 1000.1340716481208, "W_D_1KI": 0.5358904452390829, "J_D_1KI": 0.0035290544365139706}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 159777, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 12.599762678146362, "TIME_S_1KI": 0.0788584256691912, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1625.0090211582183, "W": 118.91, "J_1KI": 10.17048149081669, "W_1KI": 0.7442247632637988, "W_D": 83.03999999999999, "J_D": 1134.81413772583, "W_D_1KI": 0.5197243658348824, "J_D_1KI": 0.0032528108916482492}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.output
index f6f6dab..cbddaed 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.027439117431640625}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.028786182403564453}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     55,    107,  ..., 249902, 249955,
+tensor(crow_indices=tensor([     0,     59,    104,  ..., 249898, 249953,
                             250000]),
-       col_indices=tensor([  26,  155,  397,  ..., 4652, 4756, 4760]),
-       values=tensor([0.9134, 0.8993, 0.8423,  ..., 0.2444, 0.0288, 0.7023]),
+       col_indices=tensor([ 221,  274,  328,  ..., 4878, 4976, 4999]),
+       values=tensor([0.0342, 0.1493, 0.9761,  ..., 0.6975, 0.3452, 0.2059]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3636, 0.7288, 0.4529,  ..., 0.6147, 0.2907, 0.3015])
+tensor([0.5412, 0.2977, 0.2231,  ..., 0.9873, 0.5104, 0.9074])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 0.027439117431640625 seconds
+Time: 0.028786182403564453 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '38266', '-ss', '5000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.645953893661499}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '36475', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.560610294342041}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     49,     92,  ..., 249897, 249949,
+tensor(crow_indices=tensor([     0,     55,    109,  ..., 249892, 249939,
                             250000]),
-       col_indices=tensor([ 507,  568,  655,  ..., 4839, 4844, 4959]),
-       values=tensor([0.4543, 0.3787, 0.6932,  ..., 0.4487, 0.3087, 0.1431]),
+       col_indices=tensor([  97,  194,  218,  ..., 4655, 4840, 4910]),
+       values=tensor([0.5896, 0.1215, 0.2623,  ..., 0.5727, 0.7694, 0.3887]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.4167, 0.3931, 0.0326,  ..., 0.8288, 0.4472, 0.1506])
+tensor([0.3335, 0.3388, 0.8775,  ..., 0.7612, 0.9315, 0.4075])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 2.645953893661499 seconds
+Time: 2.560610294342041 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '151851', '-ss', '5000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.677409887313843}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '149568', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 9.829088926315308}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     62,    123,  ..., 249890, 249948,
+tensor(crow_indices=tensor([     0,     53,    101,  ..., 249907, 249953,
                             250000]),
-       col_indices=tensor([  53,   60,  101,  ..., 4781, 4787, 4941]),
-       values=tensor([0.8546, 0.9316, 0.6470,  ..., 0.1212, 0.6179, 0.4318]),
+       col_indices=tensor([  20,  135,  201,  ..., 4697, 4718, 4926]),
+       values=tensor([0.9828, 0.0133, 0.5809,  ..., 0.0122, 0.8671, 0.9428]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7208, 0.6556, 0.2590,  ..., 0.8294, 0.6979, 0.2347])
+tensor([0.3418, 0.3993, 0.8591,  ..., 0.6204, 0.2914, 0.3019])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.677409887313843 seconds
+Time: 9.829088926315308 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '159777', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 12.599762678146362}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     62,    123,  ..., 249890, 249948,
+tensor(crow_indices=tensor([     0,     55,    105,  ..., 249903, 249945,
                             250000]),
-       col_indices=tensor([  53,   60,  101,  ..., 4781, 4787, 4941]),
-       values=tensor([0.8546, 0.9316, 0.6470,  ..., 0.1212, 0.6179, 0.4318]),
+       col_indices=tensor([ 114,  208,  220,  ..., 4787, 4793, 4955]),
+       values=tensor([0.0792, 0.6264, 0.4827,  ..., 0.0840, 0.3114, 0.5425]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7208, 0.6556, 0.2590,  ..., 0.8294, 0.6979, 0.2347])
+tensor([0.4452, 0.0456, 0.3361,  ..., 0.6556, 0.9661, 0.5009])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +76,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.677409887313843 seconds
+Time: 12.599762678146362 seconds
 
-[39.83, 39.62, 39.22, 39.2, 39.58, 39.98, 44.8, 39.52, 39.58, 39.44]
-[117.47]
-12.290358543395996
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 151851, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.677409887313843, 'TIME_S_1KI': 0.07031504492768466, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.7484180927277, 'W': 117.47}
-[39.83, 39.62, 39.22, 39.2, 39.58, 39.98, 44.8, 39.52, 39.58, 39.44, 40.11, 39.32, 39.47, 44.63, 39.38, 39.24, 39.42, 39.88, 39.66, 39.4]
-721.8900000000001
-36.094500000000004
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 151851, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.677409887313843, 'TIME_S_1KI': 0.07031504492768466, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.7484180927277, 'W': 117.47, 'J_1KI': 9.507664869462351, 'W_1KI': 0.7735872664651534, 'W_D': 81.37549999999999, 'J_D': 1000.1340716481208, 'W_D_1KI': 0.5358904452390829, 'J_D_1KI': 0.0035290544365139706}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     55,    105,  ..., 249903, 249945,
+                            250000]),
+       col_indices=tensor([ 114,  208,  220,  ..., 4787, 4793, 4955]),
+       values=tensor([0.0792, 0.6264, 0.4827,  ..., 0.0840, 0.3114, 0.5425]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4452, 0.0456, 0.3361,  ..., 0.6556, 0.9661, 0.5009])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 12.599762678146362 seconds
+
+[41.42, 39.73, 39.61, 39.49, 39.71, 39.39, 39.69, 39.84, 39.88, 39.81]
+[118.91]
+13.665873527526855
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 159777, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 12.599762678146362, 'TIME_S_1KI': 0.0788584256691912, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1625.0090211582183, 'W': 118.91}
+[41.42, 39.73, 39.61, 39.49, 39.71, 39.39, 39.69, 39.84, 39.88, 39.81, 40.25, 41.73, 39.59, 39.81, 39.53, 39.41, 39.53, 39.92, 39.91, 39.78]
+717.4000000000001
+35.870000000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 159777, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 12.599762678146362, 'TIME_S_1KI': 0.0788584256691912, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1625.0090211582183, 'W': 118.91, 'J_1KI': 10.17048149081669, 'W_1KI': 0.7442247632637988, 'W_D': 83.03999999999999, 'J_D': 1134.81413772583, 'W_D_1KI': 0.5197243658348824, 'J_D_1KI': 0.0032528108916482492}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json
index 0dcc054..0c87859 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 91742, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.460400104522705, "TIME_S_1KI": 0.11401975218027409, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1797.7089305996894, "W": 131.45, "J_1KI": 19.595266405786766, "W_1KI": 1.4328224804342613, "W_D": 95.54724999999999, "J_D": 1306.7032683091759, "W_D_1KI": 1.0414777310283185, "J_D_1KI": 0.011352245765607012}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 91807, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.539710760116577, "TIME_S_1KI": 0.11480291001902444, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1775.26453387022, "W": 132.79, "J_1KI": 19.33691912240047, "W_1KI": 1.446403868986025, "W_D": 96.68149999999999, "J_D": 1292.5313504885432, "W_D_1KI": 1.0530950798958683, "J_D_1KI": 0.011470749288135636}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.output
index ee62fae..5e9885f 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03591585159301758}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03616905212402344}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     282,     523,  ..., 1249534,
-                            1249768, 1250000]),
-       col_indices=tensor([  14,   20,   65,  ..., 4981, 4988, 4994]),
-       values=tensor([0.5427, 0.7626, 0.3688,  ..., 0.1462, 0.4395, 0.5084]),
+tensor(crow_indices=tensor([      0,     239,     475,  ..., 1249454,
+                            1249729, 1250000]),
+       col_indices=tensor([  10,   41,   59,  ..., 4943, 4952, 4982]),
+       values=tensor([0.5383, 0.7420, 0.9700,  ..., 0.7402, 0.6039, 0.9535]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.3604, 0.6234, 0.1526,  ..., 0.4035, 0.4868, 0.5530])
+tensor([0.9383, 0.0034, 0.5962,  ..., 0.7530, 0.7746, 0.7053])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,39 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 0.03591585159301758 seconds
+Time: 0.03616905212402344 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '29235', '-ss', '5000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 3.345984697341919}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '29030', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 3.320157527923584}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     247,     522,  ..., 1249509,
-                            1249766, 1250000]),
-       col_indices=tensor([  11,   41,   47,  ..., 4983, 4993, 4996]),
-       values=tensor([0.4860, 0.1371, 0.2214,  ..., 0.6634, 0.1469, 0.9637]),
-       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.5776, 0.4725, 0.0368,  ..., 0.6036, 0.3775, 0.0011])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 1250000
-Density: 0.05
-Time: 3.345984697341919 seconds
-
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '91742', '-ss', '5000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.460400104522705}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     245,     499,  ..., 1249503,
+tensor(crow_indices=tensor([      0,     264,     493,  ..., 1249506,
                             1249746, 1250000]),
-       col_indices=tensor([  11,   13,   56,  ..., 4961, 4967, 4994]),
-       values=tensor([0.1643, 0.9353, 0.3976,  ..., 0.1683, 0.6963, 0.8462]),
+       col_indices=tensor([  38,   65,   78,  ..., 4953, 4995, 4997]),
+       values=tensor([0.9639, 0.6446, 0.1864,  ..., 0.7835, 0.7572, 0.9616]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.6649, 0.4073, 0.4211,  ..., 0.1078, 0.2188, 0.0388])
+tensor([0.0443, 0.2357, 0.4128,  ..., 0.0404, 0.6082, 0.2175])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.460400104522705 seconds
+Time: 3.320157527923584 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '91807', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.539710760116577}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     245,     499,  ..., 1249503,
-                            1249746, 1250000]),
-       col_indices=tensor([  11,   13,   56,  ..., 4961, 4967, 4994]),
-       values=tensor([0.1643, 0.9353, 0.3976,  ..., 0.1683, 0.6963, 0.8462]),
+tensor(crow_indices=tensor([      0,     239,     477,  ..., 1249471,
+                            1249754, 1250000]),
+       col_indices=tensor([   7,   12,   31,  ..., 4957, 4958, 4966]),
+       values=tensor([0.8697, 0.7101, 0.2841,  ..., 0.5399, 0.7906, 0.6573]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.6649, 0.4073, 0.4211,  ..., 0.1078, 0.2188, 0.0388])
+tensor([0.4895, 0.4712, 0.1549,  ..., 0.9337, 0.8190, 0.0696])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +56,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.460400104522705 seconds
+Time: 10.539710760116577 seconds
 
-[40.58, 39.92, 39.93, 39.37, 39.59, 39.9, 39.71, 40.07, 40.76, 39.67]
-[131.45]
-13.675990343093872
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91742, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.460400104522705, 'TIME_S_1KI': 0.11401975218027409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1797.7089305996894, 'W': 131.45}
-[40.58, 39.92, 39.93, 39.37, 39.59, 39.9, 39.71, 40.07, 40.76, 39.67, 40.34, 39.45, 39.48, 40.31, 39.86, 39.84, 39.9, 39.88, 39.9, 39.78]
-718.055
-35.90275
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91742, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.460400104522705, 'TIME_S_1KI': 0.11401975218027409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1797.7089305996894, 'W': 131.45, 'J_1KI': 19.595266405786766, 'W_1KI': 1.4328224804342613, 'W_D': 95.54724999999999, 'J_D': 1306.7032683091759, 'W_D_1KI': 1.0414777310283185, 'J_D_1KI': 0.011352245765607012}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     239,     477,  ..., 1249471,
+                            1249754, 1250000]),
+       col_indices=tensor([   7,   12,   31,  ..., 4957, 4958, 4966]),
+       values=tensor([0.8697, 0.7101, 0.2841,  ..., 0.5399, 0.7906, 0.6573]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.4895, 0.4712, 0.1549,  ..., 0.9337, 0.8190, 0.0696])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.539710760116577 seconds
+
+[41.24, 39.56, 39.78, 39.5, 40.15, 40.08, 40.05, 39.48, 39.7, 39.48]
+[132.79]
+13.368962526321411
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91807, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.539710760116577, 'TIME_S_1KI': 0.11480291001902444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1775.26453387022, 'W': 132.79}
+[41.24, 39.56, 39.78, 39.5, 40.15, 40.08, 40.05, 39.48, 39.7, 39.48, 40.21, 39.89, 39.7, 39.85, 39.71, 40.16, 40.06, 41.81, 39.99, 44.47]
+722.1700000000001
+36.10850000000001
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91807, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.539710760116577, 'TIME_S_1KI': 0.11480291001902444, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1775.26453387022, 'W': 132.79, 'J_1KI': 19.33691912240047, 'W_1KI': 1.446403868986025, 'W_D': 96.68149999999999, 'J_D': 1292.5313504885432, 'W_D_1KI': 1.0530950798958683, 'J_D_1KI': 0.011470749288135636}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json
index 44fa72d..987b9ad 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 52297, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.236442565917969, "TIME_S_1KI": 0.195736706998833, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1815.1719595336915, "W": 138.62, "J_1KI": 34.70891178334688, "W_1KI": 2.650630055261296, "W_D": 102.202, "J_D": 1338.293208831787, "W_D_1KI": 1.9542612386943803, "J_D_1KI": 0.0373685151862321}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 52912, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.354979991912842, "TIME_S_1KI": 0.19570192001649608, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1873.7867654466627, "W": 137.14, "J_1KI": 35.41326665872888, "W_1KI": 2.5918506198971873, "W_D": 101.08874999999999, "J_D": 1381.2072472330926, "W_D_1KI": 1.9105070683398848, "J_D_1KI": 0.03610725484464554}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.output
index 4fe4648..4ac2b45 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.053244590759277344}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.057938575744628906}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     487,     976,  ..., 2498986,
-                            2499481, 2500000]),
-       col_indices=tensor([  13,   19,   40,  ..., 4975, 4977, 4981]),
-       values=tensor([0.0276, 0.4992, 0.8339,  ..., 0.1235, 0.3053, 0.8819]),
+tensor(crow_indices=tensor([      0,     516,     999,  ..., 2499028,
+                            2499504, 2500000]),
+       col_indices=tensor([  10,   15,   27,  ..., 4971, 4978, 4980]),
+       values=tensor([0.9206, 0.4241, 0.6810,  ..., 0.1765, 0.0854, 0.9488]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6982, 0.6943, 0.5654,  ..., 0.0343, 0.1924, 0.4615])
+tensor([0.2709, 0.2891, 0.7340,  ..., 0.3721, 0.9776, 0.3534])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 0.053244590759277344 seconds
+Time: 0.057938575744628906 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19720', '-ss', '5000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 3.959235429763794}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18122', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 3.59611177444458}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     500,     997,  ..., 2499001,
-                            2499525, 2500000]),
-       col_indices=tensor([  22,   31,   36,  ..., 4976, 4979, 4990]),
-       values=tensor([0.9139, 0.4529, 0.5623,  ..., 0.7413, 0.5022, 0.1210]),
+tensor(crow_indices=tensor([      0,     480,     982,  ..., 2499049,
+                            2499529, 2500000]),
+       col_indices=tensor([   5,   11,   37,  ..., 4957, 4962, 4990]),
+       values=tensor([0.8715, 0.2207, 0.6494,  ..., 0.4488, 0.1939, 0.3906]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.1909, 0.5057, 0.7269,  ..., 0.6307, 0.9165, 0.6325])
+tensor([0.4996, 0.4879, 0.9575,  ..., 0.2501, 0.0484, 0.4784])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 3.959235429763794 seconds
+Time: 3.59611177444458 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52297', '-ss', '5000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.236442565917969}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52912', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.354979991912842}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     495,     967,  ..., 2499009,
-                            2499501, 2500000]),
-       col_indices=tensor([   2,    3,   29,  ..., 4974, 4984, 4998]),
-       values=tensor([0.7947, 0.6825, 0.2906,  ..., 0.1208, 0.9049, 0.2265]),
+tensor(crow_indices=tensor([      0,     512,    1033,  ..., 2498964,
+                            2499478, 2500000]),
+       col_indices=tensor([   2,   17,   23,  ..., 4986, 4995, 4996]),
+       values=tensor([0.3497, 0.1928, 0.5659,  ..., 0.9965, 0.8054, 0.3638]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7067, 0.8426, 0.8818,  ..., 0.1022, 0.5608, 0.1343])
+tensor([0.5347, 0.2716, 0.2224,  ..., 0.2917, 0.3905, 0.3272])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.236442565917969 seconds
+Time: 10.354979991912842 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     495,     967,  ..., 2499009,
-                            2499501, 2500000]),
-       col_indices=tensor([   2,    3,   29,  ..., 4974, 4984, 4998]),
-       values=tensor([0.7947, 0.6825, 0.2906,  ..., 0.1208, 0.9049, 0.2265]),
+tensor(crow_indices=tensor([      0,     512,    1033,  ..., 2498964,
+                            2499478, 2500000]),
+       col_indices=tensor([   2,   17,   23,  ..., 4986, 4995, 4996]),
+       values=tensor([0.3497, 0.1928, 0.5659,  ..., 0.9965, 0.8054, 0.3638]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7067, 0.8426, 0.8818,  ..., 0.1022, 0.5608, 0.1343])
+tensor([0.5347, 0.2716, 0.2224,  ..., 0.2917, 0.3905, 0.3272])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.236442565917969 seconds
+Time: 10.354979991912842 seconds
 
-[40.97, 39.52, 39.62, 39.4, 39.87, 45.09, 39.68, 39.56, 39.77, 39.67]
-[138.62]
-13.094589233398438
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52297, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.236442565917969, 'TIME_S_1KI': 0.195736706998833, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1815.1719595336915, 'W': 138.62}
-[40.97, 39.52, 39.62, 39.4, 39.87, 45.09, 39.68, 39.56, 39.77, 39.67, 41.69, 43.46, 41.5, 39.76, 39.97, 39.88, 39.62, 39.85, 40.84, 39.61]
-728.3600000000001
-36.418000000000006
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52297, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.236442565917969, 'TIME_S_1KI': 0.195736706998833, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1815.1719595336915, 'W': 138.62, 'J_1KI': 34.70891178334688, 'W_1KI': 2.650630055261296, 'W_D': 102.202, 'J_D': 1338.293208831787, 'W_D_1KI': 1.9542612386943803, 'J_D_1KI': 0.0373685151862321}
+[40.41, 39.61, 40.39, 39.58, 41.83, 39.58, 39.84, 39.74, 39.57, 39.81]
+[137.14]
+13.663313150405884
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52912, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.354979991912842, 'TIME_S_1KI': 0.19570192001649608, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1873.7867654466627, 'W': 137.14}
+[40.41, 39.61, 40.39, 39.58, 41.83, 39.58, 39.84, 39.74, 39.57, 39.81, 40.38, 40.03, 40.18, 40.08, 39.88, 40.18, 40.14, 39.98, 39.77, 40.69]
+721.025
+36.051249999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52912, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.354979991912842, 'TIME_S_1KI': 0.19570192001649608, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1873.7867654466627, 'W': 137.14, 'J_1KI': 35.41326665872888, 'W_1KI': 2.5918506198971873, 'W_D': 101.08874999999999, 'J_D': 1381.2072472330926, 'W_D_1KI': 1.9105070683398848, 'J_D_1KI': 0.03610725484464554}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.json
index c26577c..870c328 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28289, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.465468645095825, "TIME_S_1KI": 0.36994834193841514, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1925.348158209324, "W": 138.77, "J_1KI": 68.05995822437428, "W_1KI": 4.905440277139524, "W_D": 102.84225, "J_D": 1426.8727867954374, "W_D_1KI": 3.6354148255505674, "J_D_1KI": 0.1285098386493184}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28846, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.404276132583618, "TIME_S_1KI": 0.3606834962415454, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1932.1177334833146, "W": 139.74, "J_1KI": 66.98043865642774, "W_1KI": 4.8443458365111285, "W_D": 103.11100000000002, "J_D": 1425.6661773092749, "W_D_1KI": 3.5745337308465652, "J_D_1KI": 0.12391783023110882}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.output
index 4cf3fef..66e4164 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.07305312156677246}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.0717768669128418}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1021,    1969,  ..., 4997949,
-                            4999001, 5000000]),
-       col_indices=tensor([   1,    7,   18,  ..., 4990, 4995, 4998]),
-       values=tensor([0.6231, 0.9951, 0.8252,  ..., 0.9756, 0.2344, 0.2983]),
+tensor(crow_indices=tensor([      0,     982,    1957,  ..., 4997963,
+                            4998987, 5000000]),
+       col_indices=tensor([   5,    6,    7,  ..., 4993, 4997, 4999]),
+       values=tensor([0.8941, 0.9992, 0.8012,  ..., 0.3746, 0.6607, 0.5735]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.0355, 0.1933, 0.9788,  ..., 0.6197, 0.2365, 0.0393])
+tensor([0.3587, 0.9276, 0.9527,  ..., 0.4878, 0.1315, 0.8391])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 0.07305312156677246 seconds
+Time: 0.0717768669128418 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '14373', '-ss', '5000', '-sd', '0.2']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 5.334789037704468}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '14628', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 5.324462413787842}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1024,    2040,  ..., 4998012,
-                            4999011, 5000000]),
-       col_indices=tensor([   1,    7,   11,  ..., 4993, 4994, 4998]),
-       values=tensor([0.0452, 0.7491, 0.1728,  ..., 0.4616, 0.1426, 0.7347]),
+tensor(crow_indices=tensor([      0,     969,    2010,  ..., 4998017,
+                            4998963, 5000000]),
+       col_indices=tensor([   2,    7,   11,  ..., 4968, 4973, 4981]),
+       values=tensor([0.1932, 0.9028, 0.0773,  ..., 0.1884, 0.2681, 0.3774]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.8794, 0.6343, 0.4463,  ..., 0.6355, 0.8597, 0.5087])
+tensor([0.7410, 0.6895, 0.0960,  ..., 0.1902, 0.8343, 0.6611])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 5.334789037704468 seconds
+Time: 5.324462413787842 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28289', '-ss', '5000', '-sd', '0.2']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.465468645095825}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28846', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.404276132583618}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     999,    2016,  ..., 4997966,
-                            4998976, 5000000]),
-       col_indices=tensor([   2,    9,   14,  ..., 4993, 4994, 4995]),
-       values=tensor([0.1135, 0.5000, 0.4923,  ..., 0.1880, 0.5290, 0.9229]),
+tensor(crow_indices=tensor([      0,     985,    2039,  ..., 4997936,
+                            4998963, 5000000]),
+       col_indices=tensor([   8,   12,   28,  ..., 4988, 4996, 4997]),
+       values=tensor([0.4804, 0.1658, 0.3798,  ..., 0.8036, 0.6527, 0.9757]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.0610, 0.8156, 0.2755,  ..., 0.7165, 0.9008, 0.9624])
+tensor([0.2723, 0.5864, 0.6330,  ..., 0.7738, 0.3370, 0.9006])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.465468645095825 seconds
+Time: 10.404276132583618 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     999,    2016,  ..., 4997966,
-                            4998976, 5000000]),
-       col_indices=tensor([   2,    9,   14,  ..., 4993, 4994, 4995]),
-       values=tensor([0.1135, 0.5000, 0.4923,  ..., 0.1880, 0.5290, 0.9229]),
+tensor(crow_indices=tensor([      0,     985,    2039,  ..., 4997936,
+                            4998963, 5000000]),
+       col_indices=tensor([   8,   12,   28,  ..., 4988, 4996, 4997]),
+       values=tensor([0.4804, 0.1658, 0.3798,  ..., 0.8036, 0.6527, 0.9757]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.0610, 0.8156, 0.2755,  ..., 0.7165, 0.9008, 0.9624])
+tensor([0.2723, 0.5864, 0.6330,  ..., 0.7738, 0.3370, 0.9006])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.465468645095825 seconds
+Time: 10.404276132583618 seconds
 
-[40.8, 40.7, 39.71, 40.23, 39.76, 39.64, 39.79, 39.68, 39.73, 39.72]
-[138.77]
-13.874383211135864
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28289, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.465468645095825, 'TIME_S_1KI': 0.36994834193841514, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1925.348158209324, 'W': 138.77}
-[40.8, 40.7, 39.71, 40.23, 39.76, 39.64, 39.79, 39.68, 39.73, 39.72, 40.37, 39.65, 40.3, 39.61, 40.3, 40.15, 39.64, 39.78, 39.65, 39.58]
-718.555
-35.927749999999996
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28289, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.465468645095825, 'TIME_S_1KI': 0.36994834193841514, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1925.348158209324, 'W': 138.77, 'J_1KI': 68.05995822437428, 'W_1KI': 4.905440277139524, 'W_D': 102.84225, 'J_D': 1426.8727867954374, 'W_D_1KI': 3.6354148255505674, 'J_D_1KI': 0.1285098386493184}
+[41.19, 40.35, 39.91, 40.05, 41.57, 40.01, 40.37, 44.94, 40.26, 39.6]
+[139.74]
+13.826518774032593
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28846, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.404276132583618, 'TIME_S_1KI': 0.3606834962415454, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1932.1177334833146, 'W': 139.74}
+[41.19, 40.35, 39.91, 40.05, 41.57, 40.01, 40.37, 44.94, 40.26, 39.6, 41.27, 39.63, 39.67, 45.04, 39.7, 39.68, 40.15, 40.06, 40.17, 39.98]
+732.5799999999999
+36.629
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28846, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.404276132583618, 'TIME_S_1KI': 0.3606834962415454, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1932.1177334833146, 'W': 139.74, 'J_1KI': 66.98043865642774, 'W_1KI': 4.8443458365111285, 'W_D': 103.11100000000002, 'J_D': 1425.6661773092749, 'W_D_1KI': 3.5745337308465652, 'J_D_1KI': 0.12391783023110882}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.json
index edb21c0..4f78c17 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19365, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.708929538726807, "TIME_S_1KI": 0.5530043655423086, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1994.3257465744018, "W": 137.56, "J_1KI": 102.98609587267761, "W_1KI": 7.103537309579138, "W_D": 101.59625, "J_D": 1472.9283013260365, "W_D_1KI": 5.246385231087013, "J_D_1KI": 0.270921003412704}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19144, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.589388370513916, "TIME_S_1KI": 0.5531439809085832, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2009.800785241127, "W": 137.31, "J_1KI": 104.98332559763514, "W_1KI": 7.172482239866277, "W_D": 101.2175, "J_D": 1481.5163570034504, "W_D_1KI": 5.287165691600501, "J_D_1KI": 0.27617873441289703}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.output
index 4cf90e5..32dc9a9 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.0932457447052002}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.09389424324035645}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1472,    2953,  ..., 7496947,
-                            7498464, 7500000]),
-       col_indices=tensor([   7,    9,   10,  ..., 4989, 4991, 4994]),
-       values=tensor([0.9868, 0.9731, 0.3711,  ..., 0.9277, 0.6596, 0.4560]),
+tensor(crow_indices=tensor([      0,    1453,    2925,  ..., 7497044,
+                            7498511, 7500000]),
+       col_indices=tensor([   2,    8,   11,  ..., 4986, 4987, 4995]),
+       values=tensor([0.6050, 0.7562, 0.0795,  ..., 0.5439, 0.7811, 0.7483]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.4150, 0.1407, 0.7534,  ..., 0.5098, 0.0887, 0.6433])
+tensor([0.3581, 0.7659, 0.8458,  ..., 0.9868, 0.1607, 0.0117])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 0.0932457447052002 seconds
+Time: 0.09389424324035645 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '11260', '-ss', '5000', '-sd', '0.3']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 6.105090618133545}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '11182', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 6.13297963142395}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1440,    2968,  ..., 7497040,
-                            7498552, 7500000]),
-       col_indices=tensor([   4,   15,   16,  ..., 4993, 4997, 4998]),
-       values=tensor([0.0079, 0.6033, 0.5837,  ..., 0.4070, 0.1537, 0.2862]),
+tensor(crow_indices=tensor([      0,    1535,    3010,  ..., 7496960,
+                            7498491, 7500000]),
+       col_indices=tensor([   0,    1,    4,  ..., 4993, 4997, 4999]),
+       values=tensor([0.0177, 0.3777, 0.8712,  ..., 0.1638, 0.9917, 0.2791]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.9942, 0.8351, 0.2634,  ..., 0.1203, 0.3761, 0.2393])
+tensor([0.5968, 0.6885, 0.5169,  ..., 0.8778, 0.7014, 0.4474])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 6.105090618133545 seconds
+Time: 6.13297963142395 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19365', '-ss', '5000', '-sd', '0.3']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.708929538726807}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19144', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.589388370513916}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1483,    3029,  ..., 7497007,
-                            7498484, 7500000]),
-       col_indices=tensor([   0,    1,    2,  ..., 4991, 4992, 4999]),
-       values=tensor([0.2037, 0.3378, 0.5245,  ..., 0.5597, 0.6700, 0.6684]),
+tensor(crow_indices=tensor([      0,    1480,    2995,  ..., 7496986,
+                            7498507, 7500000]),
+       col_indices=tensor([   2,    7,   10,  ..., 4984, 4996, 4997]),
+       values=tensor([0.8500, 0.4156, 0.3807,  ..., 0.4612, 0.1880, 0.4263]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.7739, 0.8705, 0.9400,  ..., 0.4166, 0.9328, 0.4141])
+tensor([0.5811, 0.5191, 0.7210,  ..., 0.8257, 0.8574, 0.1320])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.708929538726807 seconds
+Time: 10.589388370513916 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1483,    3029,  ..., 7497007,
-                            7498484, 7500000]),
-       col_indices=tensor([   0,    1,    2,  ..., 4991, 4992, 4999]),
-       values=tensor([0.2037, 0.3378, 0.5245,  ..., 0.5597, 0.6700, 0.6684]),
+tensor(crow_indices=tensor([      0,    1480,    2995,  ..., 7496986,
+                            7498507, 7500000]),
+       col_indices=tensor([   2,    7,   10,  ..., 4984, 4996, 4997]),
+       values=tensor([0.8500, 0.4156, 0.3807,  ..., 0.4612, 0.1880, 0.4263]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.7739, 0.8705, 0.9400,  ..., 0.4166, 0.9328, 0.4141])
+tensor([0.5811, 0.5191, 0.7210,  ..., 0.8257, 0.8574, 0.1320])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.708929538726807 seconds
+Time: 10.589388370513916 seconds
 
-[42.29, 39.85, 39.72, 39.53, 39.58, 40.0, 40.36, 39.5, 39.8, 40.06]
-[137.56]
-14.4978609085083
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19365, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.708929538726807, 'TIME_S_1KI': 0.5530043655423086, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1994.3257465744018, 'W': 137.56}
-[42.29, 39.85, 39.72, 39.53, 39.58, 40.0, 40.36, 39.5, 39.8, 40.06, 40.39, 40.11, 40.04, 39.88, 41.07, 39.92, 39.57, 39.46, 39.73, 39.57]
-719.275
-35.96375
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19365, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.708929538726807, 'TIME_S_1KI': 0.5530043655423086, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1994.3257465744018, 'W': 137.56, 'J_1KI': 102.98609587267761, 'W_1KI': 7.103537309579138, 'W_D': 101.59625, 'J_D': 1472.9283013260365, 'W_D_1KI': 5.246385231087013, 'J_D_1KI': 0.270921003412704}
+[41.93, 40.2, 39.72, 39.76, 39.9, 40.29, 40.01, 40.65, 39.8, 39.55]
+[137.31]
+14.636958599090576
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19144, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.589388370513916, 'TIME_S_1KI': 0.5531439809085832, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2009.800785241127, 'W': 137.31}
+[41.93, 40.2, 39.72, 39.76, 39.9, 40.29, 40.01, 40.65, 39.8, 39.55, 40.75, 39.74, 39.86, 39.82, 39.94, 40.37, 39.63, 40.91, 40.17, 39.93]
+721.85
+36.0925
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19144, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.589388370513916, 'TIME_S_1KI': 0.5531439809085832, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2009.800785241127, 'W': 137.31, 'J_1KI': 104.98332559763514, 'W_1KI': 7.172482239866277, 'W_D': 101.2175, 'J_D': 1481.5163570034504, 'W_D_1KI': 5.287165691600501, 'J_D_1KI': 0.27617873441289703}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.json
index d0ddde8..b2ebd01 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4054, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.937983989715576, "TIME_S_1KI": 2.6980720250901764, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1733.5753089761736, "W": 118.18, "J_1KI": 427.62094449338275, "W_1KI": 29.15145535273804, "W_D": 74.48075000000001, "J_D": 1092.5536401593092, "W_D_1KI": 18.37216329551061, "J_D_1KI": 4.531860704368675}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 5230, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.803205013275146, "TIME_S_1KI": 2.0656223734751715, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1996.9131930828094, "W": 124.05, "J_1KI": 381.8189661726213, "W_1KI": 23.718929254302104, "W_D": 87.92925, "J_D": 1415.454086117506, "W_D_1KI": 16.812476099426387, "J_D_1KI": 3.214622581152273}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.output
index fbc9c8c..e008023 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.25896668434143066}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.2405378818511963}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1967,     3942,  ...,  9996007,
-                             9997987, 10000000]),
-       col_indices=tensor([   0,    1,    3,  ..., 4989, 4995, 4996]),
-       values=tensor([0.9037, 0.0824, 0.8127,  ..., 0.2074, 0.6033, 0.5497]),
+tensor(crow_indices=tensor([       0,     2034,     4065,  ...,  9995925,
+                             9997951, 10000000]),
+       col_indices=tensor([   0,    3,    4,  ..., 4996, 4997, 4999]),
+       values=tensor([0.2630, 0.0419, 0.7378,  ..., 0.8916, 0.9324, 0.9315]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.6227, 0.6614, 0.9902,  ..., 0.2660, 0.9614, 0.3260])
+tensor([0.7523, 0.5983, 0.1032,  ..., 0.8622, 0.3372, 0.3026])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 0.25896668434143066 seconds
+Time: 0.2405378818511963 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4054', '-ss', '5000', '-sd', '0.4']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.937983989715576}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4365', '-ss', '5000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 8.763044357299805}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2032,     3978,  ...,  9995983,
-                             9998000, 10000000]),
-       col_indices=tensor([   1,    2,    3,  ..., 4988, 4993, 4994]),
-       values=tensor([0.2457, 0.5907, 0.9941,  ..., 0.3357, 0.2301, 0.2269]),
+tensor(crow_indices=tensor([       0,     1961,     3998,  ...,  9995938,
+                             9997917, 10000000]),
+       col_indices=tensor([   3,    4,    5,  ..., 4996, 4998, 4999]),
+       values=tensor([0.0989, 0.8910, 0.1341,  ..., 0.2098, 0.3959, 0.3132]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.2230, 0.0807, 0.5998,  ..., 0.1430, 0.1498, 0.1360])
+tensor([0.4158, 0.1466, 0.9233,  ..., 0.3419, 0.5659, 0.6621])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,16 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 10.937983989715576 seconds
+Time: 8.763044357299805 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '5230', '-ss', '5000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.803205013275146}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2032,     3978,  ...,  9995983,
-                             9998000, 10000000]),
-       col_indices=tensor([   1,    2,    3,  ..., 4988, 4993, 4994]),
-       values=tensor([0.2457, 0.5907, 0.9941,  ..., 0.3357, 0.2301, 0.2269]),
+tensor(crow_indices=tensor([       0,     1987,     4084,  ...,  9995959,
+                             9998006, 10000000]),
+       col_indices=tensor([   5,    9,   13,  ..., 4991, 4995, 4998]),
+       values=tensor([0.2998, 0.3930, 0.1334,  ..., 0.0607, 0.7503, 0.6485]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.2230, 0.0807, 0.5998,  ..., 0.1430, 0.1498, 0.1360])
+tensor([0.5362, 0.2142, 0.9699,  ..., 0.8371, 0.3959, 0.8353])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,13 +56,30 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 10.937983989715576 seconds
+Time: 10.803205013275146 seconds
 
-[40.89, 40.0, 39.72, 39.6, 39.91, 39.64, 39.68, 39.81, 39.88, 45.41]
-[118.18]
-14.66893982887268
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4054, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.937983989715576, 'TIME_S_1KI': 2.6980720250901764, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1733.5753089761736, 'W': 118.18}
-[40.89, 40.0, 39.72, 39.6, 39.91, 39.64, 39.68, 39.81, 39.88, 45.41, 40.78, 40.13, 44.65, 73.6, 81.22, 73.52, 67.52, 42.65, 48.49, 40.85]
-873.9849999999999
-43.69924999999999
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4054, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.937983989715576, 'TIME_S_1KI': 2.6980720250901764, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1733.5753089761736, 'W': 118.18, 'J_1KI': 427.62094449338275, 'W_1KI': 29.15145535273804, 'W_D': 74.48075000000001, 'J_D': 1092.5536401593092, 'W_D_1KI': 18.37216329551061, 'J_D_1KI': 4.531860704368675}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1987,     4084,  ...,  9995959,
+                             9998006, 10000000]),
+       col_indices=tensor([   5,    9,   13,  ..., 4991, 4995, 4998]),
+       values=tensor([0.2998, 0.3930, 0.1334,  ..., 0.0607, 0.7503, 0.6485]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.5362, 0.2142, 0.9699,  ..., 0.8371, 0.3959, 0.8353])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.803205013275146 seconds
+
+[41.24, 40.18, 39.91, 41.32, 39.92, 41.22, 40.71, 39.64, 39.82, 39.65]
+[124.05]
+16.097647666931152
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 5230, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.803205013275146, 'TIME_S_1KI': 2.0656223734751715, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1996.9131930828094, 'W': 124.05}
+[41.24, 40.18, 39.91, 41.32, 39.92, 41.22, 40.71, 39.64, 39.82, 39.65, 41.69, 39.74, 40.19, 39.63, 40.0, 39.64, 39.86, 39.81, 39.72, 39.63]
+722.415
+36.12075
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 5230, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.803205013275146, 'TIME_S_1KI': 2.0656223734751715, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1996.9131930828094, 'W': 124.05, 'J_1KI': 381.8189661726213, 'W_1KI': 23.718929254302104, 'W_D': 87.92925, 'J_D': 1415.454086117506, 'W_D_1KI': 16.812476099426387, 'J_D_1KI': 3.214622581152273}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.json
index a25c6eb..cf977fd 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3758, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.1781005859375, "TIME_S_1KI": 2.708382274065327, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1956.303444838524, "W": 122.9, "J_1KI": 520.570368504131, "W_1KI": 32.70356572645024, "W_D": 86.40525000000001, "J_D": 1375.3855836219193, "W_D_1KI": 22.992349654071315, "J_D_1KI": 6.118240993632601}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3816, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.633152484893799, "TIME_S_1KI": 2.7864655358736368, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2084.05164506197, "W": 121.69, "J_1KI": 546.1351271126755, "W_1KI": 31.889412997903563, "W_D": 84.58225, "J_D": 1448.5477628033757, "W_D_1KI": 22.165159853249474, "J_D_1KI": 5.808480045400806}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.output
index fd072be..bc52cdb 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.29501914978027344}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.3140254020690918}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2490,     5019,  ..., 12495032,
-                            12497514, 12500000]),
-       col_indices=tensor([   0,    1,    2,  ..., 4992, 4993, 4994]),
-       values=tensor([0.4521, 0.6419, 0.1807,  ..., 0.6429, 0.2936, 0.1963]),
+tensor(crow_indices=tensor([       0,     2477,     4942,  ..., 12494970,
+                            12497427, 12500000]),
+       col_indices=tensor([   2,    9,   10,  ..., 4997, 4998, 4999]),
+       values=tensor([0.2607, 0.2897, 0.6113,  ..., 0.8033, 0.0026, 0.0427]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.6822, 0.2314, 0.5095,  ..., 0.2635, 0.2792, 0.8048])
+tensor([0.0281, 0.1769, 0.9631,  ..., 0.7506, 0.8075, 0.4975])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 0.29501914978027344 seconds
+Time: 0.3140254020690918 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3559', '-ss', '5000', '-sd', '0.5']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.942713260650635}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3343', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.196410894393921}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2478,     4936,  ..., 12495093,
-                            12497610, 12500000]),
-       col_indices=tensor([   1,    2,    3,  ..., 4992, 4995, 4998]),
-       values=tensor([0.6608, 0.6509, 0.8650,  ..., 0.2551, 0.6130, 0.3679]),
+tensor(crow_indices=tensor([       0,     2432,     4927,  ..., 12494982,
+                            12497476, 12500000]),
+       col_indices=tensor([   4,    6,    7,  ..., 4997, 4998, 4999]),
+       values=tensor([0.9429, 0.5134, 0.0149,  ..., 0.4540, 0.0443, 0.1429]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.9258, 0.3878, 0.0027,  ..., 0.4707, 0.4169, 0.1792])
+tensor([0.5035, 0.8624, 0.0188,  ..., 0.3732, 0.5744, 0.8509])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 9.942713260650635 seconds
+Time: 9.196410894393921 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3758', '-ss', '5000', '-sd', '0.5']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.1781005859375}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3816', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.633152484893799}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2527,     4930,  ..., 12495017,
-                            12497540, 12500000]),
-       col_indices=tensor([   1,    2,    3,  ..., 4995, 4997, 4999]),
-       values=tensor([0.2576, 0.5438, 0.7818,  ..., 0.1593, 0.4265, 0.7530]),
+tensor(crow_indices=tensor([       0,     2504,     4975,  ..., 12494911,
+                            12497444, 12500000]),
+       col_indices=tensor([   3,    4,    8,  ..., 4988, 4993, 4995]),
+       values=tensor([0.1232, 0.7788, 0.1132,  ..., 0.9068, 0.1197, 0.2972]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.0335, 0.1835, 0.7330,  ..., 0.5684, 0.8047, 0.4810])
+tensor([0.8363, 0.8796, 0.7824,  ..., 0.7053, 0.2246, 0.6262])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 10.1781005859375 seconds
+Time: 10.633152484893799 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2527,     4930,  ..., 12495017,
-                            12497540, 12500000]),
-       col_indices=tensor([   1,    2,    3,  ..., 4995, 4997, 4999]),
-       values=tensor([0.2576, 0.5438, 0.7818,  ..., 0.1593, 0.4265, 0.7530]),
+tensor(crow_indices=tensor([       0,     2504,     4975,  ..., 12494911,
+                            12497444, 12500000]),
+       col_indices=tensor([   3,    4,    8,  ..., 4988, 4993, 4995]),
+       values=tensor([0.1232, 0.7788, 0.1132,  ..., 0.9068, 0.1197, 0.2972]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.0335, 0.1835, 0.7330,  ..., 0.5684, 0.8047, 0.4810])
+tensor([0.8363, 0.8796, 0.7824,  ..., 0.7053, 0.2246, 0.6262])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 10.1781005859375 seconds
+Time: 10.633152484893799 seconds
 
-[41.47, 40.36, 39.99, 40.33, 39.95, 40.28, 39.98, 39.88, 39.78, 45.28]
-[122.9]
-15.917847394943237
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3758, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.1781005859375, 'TIME_S_1KI': 2.708382274065327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1956.303444838524, 'W': 122.9}
-[41.47, 40.36, 39.99, 40.33, 39.95, 40.28, 39.98, 39.88, 39.78, 45.28, 40.38, 39.63, 39.74, 44.87, 40.1, 40.15, 40.75, 40.1, 40.4, 40.08]
-729.895
-36.494749999999996
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3758, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.1781005859375, 'TIME_S_1KI': 2.708382274065327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1956.303444838524, 'W': 122.9, 'J_1KI': 520.570368504131, 'W_1KI': 32.70356572645024, 'W_D': 86.40525000000001, 'J_D': 1375.3855836219193, 'W_D_1KI': 22.992349654071315, 'J_D_1KI': 6.118240993632601}
+[40.51, 40.07, 40.7, 39.76, 40.61, 40.3, 40.25, 40.11, 56.38, 40.41]
+[121.69]
+17.12590718269348
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3816, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.633152484893799, 'TIME_S_1KI': 2.7864655358736368, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2084.05164506197, 'W': 121.69}
+[40.51, 40.07, 40.7, 39.76, 40.61, 40.3, 40.25, 40.11, 56.38, 40.41, 40.56, 39.77, 40.21, 39.71, 40.16, 40.19, 39.82, 40.88, 39.84, 45.31]
+742.155
+37.107749999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3816, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.633152484893799, 'TIME_S_1KI': 2.7864655358736368, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2084.05164506197, 'W': 121.69, 'J_1KI': 546.1351271126755, 'W_1KI': 31.889412997903563, 'W_D': 84.58225, 'J_D': 1448.5477628033757, 'W_D_1KI': 22.165159853249474, 'J_D_1KI': 5.808480045400806}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json
index c2948bd..2dcdfe3 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 505155, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.3298921585083, "TIME_S_1KI": 0.020448955584935914, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1240.863141503334, "W": 95.58, "J_1KI": 2.4564007908529737, "W_1KI": 0.1892092526056359, "W_D": 59.942499999999995, "J_D": 778.2008669131993, "W_D_1KI": 0.11866159891518444, "J_D_1KI": 0.0002349013647596964}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 496427, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.657760858535767, "TIME_S_1KI": 0.021468938753403354, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1249.036185359955, "W": 96.43, "J_1KI": 2.5160520788755543, "W_1KI": 0.19424809690045064, "W_D": 60.4765, "J_D": 783.3385550546647, "W_D_1KI": 0.12182355109613296, "J_D_1KI": 0.0002454007358506547}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output
index 107ac32..0a44d98 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,75 +1,75 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.013948440551757812}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.013672351837158203}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([3548, 3508, 4386, 3528, 2702, 3004, 3629, 4756, 1243,
-                            213, 2804, 1698,  689, 4639, 4580, 1578, 3327,  694,
-                           1408, 2610, 4665, 1701, 4464,  632, 2037, 2500, 1517,
-                           2177, 1389, 4628,  306, 1568, 3761, 3194, 3074, 2522,
-                           3705, 2681, 4246,  249, 1916, 3633, 4678, 1217,  107,
-                           2703, 1648, 2700, 2961, 4336, 1084, 4254,  396, 3740,
-                           3046, 2671, 2061, 1766, 3209, 4565, 1985, 2700, 4834,
-                           2805,  875, 2910, 2400, 2621, 4389,  955, 1399,  578,
-                           2242, 4964, 3239,  222, 1256, 3099, 3567, 2886, 3721,
-                           1671, 1246, 4445, 3748, 4434, 1765,  983, 1353, 3314,
-                           2249, 2525, 4314, 2896, 2171, 3775, 3320,  730, 2027,
-                           2731, 3976, 3825, 4171, 1978, 4468, 2371,  386, 1118,
-                           3263,  840, 3509, 4865, 3412, 2573, 1668, 4140, 1828,
-                           1203,  819, 4214, 2533, 3446,  643, 4924, 2902, 1393,
-                           4975,  841, 1924, 1159, 1396, 1327, 3531, 2008, 2330,
-                           3344,    0, 1785, 2268, 4522, 1792, 2828,  305, 4487,
-                           4986, 3210, 3476, 4418, 3986, 3188, 1206, 4837, 2877,
-                           2143, 1316, 3014, 3807,  339, 1928, 4332, 1721, 1955,
-                           1430, 1820, 1733,  132, 1124, 4910,  399, 4998, 3203,
-                           1066, 4770, 3787, 2390, 4240,  862, 2987, 1396, 4199,
-                           2140, 4278, 4725, 3767, 4419, 1019, 3708,   90, 2851,
-                           2610, 3655, 3402, 2040, 1712, 1375, 4589, 2905, 1572,
-                            545, 3985, 3399,  582, 4328, 3912, 2552,   83, 2255,
-                           1709,  772, 4299, 2146, 3329, 2442, 3295,   60,  173,
-                            543, 4997, 2966, 3912, 1602,  135, 2282, 3935, 2764,
-                           2342, 3756, 4573, 3705, 1470, 3025, 1498, 4276,  668,
-                           3561, 4033,  260, 3652,  775, 4020, 1031, 2617, 2294,
-                           2109, 2487, 3590, 1199, 2797, 1290, 3990]),
-       values=tensor([0.6994, 0.2438, 0.4802, 0.0829, 0.0677, 0.0178, 0.7638,
-                      0.1665, 0.8626, 0.8633, 0.8809, 0.3889, 0.5842, 0.4728,
-                      0.4918, 0.0860, 0.7324, 0.8491, 0.3798, 0.3500, 0.4975,
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        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.3949, 0.9428, 0.0102,  ..., 0.0310, 0.9492, 0.7070])
+tensor([0.8445, 0.0461, 0.7177,  ..., 0.0951, 0.9998, 0.0843])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -77,80 +77,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.013948440551757812 seconds
+Time: 0.013672351837158203 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '75277', '-ss', '5000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.5646839141845703}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '76797', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.6243436336517334}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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+                      0.6083, 0.6540, 0.5515, 0.2964, 0.1120, 0.6021, 0.6347,
+                      0.3907, 0.2739, 0.3659, 0.3709, 0.6294, 0.4562, 0.7659,
+                      0.5239, 0.5849, 0.2870, 0.6230, 0.1707, 0.0297, 0.9162,
+                      0.3474, 0.4202, 0.5536, 0.7120, 0.0212, 0.2778, 0.0910,
+                      0.7705, 0.5502, 0.7796, 0.5337, 0.3633, 0.7268, 0.9379,
+                      0.1172, 0.6666, 0.1324, 0.4072, 0.3612, 0.1135, 0.0575,
+                      0.1471, 0.4608, 0.5917, 0.8388, 0.4952, 0.0199, 0.6047,
+                      0.9696, 0.1826, 0.1339, 0.7125, 0.7144, 0.2370, 0.3706,
+                      0.8566, 0.3904, 0.0904, 0.1191, 0.1595, 0.7751, 0.7993,
+                      0.7943, 0.0017, 0.3810, 0.0843, 0.0930, 0.2029, 0.8088,
+                      0.0161, 0.4011, 0.1640, 0.3499, 0.7508, 0.6681, 0.8892,
+                      0.2737, 0.7342, 0.9081, 0.5222, 0.0526, 0.8172, 0.6113,
+                      0.5203, 0.1084, 0.8601, 0.6553, 0.9590]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.0748, 0.8455, 0.5581,  ..., 0.9449, 0.9600, 0.8816])
+tensor([0.9332, 0.9689, 0.0023,  ..., 0.6824, 0.5938, 0.3117])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -158,80 +158,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 1.5646839141845703 seconds
+Time: 1.6243436336517334 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '505155', '-ss', '5000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.3298921585083}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '496427', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.657760858535767}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([1247, 4227, 2384, 1324, 2688,  777, 3611, 2096, 3777,
-                           4017, 3060, 3853,  275, 2455, 1734, 3565,   37, 2244,
-                           1733, 4350, 1234, 2896, 3213, 1561, 1980, 4472, 4663,
-                            506, 4951, 3982, 1179,  751, 4549, 4447, 2215,  404,
-                            243, 3834, 4527, 3698, 4091, 3198, 2812, 3614, 4523,
-                           1643, 1713, 1084, 2707,    4, 4057, 1938, 3151, 2591,
-                            736, 3345,  437, 2659,  175, 3499, 4582, 1472,  654,
-                           3080, 4209,  728, 2500, 4977, 4359, 4315, 4550, 1180,
-                             65, 1612, 4551, 4050, 2941, 4296, 4434, 1223, 1238,
-                           1086, 3880, 4530, 1316, 2102, 4761, 3908, 1050, 3705,
-                           3194, 4362, 3882,  122,  751, 3713, 3799, 1795, 2520,
-                           1510, 2238, 4244, 1885, 2083, 3076, 2704, 4833, 3679,
-                           4672, 3718, 2996, 4020, 4241, 4395,  853, 4475, 4071,
-                           1868, 2791,   21, 4190, 1530, 3067, 1932, 3665, 3853,
-                           1426, 1044, 3601, 1259, 2412,  133, 3883, 2099, 3331,
-                           2256, 2974, 3456, 1750, 1889, 4841, 2200, 4977, 1425,
-                           1536,  158, 2655, 3709, 3160, 4144, 3380, 1076, 1185,
-                           3818, 1983, 1852, 4777, 1300,  431, 3274, 3575, 4837,
-                            325, 4137, 4592,  671,  998, 3275, 4613,  992, 3846,
-                           2916, 2447, 2605, 2672, 2276, 4850, 4965, 1297,  694,
-                            259, 1199, 4640, 4880, 3240, 1438,   11, 2797, 2545,
-                           3917, 4254,  214, 1182, 3713, 4287, 4785, 2360, 1203,
-                           4398, 3290, 2436, 3411, 3739,  928, 1526,  663, 2059,
-                           3931,  404, 1876, 3217, 4800, 4377, 4206,  877, 1100,
-                           1979, 2229, 3016,  180,  945, 4382, 2217, 3064,  609,
-                           1772, 4045, 3022, 1213, 4864, 3162, 1697, 2645,  550,
-                           2485, 4784, 4550,  726, 4920, 1331,  357, 3709, 4153,
-                           3528, 2484, 3864, 1866, 4322,  727, 4703]),
-       values=tensor([0.4663, 0.4340, 0.1210, 0.0765, 0.6336, 0.1902, 0.3091,
-                      0.4203, 0.8220, 0.0850, 0.1025, 0.1361, 0.5908, 0.4685,
-                      0.3710, 0.6463, 0.1664, 0.6973, 0.9489, 0.6122, 0.1354,
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-                      0.7613, 0.9018, 0.0697, 0.9293, 0.2543, 0.2531, 0.2854,
-                      0.3722, 0.6889, 0.4487, 0.3475, 0.2897]),
+       col_indices=tensor([1456, 4108, 1825, 4211, 4006, 2009, 4794, 4629, 3634,
+                           4848, 1328, 2172, 3777, 2426, 4781, 1153,  688, 3242,
+                           2166, 3793,  832, 1187,  491, 2413, 2550, 3996, 4467,
+                           4197, 4228, 4763, 2049, 3721, 3333, 1832, 2295, 1871,
+                           2507, 1917, 2328,  386, 2663, 3427,   92, 2215,  884,
+                            831, 4189, 3965, 2582, 4594, 2311, 1985, 3332, 3783,
+                           1166, 2640,  712, 1543, 3187, 4411, 2385,   76, 4213,
+                           3202,  109, 3740, 4838, 4865, 1693, 4224, 4919, 3814,
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+                           2744, 3061, 1128, 1053, 2061, 4640, 4375,  720,  711,
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+                           3602, 3370, 1566, 3351, 4695, 1100, 3450, 1169,  219,
+                           1866, 2008,  732, 2608, 2038, 1688, 4812, 3051, 3823,
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+                           2340, 2522, 3477,  650, 1785, 2894, 3925,  153,  849,
+                           3219,  884, 2150, 4713, 4341,  386, 2129]),
+       values=tensor([0.7648, 0.0388, 0.1971, 0.1656, 0.3027, 0.2125, 0.3792,
+                      0.4125, 0.6482, 0.6670, 0.7095, 0.4821, 0.4863, 0.5822,
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+                      0.3565, 0.3141, 0.1558, 0.1707, 0.9956, 0.8038, 0.2783,
+                      0.2084, 0.5138, 0.6444, 0.7914, 0.3804, 0.7614, 0.5121,
+                      0.6257, 0.6564, 0.2095, 0.6303, 0.2489, 0.7484, 0.7121,
+                      0.4636, 0.5455, 0.2133, 0.5314, 0.7354, 0.9506, 0.1953,
+                      0.6846, 0.0267, 0.7328, 0.3577, 0.1689, 0.9587, 0.6119,
+                      0.8490, 0.2600, 0.6883, 0.5099, 0.9089, 0.4716, 0.1370,
+                      0.1882, 0.4826, 0.1770, 0.4748, 0.7396, 0.2977, 0.4567,
+                      0.1595, 0.0502, 0.1923, 0.3134, 0.3518, 0.5690, 0.7860,
+                      0.7362, 0.5825, 0.7561, 0.9342, 0.9995, 0.7983, 0.5089,
+                      0.1453, 0.3062, 0.0429, 0.2336, 0.8068, 0.1462, 0.1833,
+                      0.1836, 0.6762, 0.4474, 0.9413, 0.8858, 0.7649, 0.1029,
+                      0.0340, 0.8541, 0.2876, 0.7440, 0.9437, 0.4278, 0.2873,
+                      0.6276, 0.1069, 0.0430, 0.7398, 0.2766, 0.0315, 0.1646,
+                      0.8701, 0.9575, 0.6657, 0.2313, 0.8344, 0.9596, 0.4326,
+                      0.3935, 0.2810, 0.4460, 0.1700, 0.0739, 0.9546, 0.3029,
+                      0.9901, 0.3485, 0.9910, 0.9908, 0.3133, 0.2767, 0.6857,
+                      0.6285, 0.8413, 0.3472, 0.1282, 0.1747, 0.8309, 0.1162,
+                      0.5105, 0.4325, 0.0653, 0.1299, 0.1987]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.8985, 0.0813, 0.9894,  ..., 0.1805, 0.5543, 0.3501])
+tensor([0.7200, 0.1618, 0.9398,  ..., 0.9808, 0.9900, 0.2750])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -239,77 +239,77 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.3298921585083 seconds
+Time: 10.657760858535767 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([1247, 4227, 2384, 1324, 2688,  777, 3611, 2096, 3777,
-                           4017, 3060, 3853,  275, 2455, 1734, 3565,   37, 2244,
-                           1733, 4350, 1234, 2896, 3213, 1561, 1980, 4472, 4663,
-                            506, 4951, 3982, 1179,  751, 4549, 4447, 2215,  404,
-                            243, 3834, 4527, 3698, 4091, 3198, 2812, 3614, 4523,
-                           1643, 1713, 1084, 2707,    4, 4057, 1938, 3151, 2591,
-                            736, 3345,  437, 2659,  175, 3499, 4582, 1472,  654,
-                           3080, 4209,  728, 2500, 4977, 4359, 4315, 4550, 1180,
-                             65, 1612, 4551, 4050, 2941, 4296, 4434, 1223, 1238,
-                           1086, 3880, 4530, 1316, 2102, 4761, 3908, 1050, 3705,
-                           3194, 4362, 3882,  122,  751, 3713, 3799, 1795, 2520,
-                           1510, 2238, 4244, 1885, 2083, 3076, 2704, 4833, 3679,
-                           4672, 3718, 2996, 4020, 4241, 4395,  853, 4475, 4071,
-                           1868, 2791,   21, 4190, 1530, 3067, 1932, 3665, 3853,
-                           1426, 1044, 3601, 1259, 2412,  133, 3883, 2099, 3331,
-                           2256, 2974, 3456, 1750, 1889, 4841, 2200, 4977, 1425,
-                           1536,  158, 2655, 3709, 3160, 4144, 3380, 1076, 1185,
-                           3818, 1983, 1852, 4777, 1300,  431, 3274, 3575, 4837,
-                            325, 4137, 4592,  671,  998, 3275, 4613,  992, 3846,
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-                           4398, 3290, 2436, 3411, 3739,  928, 1526,  663, 2059,
-                           3931,  404, 1876, 3217, 4800, 4377, 4206,  877, 1100,
-                           1979, 2229, 3016,  180,  945, 4382, 2217, 3064,  609,
-                           1772, 4045, 3022, 1213, 4864, 3162, 1697, 2645,  550,
-                           2485, 4784, 4550,  726, 4920, 1331,  357, 3709, 4153,
-                           3528, 2484, 3864, 1866, 4322,  727, 4703]),
-       values=tensor([0.4663, 0.4340, 0.1210, 0.0765, 0.6336, 0.1902, 0.3091,
-                      0.4203, 0.8220, 0.0850, 0.1025, 0.1361, 0.5908, 0.4685,
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-                      0.8492, 0.9495, 0.7252, 0.1393, 0.3125, 0.0955, 0.0411,
-                      0.0855, 0.4594, 0.0571, 0.4760, 0.9753, 0.9680, 0.6765,
-                      0.6632, 0.7732, 0.2205, 0.7219, 0.1231, 0.5507, 0.9386,
-                      0.9853, 0.1484, 0.5471, 0.2415, 0.3412, 0.3970, 0.9721,
-                      0.4075, 0.7397, 0.6041, 0.4919, 0.1150, 0.1028, 0.3707,
-                      0.5907, 0.4305, 0.9162, 0.9956, 0.3282, 0.6112, 0.6540,
-                      0.0961, 0.8665, 0.2552, 0.6175, 0.4850, 0.4310, 0.1165,
-                      0.3274, 0.7923, 0.1515, 0.5293, 0.8418, 0.1450, 0.8268,
-                      0.9665, 0.7626, 0.7605, 0.9986, 0.9489, 0.8011, 0.9290,
-                      0.5451, 0.8590, 0.5389, 0.0080, 0.8363, 0.8570, 0.5734,
-                      0.7613, 0.9018, 0.0697, 0.9293, 0.2543, 0.2531, 0.2854,
-                      0.3722, 0.6889, 0.4487, 0.3475, 0.2897]),
+       col_indices=tensor([1456, 4108, 1825, 4211, 4006, 2009, 4794, 4629, 3634,
+                           4848, 1328, 2172, 3777, 2426, 4781, 1153,  688, 3242,
+                           2166, 3793,  832, 1187,  491, 2413, 2550, 3996, 4467,
+                           4197, 4228, 4763, 2049, 3721, 3333, 1832, 2295, 1871,
+                           2507, 1917, 2328,  386, 2663, 3427,   92, 2215,  884,
+                            831, 4189, 3965, 2582, 4594, 2311, 1985, 3332, 3783,
+                           1166, 2640,  712, 1543, 3187, 4411, 2385,   76, 4213,
+                           3202,  109, 3740, 4838, 4865, 1693, 4224, 4919, 3814,
+                           2015, 3428, 1352, 4341, 1472, 2797, 2890, 4076, 4688,
+                           3016, 2258, 2816, 3403, 2779, 3008, 2741, 3872, 4297,
+                           2744, 3061, 1128, 1053, 2061, 4640, 4375,  720,  711,
+                           4960, 2985, 1442, 2777, 3995,  402, 2650, 2504, 3872,
+                           3802, 4082, 1303, 3783, 3433,  706, 1390, 3838, 2208,
+                           4107, 3973, 1734, 1507,  272, 1533,  615, 4787, 3665,
+                           2779, 1666, 3893,  329,  484, 3682, 3581, 3861, 1712,
+                           3602, 3370, 1566, 3351, 4695, 1100, 3450, 1169,  219,
+                           1866, 2008,  732, 2608, 2038, 1688, 4812, 3051, 3823,
+                           1823,  939, 3738, 2330, 4889, 4110, 4784, 1533, 4611,
+                           2059, 1617, 2978, 3099, 1232, 2100,  335, 4107, 2680,
+                           3128, 3276, 4068,  418, 1349, 3022, 3207,  239, 1757,
+                           4965, 2122, 1181, 2639, 3705, 1459,  440, 3554, 3043,
+                           4807, 3603, 4909, 3556,  523, 4148, 3900, 4779, 4147,
+                           3592, 3815, 3834,  346, 1568, 2888, 1453, 4360, 2938,
+                           2512, 2526,  770, 1001, 1840, 3310, 2261, 2368,  871,
+                           4581, 2300, 1652,   99, 3175, 3134, 1698, 1430, 2502,
+                            796, 3677, 1704, 1667, 1603, 4332,  674, 1627, 3977,
+                           2340, 2522, 3477,  650, 1785, 2894, 3925,  153,  849,
+                           3219,  884, 2150, 4713, 4341,  386, 2129]),
+       values=tensor([0.7648, 0.0388, 0.1971, 0.1656, 0.3027, 0.2125, 0.3792,
+                      0.4125, 0.6482, 0.6670, 0.7095, 0.4821, 0.4863, 0.5822,
+                      0.6538, 0.0534, 0.6044, 0.3759, 0.6694, 0.9058, 0.8034,
+                      0.2582, 0.5391, 0.3103, 0.1562, 0.1893, 0.1390, 0.4308,
+                      0.2867, 0.5767, 0.7856, 0.3429, 0.4270, 0.8922, 0.9477,
+                      0.6459, 0.8706, 0.3536, 0.3872, 0.6240, 0.3744, 0.1308,
+                      0.7032, 0.6133, 0.4417, 0.0247, 0.6177, 0.7538, 0.9642,
+                      0.7692, 0.3201, 0.7630, 0.1763, 0.9162, 0.2149, 0.5298,
+                      0.9979, 0.1331, 0.0152, 0.8651, 0.1298, 0.9376, 0.6580,
+                      0.3607, 0.7740, 0.8075, 0.7657, 0.5525, 0.6175, 0.2880,
+                      0.6706, 0.0327, 0.9945, 0.6498, 0.7256, 0.5117, 0.5980,
+                      0.3927, 0.7003, 0.3495, 0.5625, 0.7323, 0.1430, 0.8774,
+                      0.3007, 0.5835, 0.6654, 0.7253, 0.3632, 0.3645, 0.8047,
+                      0.4157, 0.7866, 0.4430, 0.2446, 0.2544, 0.2621, 0.6264,
+                      0.4385, 0.3533, 0.5965, 0.7609, 0.3438, 0.5220, 0.2899,
+                      0.2775, 0.2450, 0.8517, 0.5321, 0.0807, 0.2339, 0.9026,
+                      0.0430, 0.7174, 0.3348, 0.8876, 0.6517, 0.5060, 0.4026,
+                      0.0860, 0.4949, 0.8231, 0.6247, 0.1961, 0.6284, 0.9780,
+                      0.3565, 0.3141, 0.1558, 0.1707, 0.9956, 0.8038, 0.2783,
+                      0.2084, 0.5138, 0.6444, 0.7914, 0.3804, 0.7614, 0.5121,
+                      0.6257, 0.6564, 0.2095, 0.6303, 0.2489, 0.7484, 0.7121,
+                      0.4636, 0.5455, 0.2133, 0.5314, 0.7354, 0.9506, 0.1953,
+                      0.6846, 0.0267, 0.7328, 0.3577, 0.1689, 0.9587, 0.6119,
+                      0.8490, 0.2600, 0.6883, 0.5099, 0.9089, 0.4716, 0.1370,
+                      0.1882, 0.4826, 0.1770, 0.4748, 0.7396, 0.2977, 0.4567,
+                      0.1595, 0.0502, 0.1923, 0.3134, 0.3518, 0.5690, 0.7860,
+                      0.7362, 0.5825, 0.7561, 0.9342, 0.9995, 0.7983, 0.5089,
+                      0.1453, 0.3062, 0.0429, 0.2336, 0.8068, 0.1462, 0.1833,
+                      0.1836, 0.6762, 0.4474, 0.9413, 0.8858, 0.7649, 0.1029,
+                      0.0340, 0.8541, 0.2876, 0.7440, 0.9437, 0.4278, 0.2873,
+                      0.6276, 0.1069, 0.0430, 0.7398, 0.2766, 0.0315, 0.1646,
+                      0.8701, 0.9575, 0.6657, 0.2313, 0.8344, 0.9596, 0.4326,
+                      0.3935, 0.2810, 0.4460, 0.1700, 0.0739, 0.9546, 0.3029,
+                      0.9901, 0.3485, 0.9910, 0.9908, 0.3133, 0.2767, 0.6857,
+                      0.6285, 0.8413, 0.3472, 0.1282, 0.1747, 0.8309, 0.1162,
+                      0.5105, 0.4325, 0.0653, 0.1299, 0.1987]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.8985, 0.0813, 0.9894,  ..., 0.1805, 0.5543, 0.3501])
+tensor([0.7200, 0.1618, 0.9398,  ..., 0.9808, 0.9900, 0.2750])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -317,13 +317,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.3298921585083 seconds
+Time: 10.657760858535767 seconds
 
-[40.32, 39.94, 39.79, 39.67, 40.95, 39.71, 39.34, 39.43, 39.56, 39.61]
-[95.58]
-12.982455968856812
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 505155, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.3298921585083, 'TIME_S_1KI': 0.020448955584935914, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1240.863141503334, 'W': 95.58}
-[40.32, 39.94, 39.79, 39.67, 40.95, 39.71, 39.34, 39.43, 39.56, 39.61, 39.84, 39.42, 39.39, 39.45, 39.34, 39.21, 39.4, 39.18, 39.13, 39.91]
-712.75
-35.6375
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 505155, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.3298921585083, 'TIME_S_1KI': 0.020448955584935914, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1240.863141503334, 'W': 95.58, 'J_1KI': 2.4564007908529737, 'W_1KI': 0.1892092526056359, 'W_D': 59.942499999999995, 'J_D': 778.2008669131993, 'W_D_1KI': 0.11866159891518444, 'J_D_1KI': 0.0002349013647596964}
+[42.32, 39.93, 39.62, 40.13, 39.61, 40.04, 40.59, 39.88, 40.09, 39.51]
+[96.43]
+12.952775955200195
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 496427, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.657760858535767, 'TIME_S_1KI': 0.021468938753403354, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1249.036185359955, 'W': 96.43}
+[42.32, 39.93, 39.62, 40.13, 39.61, 40.04, 40.59, 39.88, 40.09, 39.51, 41.79, 39.51, 39.66, 39.66, 39.52, 39.55, 39.42, 40.24, 39.94, 39.74]
+719.07
+35.953500000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 496427, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.657760858535767, 'TIME_S_1KI': 0.021468938753403354, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1249.036185359955, 'W': 96.43, 'J_1KI': 2.5160520788755543, 'W_1KI': 0.19424809690045064, 'W_D': 60.4765, 'J_D': 783.3385550546647, 'W_D_1KI': 0.12182355109613296, 'J_D_1KI': 0.0002454007358506547}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.json
index 1cf76cc..a1e614e 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.json
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 461197, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.877047538757324, "TIME_S_1KI": 0.023584384848030937, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1221.180625166893, "W": 95.74, "J_1KI": 2.6478503224585004, "W_1KI": 0.20759024885244265, "W_D": 60.15774999999999, "J_D": 767.3227360939383, "W_D_1KI": 0.13043829426470682, "J_D_1KI": 0.0002828255480081328}
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 479482, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.686605215072632, "TIME_S_1KI": 0.02228781312973716, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1256.431904683113, "W": 97.02, "J_1KI": 2.6203943102829994, "W_1KI": 0.20234336221172014, "W_D": 61.007, "J_D": 790.0550526592731, "W_D_1KI": 0.12723522467996715, "J_D_1KI": 0.0002653597521491258}
diff --git a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.output
index 09b3a13..74cfd6b 100644
--- a/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.output
+++ b/pytorch/output_synthetic_maxcore/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.0320582389831543}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.018622159957885742}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1249, 1250]),
-       col_indices=tensor([2956,  558, 3504,  ..., 1528, 4784, 1878]),
-       values=tensor([0.5224, 0.1438, 0.5941,  ..., 0.0368, 0.6760, 0.3012]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([4747, 1400,  364,  ..., 4145, 1033, 2512]),
+       values=tensor([0.2725, 0.0769, 0.1246,  ..., 0.7043, 0.6053, 0.3533]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.8976, 0.5094, 0.6995,  ..., 0.0327, 0.1649, 0.7937])
+tensor([0.7933, 0.6184, 0.7441,  ..., 0.6976, 0.5256, 0.3905])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 0.0320582389831543 seconds
+Time: 0.018622159957885742 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '32752', '-ss', '5000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.745659589767456}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '56384', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.3166499137878418}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([1743, 4461,  346,  ..., 1137, 3893, 4349]),
-       values=tensor([0.7861, 0.9854, 0.5411,  ..., 0.5282, 0.2898, 0.9587]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4757, 3329, 1263,  ..., 1469, 3329, 2701]),
+       values=tensor([0.1246, 0.9627, 0.7339,  ..., 0.9069, 0.1668, 0.3333]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.5708, 0.3567, 0.0850,  ..., 0.6472, 0.1624, 0.7150])
+tensor([0.2123, 0.9228, 0.8495,  ..., 0.6306, 0.5525, 0.0271])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 0.745659589767456 seconds
+Time: 1.3166499137878418 seconds
 
-['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '461197', '-ss', '5000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.877047538757324}
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '449650', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.846704244613647}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([4244, 4483, 4692,  ..., 3607, 4429,  290]),
-       values=tensor([0.6080, 0.0136, 0.3918,  ..., 0.5066, 0.3391, 0.6977]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1248, 1249, 1250]),
+       col_indices=tensor([3866, 4415,  541,  ..., 3166, 1514,  574]),
+       values=tensor([0.2175, 0.0585, 0.9595,  ..., 0.1738, 0.3089, 0.4824]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.1101, 0.0872, 0.5048,  ..., 0.5059, 0.1642, 0.4124])
+tensor([0.0321, 0.8662, 0.1160,  ..., 0.1960, 0.2258, 0.6699])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.877047538757324 seconds
+Time: 9.846704244613647 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '479482', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.686605215072632}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
-       col_indices=tensor([4244, 4483, 4692,  ..., 3607, 4429,  290]),
-       values=tensor([0.6080, 0.0136, 0.3918,  ..., 0.5066, 0.3391, 0.6977]),
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4848,  292, 4243,  ..., 2482, 3447, 2721]),
+       values=tensor([0.8445, 0.4097, 0.2554,  ..., 0.4119, 0.7548, 0.6186]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.1101, 0.0872, 0.5048,  ..., 0.5059, 0.1642, 0.4124])
+tensor([0.9146, 0.3592, 0.2749,  ..., 0.2096, 0.7041, 0.4735])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +72,29 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.877047538757324 seconds
+Time: 10.686605215072632 seconds
 
-[42.19, 40.35, 39.27, 40.36, 39.17, 39.06, 39.17, 39.21, 39.04, 39.45]
-[95.74]
-12.755176782608032
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 461197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.877047538757324, 'TIME_S_1KI': 0.023584384848030937, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.180625166893, 'W': 95.74}
-[42.19, 40.35, 39.27, 40.36, 39.17, 39.06, 39.17, 39.21, 39.04, 39.45, 39.84, 39.7, 39.64, 39.58, 39.75, 39.24, 39.17, 39.36, 39.12, 39.43]
-711.645
-35.58225
-{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 461197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.877047538757324, 'TIME_S_1KI': 0.023584384848030937, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.180625166893, 'W': 95.74, 'J_1KI': 2.6478503224585004, 'W_1KI': 0.20759024885244265, 'W_D': 60.15774999999999, 'J_D': 767.3227360939383, 'W_D_1KI': 0.13043829426470682, 'J_D_1KI': 0.0002828255480081328}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4848,  292, 4243,  ..., 2482, 3447, 2721]),
+       values=tensor([0.8445, 0.4097, 0.2554,  ..., 0.4119, 0.7548, 0.6186]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.9146, 0.3592, 0.2749,  ..., 0.2096, 0.7041, 0.4735])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.686605215072632 seconds
+
+[40.33, 39.49, 39.61, 39.68, 39.98, 40.15, 40.37, 39.78, 40.33, 39.71]
+[97.02]
+12.950236082077026
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 479482, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.686605215072632, 'TIME_S_1KI': 0.02228781312973716, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1256.431904683113, 'W': 97.02}
+[40.33, 39.49, 39.61, 39.68, 39.98, 40.15, 40.37, 39.78, 40.33, 39.71, 40.16, 39.74, 40.01, 39.44, 39.5, 40.68, 39.86, 40.07, 41.79, 39.36]
+720.26
+36.013
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 479482, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.686605215072632, 'TIME_S_1KI': 0.02228781312973716, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1256.431904683113, 'W': 97.02, 'J_1KI': 2.6203943102829994, 'W_1KI': 0.20234336221172014, 'W_D': 61.007, 'J_D': 790.0550526592731, 'W_D_1KI': 0.12723522467996715, 'J_D_1KI': 0.0002653597521491258}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..b134077
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 277, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.484652519226074, "TIME_S_1KI": 37.85073111633962, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 815.8254318380356, "W": 58.18, "J_1KI": 2945.2181654802725, "W_1KI": 210.0361010830325, "W_D": 41.336749999999995, "J_D": 579.6420061796307, "W_D_1KI": 149.2301444043321, "J_D_1KI": 538.7369834091411}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..a88f49b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 3.777904748916626}
+
+tensor(indices=tensor([[70224, 77791, 54738,  ..., 90159, 78472, 84710],
+                       [89635, 18305, 28352,  ..., 20029, 92022,  1919]]),
+       values=tensor([0.0841, 0.8913, 0.5780,  ..., 0.2242, 0.4150, 0.0064]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.9684, 0.4732, 0.2685,  ..., 0.9364, 0.9632, 0.8366])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 3.777904748916626 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '277', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.484652519226074}
+
+tensor(indices=tensor([[75718, 77882, 53333,  ..., 55126, 29590, 52929],
+                       [24993,  5986,  8335,  ..., 73350, 51485,  6339]]),
+       values=tensor([0.8153, 0.9561, 0.4734,  ..., 0.5384, 0.6480, 0.6223]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.4470, 0.7756, 0.9870,  ..., 0.8175, 0.5678, 0.3606])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.484652519226074 seconds
+
+tensor(indices=tensor([[75718, 77882, 53333,  ..., 55126, 29590, 52929],
+                       [24993,  5986,  8335,  ..., 73350, 51485,  6339]]),
+       values=tensor([0.8153, 0.9561, 0.4734,  ..., 0.5384, 0.6480, 0.6223]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_coo)
+tensor([0.4470, 0.7756, 0.9870,  ..., 0.8175, 0.5678, 0.3606])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.484652519226074 seconds
+
+[18.87, 18.51, 18.66, 18.8, 18.81, 18.54, 18.56, 19.08, 18.93, 18.51]
+[58.18]
+14.022437810897827
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.484652519226074, 'TIME_S_1KI': 37.85073111633962, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 815.8254318380356, 'W': 58.18}
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+336.865
+16.84325
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 277, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.484652519226074, 'TIME_S_1KI': 37.85073111633962, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 815.8254318380356, 'W': 58.18, 'J_1KI': 2945.2181654802725, 'W_1KI': 210.0361010830325, 'W_D': 41.336749999999995, 'J_D': 579.6420061796307, 'W_D_1KI': 149.2301444043321, 'J_D_1KI': 538.7369834091411}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..cb88306
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 40.743112087249756, "TIME_S_1KI": 407.43112087249756, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2558.607043964863, "W": 53.89, "J_1KI": 25586.07043964863, "W_1KI": 538.9000000000001, "W_D": 36.80825, "J_D": 1747.594131119311, "W_D_1KI": 368.08250000000004, "J_D_1KI": 3680.8250000000003}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..751fa7d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 40.743112087249756}
+
+tensor(indices=tensor([[55139, 39270, 91918,  ..., 49140, 39015, 49909],
+                       [ 5329, 58648, 67130,  ..., 91740, 79288, 38914]]),
+       values=tensor([0.0968, 0.3684, 0.7307,  ..., 0.0250, 0.0960, 0.9822]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.9208, 0.9876, 0.2395,  ..., 0.1912, 0.6607, 0.3160])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 40.743112087249756 seconds
+
+tensor(indices=tensor([[55139, 39270, 91918,  ..., 49140, 39015, 49909],
+                       [ 5329, 58648, 67130,  ..., 91740, 79288, 38914]]),
+       values=tensor([0.0968, 0.3684, 0.7307,  ..., 0.0250, 0.0960, 0.9822]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_coo)
+tensor([0.9208, 0.9876, 0.2395,  ..., 0.1912, 0.6607, 0.3160])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 40.743112087249756 seconds
+
+[18.93, 18.59, 18.84, 19.25, 18.52, 18.56, 22.71, 19.79, 18.64, 18.99]
+[53.89]
+47.47832703590393
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 40.743112087249756, 'TIME_S_1KI': 407.43112087249756, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2558.607043964863, 'W': 53.89}
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+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 40.743112087249756, 'TIME_S_1KI': 407.43112087249756, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2558.607043964863, 'W': 53.89, 'J_1KI': 25586.07043964863, 'W_1KI': 538.9000000000001, 'W_D': 36.80825, 'J_D': 1747.594131119311, 'W_D_1KI': 368.08250000000004, 'J_D_1KI': 3680.8250000000003}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..e03b0a9
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 408.6323621273041, "TIME_S_1KI": 4086.323621273041, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 24437.81099384308, "W": 53.28, "J_1KI": 244378.10993843077, "W_1KI": 532.8000000000001, "W_D": 36.44775, "J_D": 16717.402883837163, "W_D_1KI": 364.4775, "J_D_1KI": 3644.775}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..bfef7db
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_0.01.output
@@ -0,0 +1,39 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 408.6323621273041}
+
+tensor(indices=tensor([[32054,  2702, 33950,  ...,  1264, 99287,  9659],
+                       [48537, 24014, 15665,  ..., 63340, 97079, 68679]]),
+       values=tensor([0.3553, 0.4131, 0.3021,  ..., 0.5462, 0.8786, 0.8288]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_coo)
+tensor([0.7950, 0.4850, 0.6219,  ..., 0.8943, 0.5958, 0.2665])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 408.6323621273041 seconds
+
+tensor(indices=tensor([[32054,  2702, 33950,  ...,  1264, 99287,  9659],
+                       [48537, 24014, 15665,  ..., 63340, 97079, 68679]]),
+       values=tensor([0.3553, 0.4131, 0.3021,  ..., 0.5462, 0.8786, 0.8288]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_coo)
+tensor([0.7950, 0.4850, 0.6219,  ..., 0.8943, 0.5958, 0.2665])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 408.6323621273041 seconds
+
+[19.18, 18.59, 19.02, 18.5, 18.56, 18.88, 18.64, 18.58, 18.7, 18.52]
+[53.28]
+458.66762375831604
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 408.6323621273041, 'TIME_S_1KI': 4086.323621273041, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 24437.81099384308, 'W': 53.28}
+[19.18, 18.59, 19.02, 18.5, 18.56, 18.88, 18.64, 18.58, 18.7, 18.52, 18.96, 18.54, 18.84, 18.87, 18.8, 18.49, 18.58, 18.69, 18.62, 18.83]
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+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 408.6323621273041, 'TIME_S_1KI': 4086.323621273041, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 24437.81099384308, 'W': 53.28, 'J_1KI': 244378.10993843077, 'W_1KI': 532.8000000000001, 'W_D': 36.44775, 'J_D': 16717.402883837163, 'W_D_1KI': 364.4775, 'J_D_1KI': 3644.775}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..8e00da4
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..6359401
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_1e-05.output
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+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '100000', '-sd', '1e-05']
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..00308c8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_5e-05.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 538, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.484038829803467, "TIME_S_1KI": 19.48706102193953, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 863.0392302846909, "W": 61.82000000000001, "J_1KI": 1604.1621380756337, "W_1KI": 114.90706319702603, "W_D": 44.82350000000001, "J_D": 625.7592840288879, "W_D_1KI": 83.3150557620818, "J_D_1KI": 154.86069844253123}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..60409dd
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_100000_5e-05.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..7a98492
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.0001.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 29183, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.467581272125244, "TIME_S_1KI": 0.35868763568259754, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 748.1673318600655, "W": 53.97, "J_1KI": 25.637094605080545, "W_1KI": 1.849364355960662, "W_D": 37.1335, "J_D": 514.7687903951406, "W_D_1KI": 1.2724360072645033, "J_D_1KI": 0.04360196029416109}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..ee8c335
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.0001.output
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+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.0001']
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..d33b6e1
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2873, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.13928747177124, "TIME_S_1KI": 3.529163756272621, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 733.8819551849365, "W": 54.04, "J_1KI": 255.4409868377781, "W_1KI": 18.80960668290985, "W_D": 29.094, "J_D": 395.10661739730836, "W_D_1KI": 10.126696832579185, "J_D_1KI": 3.5247813548831135}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..bf0841f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,56 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.001']
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+tensor([0.3644, 0.5010, 0.4580,  ..., 0.2484, 0.5683, 0.7454])
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..52ea94b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.01.json
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..f00641f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.01.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..72fd5cb
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.05.json
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..9f803ce
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.05.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..90fb139
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.1.json
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..9dbb705
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.1.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..139f671
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..8708d5e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.2.output
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+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.2']
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..95b513d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 105.20825099945068, "TIME_S_1KI": 1052.0825099945068, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 6216.137630634308, "W": 53.74, "J_1KI": 62161.37630634308, "W_1KI": 537.4, "W_D": 36.70375, "J_D": 4245.544502426385, "W_D_1KI": 367.0375, "J_D_1KI": 3670.3750000000005}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..b729e98
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.3.output
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+Time: 105.20825099945068 seconds
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..1173009
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.4.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 140.30631065368652, "TIME_S_1KI": 1403.0631065368652, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 8230.189362194538, "W": 53.730000000000004, "J_1KI": 82301.89362194538, "W_1KI": 537.3, "W_D": 36.82475000000001, "J_D": 5640.697296025934, "W_D_1KI": 368.24750000000006, "J_D_1KI": 3682.475000000001}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..d3accaa
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.4.output
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+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.4']
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..dbe73aa
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.5.json
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..4134bcd
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_0.5.output
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+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '0.5']
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..f341b39
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_1e-05.json
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..8398219
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,962 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '10000', '-sd', '1e-05']
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..bd832dc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_5e-05.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 57363, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.50459599494934, "TIME_S_1KI": 0.18312494107611774, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 753.622943906784, "W": 54.14, "J_1KI": 13.137788189369175, "W_1KI": 0.9438139567316912, "W_D": 37.02675, "J_D": 515.4083549741507, "W_D_1KI": 0.645481407876157, "J_D_1KI": 0.011252574096127418}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..f750d8a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_10000_5e-05.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..fb2c845
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_0.0001.json
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..6ea52ae
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_0.0001.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..9567b0b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_1e-05.json
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..463d785
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_1e-05.output
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+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '500000', '-sd', '1e-05']
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..2d2f22b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 59.20624232292175, "TIME_S_1KI": 592.0624232292175, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3582.822951974869, "W": 53.59, "J_1KI": 35828.22951974869, "W_1KI": 535.9000000000001, "W_D": 36.65625000000001, "J_D": 2450.6970299184327, "W_D_1KI": 366.56250000000006, "J_D_1KI": 3665.6250000000005}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..4d2ed5e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_500000_5e-05.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..e7a72d5
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.0001.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1037, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.359702587127686, "TIME_S_1KI": 9.990069997230169, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 957.6284668636321, "W": 69.21, "J_1KI": 923.4604309196067, "W_1KI": 66.74059787849565, "W_D": 52.023999999999994, "J_D": 719.833309638977, "W_D_1KI": 50.16779170684667, "J_D_1KI": 48.37781263919641}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..53d5bcc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.0001.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..8fa8bde
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.001.json
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..f47a11e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,56 @@
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..a67b03b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.01.json
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..1b9d963
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.01.output
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+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '50000', '-sd', '0.01']
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.05.json
new file mode 100644
index 0000000..331c318
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.05.json
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.05.output
new file mode 100644
index 0000000..4a9a4f1
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_0.05.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..267612a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_1e-05.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 9450, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.515788555145264, "TIME_S_1KI": 1.1127818576873296, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1132.3027784729004, "W": 81.36, "J_1KI": 119.82039983840217, "W_1KI": 8.60952380952381, "W_D": 64.46575, "J_D": 897.1822497706413, "W_D_1KI": 6.821772486772487, "J_D_1KI": 0.721880686430951}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..5556e43
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_1e-05.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..d8c4e49
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_5e-05.json
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..0b97858
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_50000_5e-05.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..effc445
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.0001.json
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+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 110741, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.555631637573242, "TIME_S_1KI": 0.09531818962780941, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 749.4532634925843, "W": 54.06, "J_1KI": 6.767622321385795, "W_1KI": 0.4881660812165323, "W_D": 31.128750000000004, "J_D": 431.54908020615585, "W_D_1KI": 0.2810950777038315, "J_D_1KI": 0.0025383108126514255}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..25f1a7c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.0001.output
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+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '0.0001']
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+tensor([0.9224, 0.7713, 0.6372,  ..., 0.1080, 0.4847, 0.7627])
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+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '48679', '-ss', '5000', '-sd', '0.0001']
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..c331d55
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.001.json
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..0e3cfb9
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new file mode 100644
index 0000000..49bd6d7
--- /dev/null
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..596f748
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+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.01.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..a26033c
--- /dev/null
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..82fa51c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.05.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..95e5391
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.1.json
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..e81e68c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.1.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..ad9dd5b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.2.json
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..327cee0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.2.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..57347d6
--- /dev/null
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..dee7431
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.3.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..b442a3d
--- /dev/null
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..3cf5003
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.4.output
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..7336dc3
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.5.json
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..f65ba0a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_0.5.output
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+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_coo)
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+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 43.84387135505676 seconds
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+                       [1944,  625, 4971,  ..., 3925, 1009, 4877]]),
+       values=tensor([0.9799, 0.7006, 0.4017,  ..., 0.0089, 0.2742, 0.6115]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_coo)
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+Time: 43.84387135505676 seconds
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..bfc909f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
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diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..a6da299
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_1e-05.output
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+                      0.4535, 0.5541, 0.4601, 0.6611, 0.9859, 0.3452, 0.3096,
+                      0.7036, 0.2401, 0.0819, 0.5769, 0.5062, 0.6194, 0.9382,
+                      0.0295, 0.3348, 0.5122, 0.4529, 0.0255, 0.0758, 0.7708,
+                      0.0166, 0.6989, 0.0186, 0.4532, 0.1057, 0.5888, 0.3377,
+                      0.7542, 0.5158, 0.2588, 0.1427, 0.6251, 0.6878, 0.2496,
+                      0.4325, 0.8025, 0.3421, 0.4433, 0.0822, 0.2641, 0.5402,
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+                      0.3751, 0.8982, 0.1483, 0.9186, 0.2743, 0.9597, 0.3340,
+                      0.2999, 0.6533, 0.7404, 0.0130, 0.2933, 0.9789, 0.9071,
+                      0.0175, 0.3156, 0.1345, 0.2753, 0.7749, 0.2503, 0.8305,
+                      0.3034, 0.7305, 0.5373, 0.3992, 0.1960, 0.2907, 0.1535,
+                      0.1316, 0.2194, 0.1014, 0.9269, 0.9889, 0.9128, 0.0976,
+                      0.3482, 0.2877, 0.6267, 0.4150, 0.2616, 0.4682, 0.0299,
+                      0.0836, 0.7458, 0.2793, 0.4768, 0.9958, 0.2488, 0.4201,
+                      0.2869, 0.3186, 0.7942, 0.8403, 0.5786, 0.4007, 0.6150,
+                      0.4495, 0.2526, 0.2152, 0.5546, 0.7149, 0.4606, 0.9469,
+                      0.2333, 0.3018, 0.5568, 0.1244, 0.1971, 0.4252, 0.7876,
+                      0.7888, 0.8426, 0.5772, 0.3188, 0.4090, 0.9424, 0.2536,
+                      0.8672, 0.2919, 0.0356, 0.5001, 0.2679]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_coo)
+tensor([0.3876, 0.7864, 0.7691,  ..., 0.7892, 0.3784, 0.7593])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.04805874824524 seconds
+
+[19.34, 18.38, 18.73, 18.44, 18.74, 18.5, 18.52, 18.48, 18.88, 18.5]
+[54.01]
+13.461623430252075
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 643628, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.04805874824524, 'TIME_S_1KI': 0.015611593573065869, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.0622814679145, 'W': 54.01}
+[19.34, 18.38, 18.73, 18.44, 18.74, 18.5, 18.52, 18.48, 18.88, 18.5, 19.07, 18.73, 18.74, 18.96, 18.54, 18.52, 19.3, 19.13, 19.34, 18.55]
+337.65999999999997
+16.883
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 643628, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.04805874824524, 'TIME_S_1KI': 0.015611593573065869, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 727.0622814679145, 'W': 54.01, 'J_1KI': 1.1296312178275565, 'W_1KI': 0.0839149322279329, 'W_D': 37.126999999999995, 'J_D': 499.78969309496875, 'W_D_1KI': 0.05768394165573902, 'J_D_1KI': 8.962310784449872e-05}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..5c133dd
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 206949, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.472784996032715, "TIME_S_1KI": 0.05060563228637353, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 748.6759082412719, "W": 53.96, "J_1KI": 3.617683140490034, "W_1KI": 0.2607405689324423, "W_D": 27.490750000000002, "J_D": 381.4244296605587, "W_D_1KI": 0.13283828382838284, "J_D_1KI": 0.0006418889863124867}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..2a2ec1b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_coo_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,73 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '100', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.016951322555541992}
+
+tensor(indices=tensor([[3443, 2313, 2242,  ..., 4800, 1483, 3473],
+                       [4032, 3763, 4471,  ..., 3085, 1057, 1825]]),
+       values=tensor([0.1068, 0.9154, 0.8800,  ..., 0.3341, 0.8102, 0.7867]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.7557, 0.3910, 0.5109,  ..., 0.1555, 0.7629, 0.3514])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.016951322555541992 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '61942', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.1427536010742188}
+
+tensor(indices=tensor([[1243, 1602, 2784,  ...,   26, 4298, 1321],
+                       [2502, 2812,  977,  ..., 1424, 4897, 3223]]),
+       values=tensor([0.6050, 0.9563, 0.3311,  ..., 0.5352, 0.0550, 0.6866]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.4920, 0.4870, 0.4099,  ..., 0.8715, 0.2306, 0.1514])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 3.1427536010742188 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'coo', '206949', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "coo", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.472784996032715}
+
+tensor(indices=tensor([[1387,  500, 4790,  ..., 1497, 2199, 1869],
+                       [4439, 2421, 2246,  ..., 2989,   67, 3327]]),
+       values=tensor([0.3098, 0.8075, 0.7779,  ..., 0.1098, 0.5386, 0.9688]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.6317, 0.1493, 0.2614,  ..., 0.4209, 0.4836, 0.9840])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.472784996032715 seconds
+
+tensor(indices=tensor([[1387,  500, 4790,  ..., 1497, 2199, 1869],
+                       [4439, 2421, 2246,  ..., 2989,   67, 3327]]),
+       values=tensor([0.3098, 0.8075, 0.7779,  ..., 0.1098, 0.5386, 0.9688]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_coo)
+tensor([0.6317, 0.1493, 0.2614,  ..., 0.4209, 0.4836, 0.9840])
+Matrix Type: synthetic
+Matrix Format: coo
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.472784996032715 seconds
+
+[18.98, 22.05, 19.95, 18.57, 19.29, 18.62, 18.75, 18.51, 18.71, 18.66]
+[53.96]
+13.874646186828613
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 206949, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.472784996032715, 'TIME_S_1KI': 0.05060563228637353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 748.6759082412719, 'W': 53.96}
+[18.98, 22.05, 19.95, 18.57, 19.29, 18.62, 18.75, 18.51, 18.71, 18.66, 46.37, 44.01, 43.56, 43.72, 44.21, 42.86, 34.87, 24.18, 33.73, 43.58]
+529.385
+26.46925
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 206949, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'coo', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.472784996032715, 'TIME_S_1KI': 0.05060563228637353, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 748.6759082412719, 'W': 53.96, 'J_1KI': 3.617683140490034, 'W_1KI': 0.2607405689324423, 'W_D': 27.490750000000002, 'J_D': 381.4244296605587, 'W_D_1KI': 0.13283828382838284, 'J_D_1KI': 0.0006418889863124867}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json
index b0c2a19..26904cb 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 33525, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.643972158432007, "TIME_S_1KI": 0.3174935766870099, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1221.4049115991593, "W": 88.39, "J_1KI": 36.43265955553048, "W_1KI": 2.6365398956002983, "W_D": 72.1155, "J_D": 996.5179986698627, "W_D_1KI": 2.151096196868009, "J_D_1KI": 0.06416394323245365}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 31035, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.417267084121704, "TIME_S_1KI": 0.335661900567801, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1250.4872237539291, "W": 90.01, "J_1KI": 40.292805663087776, "W_1KI": 2.9002738843241502, "W_D": 73.13425000000001, "J_D": 1016.0364986537696, "W_D_1KI": 2.3565087804092157, "J_D_1KI": 0.0759306840795623}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output
index dc5d053..6056924 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output
@@ -1,34 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.04636812210083008}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.04634833335876465}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       5,      13,  ...,  999983,
-                             999994, 1000000]),
-       col_indices=tensor([ 8176, 34026, 54478,  ..., 84998, 92494, 98961]),
-       values=tensor([0.4351, 0.9999, 0.3437,  ..., 0.3684, 0.7357, 0.5729]),
-       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8699, 0.2767, 0.3378,  ..., 0.5349, 0.7243, 0.7857])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([100000, 100000])
-Rows: 100000
-Size: 10000000000
-NNZ: 1000000
-Density: 0.0001
-Time: 0.04636812210083008 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22644', '-ss', '100000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.091935634613037}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       6,      23,  ...,  999978,
+tensor(crow_indices=tensor([      0,      10,      18,  ...,  999987,
                              999991, 1000000]),
-       col_indices=tensor([26374, 42582, 44652,  ..., 65952, 74293, 78884]),
-       values=tensor([0.4256, 0.1611, 0.6127,  ..., 0.5242, 0.3400, 0.0348]),
+       col_indices=tensor([ 9174, 26658, 55661,  ..., 82470, 90862, 91615]),
+       values=tensor([0.8356, 0.1194, 0.9715,  ..., 0.4592, 0.1175, 0.5527]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.0369, 0.6183, 0.8933,  ..., 0.8293, 0.4628, 0.9829])
+tensor([0.8705, 0.7209, 0.9154,  ..., 0.3203, 0.9994, 0.8441])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 7.091935634613037 seconds
+Time: 0.04634833335876465 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33525', '-ss', '100000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.643972158432007}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22654', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.664235591888428}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       7,      17,  ...,  999978,
-                             999994, 1000000]),
-       col_indices=tensor([ 9594, 11946, 25379,  ..., 52892, 57506, 73818]),
-       values=tensor([0.4978, 0.8076, 0.6002,  ..., 0.2925, 0.4675, 0.5122]),
+tensor(crow_indices=tensor([      0,       8,      20,  ...,  999979,
+                             999993, 1000000]),
+       col_indices=tensor([ 4251, 20168, 21247,  ..., 95888, 96747, 99036]),
+       values=tensor([0.8194, 0.4549, 0.8524,  ..., 0.6296, 0.3982, 0.3368]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8765, 0.8954, 0.8874,  ..., 0.8137, 0.3245, 0.8007])
+tensor([0.9515, 0.7824, 0.2226,  ..., 0.1315, 0.9870, 0.7032])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.643972158432007 seconds
+Time: 7.664235591888428 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '31035', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.417267084121704}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       7,      17,  ...,  999978,
-                             999994, 1000000]),
-       col_indices=tensor([ 9594, 11946, 25379,  ..., 52892, 57506, 73818]),
-       values=tensor([0.4978, 0.8076, 0.6002,  ..., 0.2925, 0.4675, 0.5122]),
+tensor(crow_indices=tensor([      0,       9,      21,  ...,  999970,
+                             999984, 1000000]),
+       col_indices=tensor([ 7852,  7977, 10481,  ..., 74970, 78788, 85228]),
+       values=tensor([0.0442, 0.5786, 0.0396,  ..., 0.2412, 0.9148, 0.4837]),
        size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8765, 0.8954, 0.8874,  ..., 0.8137, 0.3245, 0.8007])
+tensor([0.5025, 0.3489, 0.0390,  ..., 0.2046, 0.9645, 0.0609])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +56,30 @@ Rows: 100000
 Size: 10000000000
 NNZ: 1000000
 Density: 0.0001
-Time: 10.643972158432007 seconds
+Time: 10.417267084121704 seconds
 
-[18.37, 17.76, 17.76, 17.79, 18.13, 17.85, 18.03, 17.72, 18.33, 18.48]
-[88.39]
-13.818360805511475
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33525, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.643972158432007, 'TIME_S_1KI': 0.3174935766870099, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.4049115991593, 'W': 88.39}
-[18.37, 17.76, 17.76, 17.79, 18.13, 17.85, 18.03, 17.72, 18.33, 18.48, 18.41, 18.08, 18.64, 18.55, 18.21, 18.02, 18.07, 18.01, 17.96, 17.9]
-325.49
-16.2745
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33525, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.643972158432007, 'TIME_S_1KI': 0.3174935766870099, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.4049115991593, 'W': 88.39, 'J_1KI': 36.43265955553048, 'W_1KI': 2.6365398956002983, 'W_D': 72.1155, 'J_D': 996.5179986698627, 'W_D_1KI': 2.151096196868009, 'J_D_1KI': 0.06416394323245365}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      21,  ...,  999970,
+                             999984, 1000000]),
+       col_indices=tensor([ 7852,  7977, 10481,  ..., 74970, 78788, 85228]),
+       values=tensor([0.0442, 0.5786, 0.0396,  ..., 0.2412, 0.9148, 0.4837]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.5025, 0.3489, 0.0390,  ..., 0.2046, 0.9645, 0.0609])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.417267084121704 seconds
+
+[19.13, 18.7, 18.69, 18.52, 18.58, 18.87, 18.64, 18.46, 18.79, 18.85]
+[90.01]
+13.892758846282959
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 31035, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.417267084121704, 'TIME_S_1KI': 0.335661900567801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1250.4872237539291, 'W': 90.01}
+[19.13, 18.7, 18.69, 18.52, 18.58, 18.87, 18.64, 18.46, 18.79, 18.85, 19.33, 18.93, 18.71, 18.54, 18.93, 18.87, 18.59, 18.87, 18.96, 18.42]
+337.515
+16.87575
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 31035, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.417267084121704, 'TIME_S_1KI': 0.335661900567801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1250.4872237539291, 'W': 90.01, 'J_1KI': 40.292805663087776, 'W_1KI': 2.9002738843241502, 'W_D': 73.13425000000001, 'J_D': 1016.0364986537696, 'W_D_1KI': 2.3565087804092157, 'J_D_1KI': 0.0759306840795623}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json
index 7fe967e..54d8019 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2660, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.18576693534851, "TIME_S_1KI": 3.829235689980643, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1328.5220910072328, "W": 81.2, "J_1KI": 499.44439511550104, "W_1KI": 30.526315789473685, "W_D": 64.453, "J_D": 1054.5225902917387, "W_D_1KI": 24.23045112781955, "J_D_1KI": 9.10919215331562}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2624, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.40669846534729, "TIME_S_1KI": 3.965967402952473, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1365.3930686187744, "W": 83.52, "J_1KI": 520.3479682236183, "W_1KI": 31.829268292682926, "W_D": 65.95025, "J_D": 1078.161089842856, "W_D_1KI": 25.133479420731707, "J_D_1KI": 9.578307706071534}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.output
index 8cde86c..5fd31c6 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.39462947845458984}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.40013909339904785}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      104,      208,  ...,  9999814,
-                             9999906, 10000000]),
-       col_indices=tensor([ 1924,  2222,  4663,  ..., 98435, 98556, 99127]),
-       values=tensor([0.9193, 0.2961, 0.8826,  ..., 0.2999, 0.4100, 0.0457]),
+tensor(crow_indices=tensor([       0,       98,      192,  ...,  9999815,
+                             9999910, 10000000]),
+       col_indices=tensor([  373,  1519,  1794,  ..., 95818, 96513, 98259]),
+       values=tensor([0.4006, 0.4074, 0.2483,  ..., 0.9856, 0.5266, 0.6598]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8186, 0.3714, 0.9798,  ..., 0.9009, 0.9275, 0.2252])
+tensor([0.1859, 0.5690, 0.3331,  ..., 0.2308, 0.3812, 0.0144])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 0.39462947845458984 seconds
+Time: 0.40013909339904785 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2660', '-ss', '100000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.18576693534851}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2624', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.40669846534729}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      111,      209,  ...,  9999785,
-                             9999908, 10000000]),
-       col_indices=tensor([ 4849,  5332,  5597,  ..., 99100, 99293, 99777]),
-       values=tensor([0.3984, 0.3126, 0.3684,  ..., 0.2469, 0.5703, 0.8605]),
+tensor(crow_indices=tensor([       0,      117,      216,  ...,  9999790,
+                             9999902, 10000000]),
+       col_indices=tensor([  442,  3973,  5538,  ..., 98494, 99309, 99448]),
+       values=tensor([0.2013, 0.4098, 0.8755,  ..., 0.3384, 0.3727, 0.3410]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.6012, 0.7247, 0.1820,  ..., 0.8515, 0.6518, 0.6577])
+tensor([0.9970, 0.2280, 0.7030,  ..., 0.1949, 0.4690, 0.9782])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,16 +36,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 10.18576693534851 seconds
+Time: 10.40669846534729 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      111,      209,  ...,  9999785,
-                             9999908, 10000000]),
-       col_indices=tensor([ 4849,  5332,  5597,  ..., 99100, 99293, 99777]),
-       values=tensor([0.3984, 0.3126, 0.3684,  ..., 0.2469, 0.5703, 0.8605]),
+tensor(crow_indices=tensor([       0,      117,      216,  ...,  9999790,
+                             9999902, 10000000]),
+       col_indices=tensor([  442,  3973,  5538,  ..., 98494, 99309, 99448]),
+       values=tensor([0.2013, 0.4098, 0.8755,  ..., 0.3384, 0.3727, 0.3410]),
        size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.6012, 0.7247, 0.1820,  ..., 0.8515, 0.6518, 0.6577])
+tensor([0.9970, 0.2280, 0.7030,  ..., 0.1949, 0.4690, 0.9782])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -53,13 +53,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 10000000
 Density: 0.001
-Time: 10.18576693534851 seconds
+Time: 10.40669846534729 seconds
 
-[18.33, 18.01, 18.45, 18.12, 17.88, 17.91, 18.33, 22.42, 18.21, 17.9]
-[81.2]
-16.361109495162964
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2660, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.18576693534851, 'TIME_S_1KI': 3.829235689980643, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1328.5220910072328, 'W': 81.2}
-[18.33, 18.01, 18.45, 18.12, 17.88, 17.91, 18.33, 22.42, 18.21, 17.9, 22.63, 20.65, 18.09, 18.03, 18.02, 18.26, 18.0, 18.17, 17.93, 18.06]
-334.94000000000005
-16.747000000000003
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2660, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.18576693534851, 'TIME_S_1KI': 3.829235689980643, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1328.5220910072328, 'W': 81.2, 'J_1KI': 499.44439511550104, 'W_1KI': 30.526315789473685, 'W_D': 64.453, 'J_D': 1054.5225902917387, 'W_D_1KI': 24.23045112781955, 'J_D_1KI': 9.10919215331562}
+[19.16, 22.25, 18.95, 19.07, 19.39, 18.46, 18.69, 18.46, 18.92, 18.39]
+[83.52]
+16.34809708595276
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2624, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.40669846534729, 'TIME_S_1KI': 3.965967402952473, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1365.3930686187744, 'W': 83.52}
+[19.16, 22.25, 18.95, 19.07, 19.39, 18.46, 18.69, 18.46, 18.92, 18.39, 19.12, 26.91, 20.35, 18.41, 18.57, 18.57, 19.27, 18.75, 18.82, 18.44]
+351.395
+17.56975
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2624, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.40669846534729, 'TIME_S_1KI': 3.965967402952473, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1365.3930686187744, 'W': 83.52, 'J_1KI': 520.3479682236183, 'W_1KI': 31.829268292682926, 'W_D': 65.95025, 'J_D': 1078.161089842856, 'W_D_1KI': 25.133479420731707, 'J_D_1KI': 9.578307706071534}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.01.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.01.json
new file mode 100644
index 0000000..45279f5
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 305, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.495978593826294, "TIME_S_1KI": 34.41304456992227, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2866.215010499954, "W": 65.24, "J_1KI": 9397.426263934276, "W_1KI": 213.9016393442623, "W_D": 48.390249999999995, "J_D": 2125.948205270469, "W_D_1KI": 158.65655737704918, "J_D_1KI": 520.1854340231122}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.01.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.01.output
new file mode 100644
index 0000000..59f7c6e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.439396381378174}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      1014,      1961,  ...,
+                             99997966,  99998994, 100000000]),
+       col_indices=tensor([  103,   169,   257,  ..., 99672, 99692, 99993]),
+       values=tensor([0.3305, 0.0600, 0.8724,  ..., 0.9250, 0.3666, 0.7528]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.3599, 0.7206, 0.4374,  ..., 0.1863, 0.8638, 0.5657])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 3.439396381378174 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '305', '-ss', '100000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.495978593826294}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       990,      2000,  ...,
+                             99998044,  99999023, 100000000]),
+       col_indices=tensor([  187,   214,   378,  ..., 99725, 99778, 99993]),
+       values=tensor([0.2904, 0.8403, 0.4739,  ..., 0.8094, 0.3153, 0.0027]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.6334, 0.8307, 0.2969,  ..., 0.6260, 0.4306, 0.6036])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 10.495978593826294 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,       990,      2000,  ...,
+                             99998044,  99999023, 100000000]),
+       col_indices=tensor([  187,   214,   378,  ..., 99725, 99778, 99993]),
+       values=tensor([0.2904, 0.8403, 0.4739,  ..., 0.8094, 0.3153, 0.0027]),
+       size=(100000, 100000), nnz=100000000, layout=torch.sparse_csr)
+tensor([0.6334, 0.8307, 0.2969,  ..., 0.6260, 0.4306, 0.6036])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000000
+Density: 0.01
+Time: 10.495978593826294 seconds
+
+[18.83, 18.53, 18.8, 18.61, 18.65, 18.57, 19.18, 18.51, 18.59, 18.76]
+[65.24]
+43.93339991569519
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 305, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.495978593826294, 'TIME_S_1KI': 34.41304456992227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2866.215010499954, 'W': 65.24}
+[18.83, 18.53, 18.8, 18.61, 18.65, 18.57, 19.18, 18.51, 18.59, 18.76, 19.05, 18.56, 18.94, 18.42, 18.92, 18.36, 18.78, 18.44, 18.63, 20.37]
+336.995
+16.84975
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 305, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.495978593826294, 'TIME_S_1KI': 34.41304456992227, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2866.215010499954, 'W': 65.24, 'J_1KI': 9397.426263934276, 'W_1KI': 213.9016393442623, 'W_D': 48.390249999999995, 'J_D': 2125.948205270469, 'W_D_1KI': 158.65655737704918, 'J_D_1KI': 520.1854340231122}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json
index fdd737d..5c14190 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 64522, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.3600492477417, "TIME_S_1KI": 0.1605661518201807, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1143.5515343093873, "W": 83.36000000000001, "J_1KI": 17.723435949124134, "W_1KI": 1.291962431418741, "W_D": 66.67275000000001, "J_D": 914.6320244616867, "W_D_1KI": 1.0333335916431607, "J_D_1KI": 0.01601521328605996}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 66137, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.77026081085205, "TIME_S_1KI": 0.16284773743671546, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1274.6182242155076, "W": 90.22, "J_1KI": 19.27239252181846, "W_1KI": 1.3641380770219393, "W_D": 73.104, "J_D": 1032.8052611732483, "W_D_1KI": 1.1053419417270212, "J_D_1KI": 0.016712913221449736}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output
index 8218685..abb77a3 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.03250718116760254}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.034224748611450195}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([     0,      0,      1,  ...,  99998,  99998,
                             100000]),
-       col_indices=tensor([12882, 21465, 63858,  ..., 96153,  4715, 69382]),
-       values=tensor([0.1495, 0.9028, 0.7353,  ..., 0.9651, 0.0553, 0.0388]),
+       col_indices=tensor([80329, 97577, 78460,  ..., 47469, 32746, 34200]),
+       values=tensor([0.4872, 0.5808, 0.9619,  ..., 0.7805, 0.1079, 0.3610]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6296, 0.2324, 0.7696,  ..., 0.0819, 0.5051, 0.6795])
+tensor([0.1059, 0.5996, 0.0895,  ..., 0.5540, 0.1364, 0.6817])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 0.03250718116760254 seconds
+Time: 0.034224748611450195 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '32300', '-ss', '100000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.256327390670776}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '30679', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.870610475540161}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      3,  ...,  99998,  99999,
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99999, 100000,
                             100000]),
-       col_indices=tensor([24100, 22524, 41698,  ..., 71518, 54296, 46275]),
-       values=tensor([0.6729, 0.0195, 0.1396,  ..., 0.6516, 0.4177, 0.7883]),
+       col_indices=tensor([55380,  3096, 82701,  ..., 85443, 78227, 98923]),
+       values=tensor([0.5422, 0.0604, 0.4016,  ..., 0.7321, 0.2904, 0.5556]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.6330, 0.0132, 0.4522,  ..., 0.1249, 0.8426, 0.7168])
+tensor([0.5851, 0.5126, 0.9039,  ..., 0.8660, 0.2417, 0.9391])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 5.256327390670776 seconds
+Time: 4.870610475540161 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '64522', '-ss', '100000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.3600492477417}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '66137', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.77026081085205}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      1,  ...,  99996,  99999,
+tensor(crow_indices=tensor([     0,      2,      4,  ...,  99998,  99998,
                             100000]),
-       col_indices=tensor([63372, 90175, 43637,  ..., 48404, 84175, 41742]),
-       values=tensor([0.0143, 0.7083, 0.4138,  ..., 0.7171, 0.1589, 0.0907]),
+       col_indices=tensor([54463, 88672, 26831,  ..., 14962, 24051, 50951]),
+       values=tensor([0.0312, 0.6855, 0.2959,  ..., 0.0652, 0.5066, 0.5715]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2672, 0.1313, 0.6231,  ..., 0.4829, 0.5251, 0.7815])
+tensor([0.6086, 0.3249, 0.3975,  ..., 0.5070, 0.0839, 0.6292])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.3600492477417 seconds
+Time: 10.77026081085205 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      1,      1,  ...,  99996,  99999,
+tensor(crow_indices=tensor([     0,      2,      4,  ...,  99998,  99998,
                             100000]),
-       col_indices=tensor([63372, 90175, 43637,  ..., 48404, 84175, 41742]),
-       values=tensor([0.0143, 0.7083, 0.4138,  ..., 0.7171, 0.1589, 0.0907]),
+       col_indices=tensor([54463, 88672, 26831,  ..., 14962, 24051, 50951]),
+       values=tensor([0.0312, 0.6855, 0.2959,  ..., 0.0652, 0.5066, 0.5715]),
        size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.2672, 0.1313, 0.6231,  ..., 0.4829, 0.5251, 0.7815])
+tensor([0.6086, 0.3249, 0.3975,  ..., 0.5070, 0.0839, 0.6292])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 100000
 Density: 1e-05
-Time: 10.3600492477417 seconds
+Time: 10.77026081085205 seconds
 
-[18.53, 17.8, 18.06, 18.37, 18.25, 17.96, 18.14, 21.36, 18.43, 17.88]
-[83.36]
-13.718228578567505
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64522, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.3600492477417, 'TIME_S_1KI': 0.1605661518201807, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1143.5515343093873, 'W': 83.36000000000001}
-[18.53, 17.8, 18.06, 18.37, 18.25, 17.96, 18.14, 21.36, 18.43, 17.88, 18.55, 17.91, 18.58, 19.12, 20.8, 18.14, 18.33, 17.85, 18.25, 17.83]
-333.745
-16.68725
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64522, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.3600492477417, 'TIME_S_1KI': 0.1605661518201807, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1143.5515343093873, 'W': 83.36000000000001, 'J_1KI': 17.723435949124134, 'W_1KI': 1.291962431418741, 'W_D': 66.67275000000001, 'J_D': 914.6320244616867, 'W_D_1KI': 1.0333335916431607, 'J_D_1KI': 0.01601521328605996}
+[19.11, 18.48, 18.88, 20.9, 19.07, 19.66, 19.46, 18.54, 19.32, 18.58]
+[90.22]
+14.12788987159729
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 66137, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.77026081085205, 'TIME_S_1KI': 0.16284773743671546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.6182242155076, 'W': 90.22}
+[19.11, 18.48, 18.88, 20.9, 19.07, 19.66, 19.46, 18.54, 19.32, 18.58, 19.93, 18.36, 18.99, 18.76, 18.99, 18.64, 18.96, 18.38, 18.86, 18.52]
+342.32
+17.116
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 66137, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.77026081085205, 'TIME_S_1KI': 0.16284773743671546, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.6182242155076, 'W': 90.22, 'J_1KI': 19.27239252181846, 'W_1KI': 1.3641380770219393, 'W_D': 73.104, 'J_D': 1032.8052611732483, 'W_D_1KI': 1.1053419417270212, 'J_D_1KI': 0.016712913221449736}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.json
index 07c28cc..06bc5c6 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 46682, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.460205316543579, "TIME_S_1KI": 0.22407363258951157, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1228.6304648017883, "W": 87.08, "J_1KI": 26.319147954281917, "W_1KI": 1.865387087099953, "W_D": 70.695, "J_D": 997.4509727740286, "W_D_1KI": 1.51439527012553, "J_D_1KI": 0.03244066814030097}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 45734, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.604183673858643, "TIME_S_1KI": 0.2318665254265676, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1283.9346827626227, "W": 90.49, "J_1KI": 28.073964288333027, "W_1KI": 1.9786154720776665, "W_D": 73.52924999999999, "J_D": 1043.283835479319, "W_D_1KI": 1.6077589976822493, "J_D_1KI": 0.03515456766699281}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.output
index 0398e28..630ea89 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.03882646560668945}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.03907012939453125}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      4,     12,  ..., 499993, 499995,
+tensor(crow_indices=tensor([     0,      1,      7,  ..., 499989, 499996,
                             500000]),
-       col_indices=tensor([20130, 29829, 49027,  ..., 32515, 51857, 99803]),
-       values=tensor([0.4194, 0.2208, 0.8236,  ..., 0.3620, 0.7637, 0.5129]),
+       col_indices=tensor([19574,  2426, 28612,  ..., 14575, 58009, 84398]),
+       values=tensor([0.7192, 0.7689, 0.7517,  ..., 0.7221, 0.6786, 0.5171]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.7254, 0.4636, 0.7256,  ..., 0.4819, 0.1264, 0.5273])
+tensor([0.6963, 0.4880, 0.9045,  ..., 0.8071, 0.4138, 0.4778])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -16,19 +16,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 0.03882646560668945 seconds
+Time: 0.03907012939453125 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '27043', '-ss', '100000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.0826029777526855}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '26874', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.169933795928955}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     14,  ..., 499987, 499992,
+tensor(crow_indices=tensor([     0,      6,     14,  ..., 499992, 499996,
                             500000]),
-       col_indices=tensor([19007, 19428, 24486,  ..., 87536, 92504, 96559]),
-       values=tensor([0.8398, 0.0370, 0.5128,  ..., 0.3625, 0.4907, 0.6853]),
+       col_indices=tensor([27921, 32385, 39293,  ..., 21232, 30711, 67562]),
+       values=tensor([0.2888, 0.7317, 0.9554,  ..., 0.7337, 0.6318, 0.6969]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.6134, 0.6519, 0.5356,  ..., 0.0589, 0.6530, 0.3358])
+tensor([0.2864, 0.2821, 0.7119,  ..., 0.8913, 0.8685, 0.7106])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -36,19 +36,19 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 6.0826029777526855 seconds
+Time: 6.169933795928955 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '46682', '-ss', '100000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.460205316543579}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '45734', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.604183673858643}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     11,     19,  ..., 499992, 499995,
+tensor(crow_indices=tensor([     0,      8,     16,  ..., 499993, 499996,
                             500000]),
-       col_indices=tensor([ 9218, 31473, 33160,  ..., 57052, 72094, 94375]),
-       values=tensor([0.1819, 0.5310, 0.0116,  ..., 0.3541, 0.3048, 0.3110]),
+       col_indices=tensor([ 3064, 12217, 39810,  ..., 67896, 79198, 93701]),
+       values=tensor([0.1389, 0.9146, 0.3747,  ..., 0.3963, 0.8562, 0.9389]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.6007, 0.7506, 0.6846,  ..., 0.1657, 0.4869, 0.7821])
+tensor([0.9110, 0.2994, 0.1061,  ..., 0.1379, 0.8132, 0.9589])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -56,16 +56,16 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.460205316543579 seconds
+Time: 10.604183673858643 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     11,     19,  ..., 499992, 499995,
+tensor(crow_indices=tensor([     0,      8,     16,  ..., 499993, 499996,
                             500000]),
-       col_indices=tensor([ 9218, 31473, 33160,  ..., 57052, 72094, 94375]),
-       values=tensor([0.1819, 0.5310, 0.0116,  ..., 0.3541, 0.3048, 0.3110]),
+       col_indices=tensor([ 3064, 12217, 39810,  ..., 67896, 79198, 93701]),
+       values=tensor([0.1389, 0.9146, 0.3747,  ..., 0.3963, 0.8562, 0.9389]),
        size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
-tensor([0.6007, 0.7506, 0.6846,  ..., 0.1657, 0.4869, 0.7821])
+tensor([0.9110, 0.2994, 0.1061,  ..., 0.1379, 0.8132, 0.9589])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([100000, 100000])
@@ -73,13 +73,13 @@ Rows: 100000
 Size: 10000000000
 NNZ: 500000
 Density: 5e-05
-Time: 10.460205316543579 seconds
+Time: 10.604183673858643 seconds
 
-[18.32, 17.95, 18.46, 18.2, 18.15, 17.94, 18.28, 18.06, 18.31, 18.04]
-[87.08]
-14.109215259552002
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.460205316543579, 'TIME_S_1KI': 0.22407363258951157, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1228.6304648017883, 'W': 87.08}
-[18.32, 17.95, 18.46, 18.2, 18.15, 17.94, 18.28, 18.06, 18.31, 18.04, 18.26, 18.28, 17.89, 18.48, 18.48, 18.03, 17.99, 18.35, 18.44, 18.2]
-327.69999999999993
-16.384999999999998
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.460205316543579, 'TIME_S_1KI': 0.22407363258951157, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1228.6304648017883, 'W': 87.08, 'J_1KI': 26.319147954281917, 'W_1KI': 1.865387087099953, 'W_D': 70.695, 'J_D': 997.4509727740286, 'W_D_1KI': 1.51439527012553, 'J_D_1KI': 0.03244066814030097}
+[19.02, 18.68, 18.7, 18.5, 18.62, 18.64, 18.56, 18.68, 18.53, 18.56]
+[90.49]
+14.18869137763977
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 45734, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.604183673858643, 'TIME_S_1KI': 0.2318665254265676, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1283.9346827626227, 'W': 90.49}
+[19.02, 18.68, 18.7, 18.5, 18.62, 18.64, 18.56, 18.68, 18.53, 18.56, 20.14, 18.55, 18.7, 18.52, 18.59, 18.35, 19.17, 21.09, 18.78, 19.39]
+339.21500000000003
+16.96075
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 45734, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.604183673858643, 'TIME_S_1KI': 0.2318665254265676, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1283.9346827626227, 'W': 90.49, 'J_1KI': 28.073964288333027, 'W_1KI': 1.9786154720776665, 'W_D': 73.52924999999999, 'J_D': 1043.283835479319, 'W_D_1KI': 1.6077589976822493, 'J_D_1KI': 0.03515456766699281}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json
index 1b64231..0b1246f 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 253108, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.677898168563843, "TIME_S_1KI": 0.0421871223689644, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1070.6826658773423, "W": 74.61, "J_1KI": 4.230141543836395, "W_1KI": 0.2947753528138186, "W_D": 58.27775, "J_D": 836.3084939194918, "W_D_1KI": 0.23024855002607583, "J_D_1KI": 0.0009096849962311576}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 250235, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.277639150619507, "TIME_S_1KI": 0.04107194897044581, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1144.8245710134506, "W": 82.67, "J_1KI": 4.574997786134836, "W_1KI": 0.3303694527144484, "W_D": 65.82124999999999, "J_D": 911.5009591728448, "W_D_1KI": 0.26303774452015105, "J_D_1KI": 0.0010511628849687335}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output
index 1ec4855..793dab9 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.02065730094909668}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.019877910614013672}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     3,     4,  ...,  9998,  9998, 10000]),
-       col_indices=tensor([6728, 7614, 8179,  ..., 1004, 2058, 8025]),
-       values=tensor([0.0279, 0.0803, 0.4096,  ..., 0.0871, 0.5549, 0.2943]),
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([ 874, 1837, 3843,  ...,  101,  741, 1075]),
+       values=tensor([0.5637, 0.3942, 0.0462,  ..., 0.4346, 0.2450, 0.2131]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.5482, 0.4991, 0.5547,  ..., 0.6547, 0.2547, 0.5094])
+tensor([0.7228, 0.7678, 0.4139,  ..., 0.0364, 0.2913, 0.8096])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 0.02065730094909668 seconds
+Time: 0.019877910614013672 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '50829', '-ss', '10000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.10860276222229}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52822', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.2164390087127686}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     1,  ..., 10000, 10000, 10000]),
-       col_indices=tensor([5153, 2587, 4463,  ..., 5061, 9520, 1424]),
-       values=tensor([0.2204, 0.6183, 0.5613,  ..., 0.3086, 0.3306, 0.5938]),
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9999, 10000, 10000]),
+       col_indices=tensor([1121, 8248, 9956,  ..., 4131, 2037, 5129]),
+       values=tensor([0.0817, 0.3609, 0.8105,  ..., 0.0978, 0.3572, 0.8156]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.9842, 0.7142, 0.2121,  ..., 0.3434, 0.1561, 0.6145])
+tensor([0.2927, 0.3945, 0.6867,  ..., 0.0610, 0.2872, 0.3991])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 2.10860276222229 seconds
+Time: 2.2164390087127686 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '253108', '-ss', '10000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.677898168563843}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '250235', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.277639150619507}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     4,  ...,  9999,  9999, 10000]),
-       col_indices=tensor([7291,  527, 4481,  ..., 6785, 7922, 1484]),
-       values=tensor([0.3434, 0.3822, 0.2401,  ..., 0.8298, 0.4309, 0.4668]),
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([2242, 8860, 3854,  ..., 6182, 4773, 9388]),
+       values=tensor([0.8365, 0.4219, 0.7780,  ..., 0.3976, 0.7864, 0.8763]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.2882, 0.0253, 0.9805,  ..., 0.1323, 0.6315, 0.0794])
+tensor([0.3427, 0.7236, 0.7947,  ..., 0.1998, 0.1922, 0.6155])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.677898168563843 seconds
+Time: 10.277639150619507 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     4,  ...,  9999,  9999, 10000]),
-       col_indices=tensor([7291,  527, 4481,  ..., 6785, 7922, 1484]),
-       values=tensor([0.3434, 0.3822, 0.2401,  ..., 0.8298, 0.4309, 0.4668]),
+tensor(crow_indices=tensor([    0,     1,     1,  ...,  9997,  9998, 10000]),
+       col_indices=tensor([2242, 8860, 3854,  ..., 6182, 4773, 9388]),
+       values=tensor([0.8365, 0.4219, 0.7780,  ..., 0.3976, 0.7864, 0.8763]),
        size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
-tensor([0.2882, 0.0253, 0.9805,  ..., 0.1323, 0.6315, 0.0794])
+tensor([0.3427, 0.7236, 0.7947,  ..., 0.1998, 0.1922, 0.6155])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000
 Density: 0.0001
-Time: 10.677898168563843 seconds
+Time: 10.277639150619507 seconds
 
-[18.38, 17.89, 18.34, 17.77, 18.06, 17.77, 18.13, 17.86, 17.98, 18.23]
-[74.61]
-14.350390911102295
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253108, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.677898168563843, 'TIME_S_1KI': 0.0421871223689644, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1070.6826658773423, 'W': 74.61}
-[18.38, 17.89, 18.34, 17.77, 18.06, 17.77, 18.13, 17.86, 17.98, 18.23, 18.53, 18.0, 18.04, 18.11, 18.16, 18.08, 18.2, 18.27, 19.29, 18.25]
-326.645
-16.33225
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253108, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.677898168563843, 'TIME_S_1KI': 0.0421871223689644, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1070.6826658773423, 'W': 74.61, 'J_1KI': 4.230141543836395, 'W_1KI': 0.2947753528138186, 'W_D': 58.27775, 'J_D': 836.3084939194918, 'W_D_1KI': 0.23024855002607583, 'J_D_1KI': 0.0009096849962311576}
+[19.27, 19.16, 18.76, 18.51, 18.49, 18.73, 18.68, 19.0, 18.57, 18.98]
+[82.67]
+13.84812593460083
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 250235, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.277639150619507, 'TIME_S_1KI': 0.04107194897044581, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1144.8245710134506, 'W': 82.67}
+[19.27, 19.16, 18.76, 18.51, 18.49, 18.73, 18.68, 19.0, 18.57, 18.98, 19.5, 18.48, 18.63, 18.9, 18.47, 18.55, 18.63, 18.86, 18.44, 18.48]
+336.975
+16.848750000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 250235, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.277639150619507, 'TIME_S_1KI': 0.04107194897044581, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1144.8245710134506, 'W': 82.67, 'J_1KI': 4.574997786134836, 'W_1KI': 0.3303694527144484, 'W_D': 65.82124999999999, 'J_D': 911.5009591728448, 'W_D_1KI': 0.26303774452015105, 'J_D_1KI': 0.0010511628849687335}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json
index 090450b..4039661 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 194593, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.473386764526367, "TIME_S_1KI": 0.053822011914747024, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1083.1964892935753, "W": 79.61, "J_1KI": 5.566472017459905, "W_1KI": 0.40911029687604383, "W_D": 62.8515, "J_D": 855.175532556653, "W_D_1KI": 0.3229895217196919, "J_D_1KI": 0.001659820865702733}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 195448, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.777343273162842, "TIME_S_1KI": 0.05514174242337012, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1223.9583963775635, "W": 87.73, "J_1KI": 6.2623224406367095, "W_1KI": 0.4488661945888421, "W_D": 70.56325000000001, "J_D": 984.4577945194246, "W_D_1KI": 0.3610333694895829, "J_D_1KI": 0.001847209331840607}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output
index 8858416..b7388c3 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.020806312561035156}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.022109508514404297}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      7,     14,  ...,  99985,  99993,
+tensor(crow_indices=tensor([     0,     12,     20,  ...,  99984,  99991,
                             100000]),
-       col_indices=tensor([5438, 7119, 8479,  ..., 6797, 6979, 8109]),
-       values=tensor([0.2056, 0.5255, 0.6332,  ..., 0.1682, 0.9365, 0.4633]),
+       col_indices=tensor([ 774, 3258, 4252,  ..., 8619, 8738, 9232]),
+       values=tensor([0.1489, 0.7206, 0.7178,  ..., 0.3422, 0.0839, 0.6783]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.0316, 0.8438, 0.1562,  ..., 0.6730, 0.8332, 0.9126])
+tensor([0.2668, 0.6762, 0.7372,  ..., 0.4714, 0.8259, 0.5362])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 0.020806312561035156 seconds
+Time: 0.022109508514404297 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '50465', '-ss', '10000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.723015785217285}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '47490', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.551285982131958}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     18,     34,  ...,  99978,  99989,
+tensor(crow_indices=tensor([     0,     10,     18,  ...,  99986,  99993,
                             100000]),
-       col_indices=tensor([ 818, 1321, 1616,  ..., 5603, 7366, 9704]),
-       values=tensor([0.8713, 0.7316, 0.2331,  ..., 0.6687, 0.3725, 0.7818]),
+       col_indices=tensor([ 697,  699, 1538,  ..., 3424, 5514, 9931]),
+       values=tensor([0.0664, 0.0593, 0.3496,  ..., 0.3706, 0.2364, 0.3729]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.5742, 0.6233, 0.0987,  ..., 0.1452, 0.2067, 0.1195])
+tensor([0.2684, 0.6711, 0.5550,  ..., 0.5107, 0.2291, 0.1240])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 2.723015785217285 seconds
+Time: 2.551285982131958 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '194593', '-ss', '10000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.473386764526367}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '195448', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.777343273162842}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     11,     16,  ...,  99982,  99989,
+tensor(crow_indices=tensor([     0,      9,     16,  ...,  99969,  99987,
                             100000]),
-       col_indices=tensor([1034, 3380, 4243,  ..., 7428, 9116, 9600]),
-       values=tensor([0.4227, 0.1092, 0.7794,  ..., 0.2113, 0.3090, 0.9237]),
+       col_indices=tensor([ 874, 3785, 4640,  ..., 8539, 9539, 9843]),
+       values=tensor([0.7163, 0.6418, 0.7384,  ..., 0.3361, 0.8554, 0.3985]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7442, 0.3504, 0.6358,  ..., 0.6138, 0.7536, 0.9226])
+tensor([0.3512, 0.5595, 0.5579,  ..., 0.0047, 0.4046, 0.7037])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.473386764526367 seconds
+Time: 10.777343273162842 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     11,     16,  ...,  99982,  99989,
+tensor(crow_indices=tensor([     0,      9,     16,  ...,  99969,  99987,
                             100000]),
-       col_indices=tensor([1034, 3380, 4243,  ..., 7428, 9116, 9600]),
-       values=tensor([0.4227, 0.1092, 0.7794,  ..., 0.2113, 0.3090, 0.9237]),
+       col_indices=tensor([ 874, 3785, 4640,  ..., 8539, 9539, 9843]),
+       values=tensor([0.7163, 0.6418, 0.7384,  ..., 0.3361, 0.8554, 0.3985]),
        size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
-tensor([0.7442, 0.3504, 0.6358,  ..., 0.6138, 0.7536, 0.9226])
+tensor([0.3512, 0.5595, 0.5579,  ..., 0.0047, 0.4046, 0.7037])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 100000
 Density: 0.001
-Time: 10.473386764526367 seconds
+Time: 10.777343273162842 seconds
 
-[19.08, 18.28, 17.95, 17.9, 19.17, 18.22, 19.03, 18.14, 18.71, 20.45]
-[79.61]
-13.606286764144897
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 194593, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.473386764526367, 'TIME_S_1KI': 0.053822011914747024, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1083.1964892935753, 'W': 79.61}
-[19.08, 18.28, 17.95, 17.9, 19.17, 18.22, 19.03, 18.14, 18.71, 20.45, 18.56, 18.36, 18.2, 17.96, 18.09, 18.17, 22.32, 18.37, 18.26, 17.99]
-335.17
-16.7585
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 194593, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.473386764526367, 'TIME_S_1KI': 0.053822011914747024, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1083.1964892935753, 'W': 79.61, 'J_1KI': 5.566472017459905, 'W_1KI': 0.40911029687604383, 'W_D': 62.8515, 'J_D': 855.175532556653, 'W_D_1KI': 0.3229895217196919, 'J_D_1KI': 0.001659820865702733}
+[19.04, 19.62, 18.71, 18.67, 18.54, 18.38, 18.47, 18.71, 20.97, 19.17]
+[87.73]
+13.951423645019531
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 195448, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.777343273162842, 'TIME_S_1KI': 0.05514174242337012, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1223.9583963775635, 'W': 87.73}
+[19.04, 19.62, 18.71, 18.67, 18.54, 18.38, 18.47, 18.71, 20.97, 19.17, 18.99, 18.59, 18.55, 18.4, 22.57, 19.05, 18.53, 19.0, 18.59, 18.77]
+343.33500000000004
+17.16675
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 195448, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.777343273162842, 'TIME_S_1KI': 0.05514174242337012, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1223.9583963775635, 'W': 87.73, 'J_1KI': 6.2623224406367095, 'W_1KI': 0.4488661945888421, 'W_D': 70.56325000000001, 'J_D': 984.4577945194246, 'W_D_1KI': 0.3610333694895829, 'J_D_1KI': 0.001847209331840607}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json
index 8c6811d..033e5d4 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 57740, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.343472957611084, "TIME_S_1KI": 0.17913877654331634, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1219.6624715328217, "W": 87.4, "J_1KI": 21.123354200429883, "W_1KI": 1.5136820228611017, "W_D": 71.01700000000001, "J_D": 991.0385553872587, "W_D_1KI": 1.2299445791479044, "J_D_1KI": 0.021301430189606934}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 55803, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.610237836837769, "TIME_S_1KI": 0.19013740904320142, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1270.6491134166718, "W": 89.9, "J_1KI": 22.770265279943224, "W_1KI": 1.6110244968908483, "W_D": 73.03275000000001, "J_D": 1032.246930343509, "W_D_1KI": 1.3087602817052848, "J_D_1KI": 0.023453224409176653}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output
index ff97c3a..7cde805 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.03377032279968262}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.03371858596801758}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     110,     219,  ...,  999820,
-                             999911, 1000000]),
-       col_indices=tensor([   0,   38,  265,  ..., 9703, 9904, 9960]),
-       values=tensor([0.3683, 0.9828, 0.4174,  ..., 0.4331, 0.2376, 0.7467]),
+tensor(crow_indices=tensor([      0,      99,     214,  ...,  999812,
+                             999912, 1000000]),
+       col_indices=tensor([  44,   58,   94,  ..., 9363, 9662, 9795]),
+       values=tensor([0.8470, 0.3153, 0.3716,  ..., 0.2634, 0.7038, 0.5929]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8519, 0.1445, 0.2230,  ..., 0.9278, 0.7204, 0.3614])
+tensor([0.6867, 0.9635, 0.5151,  ..., 0.5948, 0.1867, 0.0815])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 0.03377032279968262 seconds
+Time: 0.03371858596801758 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '31092', '-ss', '10000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 5.654046535491943}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '31140', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 5.859341859817505}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     102,     199,  ...,  999795,
-                             999900, 1000000]),
-       col_indices=tensor([ 113,  165,  189,  ..., 9912, 9940, 9996]),
-       values=tensor([0.2048, 0.9236, 0.8269,  ..., 0.2195, 0.4387, 0.6731]),
+tensor(crow_indices=tensor([      0,      96,     195,  ...,  999807,
+                             999901, 1000000]),
+       col_indices=tensor([   7,  260,  452,  ..., 9620, 9734, 9843]),
+       values=tensor([0.9634, 0.1244, 0.5347,  ..., 0.1267, 0.3210, 0.5779]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.8364, 0.2950, 0.2365,  ..., 0.2102, 0.7661, 0.3156])
+tensor([0.0916, 0.0848, 0.6723,  ..., 0.9740, 0.3856, 0.4112])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 5.654046535491943 seconds
+Time: 5.859341859817505 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '57740', '-ss', '10000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.343472957611084}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '55803', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.610237836837769}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     106,     216,  ...,  999804,
-                             999899, 1000000]),
-       col_indices=tensor([  61,   88,  117,  ..., 9666, 9676, 9799]),
-       values=tensor([0.7050, 0.8533, 0.9508,  ..., 0.3667, 0.6991, 0.8071]),
+tensor(crow_indices=tensor([      0,      90,     184,  ...,  999818,
+                             999908, 1000000]),
+       col_indices=tensor([   1,   41,  423,  ..., 9632, 9973, 9980]),
+       values=tensor([0.5680, 0.3426, 0.4538,  ..., 0.2093, 0.7467, 0.4938]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9939, 0.1893, 0.9694,  ..., 0.0779, 0.3428, 0.9229])
+tensor([0.0892, 0.5048, 0.6849,  ..., 0.8658, 0.9878, 0.2650])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,16 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.343472957611084 seconds
+Time: 10.610237836837769 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     106,     216,  ...,  999804,
-                             999899, 1000000]),
-       col_indices=tensor([  61,   88,  117,  ..., 9666, 9676, 9799]),
-       values=tensor([0.7050, 0.8533, 0.9508,  ..., 0.3667, 0.6991, 0.8071]),
+tensor(crow_indices=tensor([      0,      90,     184,  ...,  999818,
+                             999908, 1000000]),
+       col_indices=tensor([   1,   41,  423,  ..., 9632, 9973, 9980]),
+       values=tensor([0.5680, 0.3426, 0.4538,  ..., 0.2093, 0.7467, 0.4938]),
        size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
-tensor([0.9939, 0.1893, 0.9694,  ..., 0.0779, 0.3428, 0.9229])
+tensor([0.0892, 0.5048, 0.6849,  ..., 0.8658, 0.9878, 0.2650])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -73,13 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000000
 Density: 0.01
-Time: 10.343472957611084 seconds
+Time: 10.610237836837769 seconds
 
-[18.73, 18.07, 18.16, 18.31, 18.98, 17.81, 18.18, 18.17, 18.06, 18.83]
-[87.4]
-13.95494818687439
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 57740, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.343472957611084, 'TIME_S_1KI': 0.17913877654331634, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1219.6624715328217, 'W': 87.4}
-[18.73, 18.07, 18.16, 18.31, 18.98, 17.81, 18.18, 18.17, 18.06, 18.83, 18.31, 18.07, 18.19, 17.94, 18.17, 18.13, 18.04, 18.09, 18.22, 18.27]
-327.65999999999997
-16.383
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 57740, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.343472957611084, 'TIME_S_1KI': 0.17913877654331634, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1219.6624715328217, 'W': 87.4, 'J_1KI': 21.123354200429883, 'W_1KI': 1.5136820228611017, 'W_D': 71.01700000000001, 'J_D': 991.0385553872587, 'W_D_1KI': 1.2299445791479044, 'J_D_1KI': 0.021301430189606934}
+[18.89, 18.41, 18.79, 18.72, 18.62, 18.97, 18.94, 18.42, 18.71, 18.3]
+[89.9]
+14.13402795791626
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 55803, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.610237836837769, 'TIME_S_1KI': 0.19013740904320142, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1270.6491134166718, 'W': 89.9}
+[18.89, 18.41, 18.79, 18.72, 18.62, 18.97, 18.94, 18.42, 18.71, 18.3, 19.28, 18.73, 18.54, 18.62, 18.82, 18.47, 19.62, 18.44, 19.04, 18.5]
+337.345
+16.867250000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 55803, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.610237836837769, 'TIME_S_1KI': 0.19013740904320142, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1270.6491134166718, 'W': 89.9, 'J_1KI': 22.770265279943224, 'W_1KI': 1.6110244968908483, 'W_D': 73.03275000000001, 'J_D': 1032.246930343509, 'W_D_1KI': 1.3087602817052848, 'J_D_1KI': 0.023453224409176653}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json
index 9882866..350b8f3 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8902, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.860300064086914, "TIME_S_1KI": 1.2199842803961933, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1340.0520877552033, "W": 84.52, "J_1KI": 150.53382248429602, "W_1KI": 9.49449561896203, "W_D": 68.1745, "J_D": 1080.896605024457, "W_D_1KI": 7.658335205571781, "J_D_1KI": 0.8602937773053}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8698, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.610017538070679, "TIME_S_1KI": 1.2198226647586432, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1341.4551094627382, "W": 87.67, "J_1KI": 154.22569665011937, "W_1KI": 10.079328581283054, "W_D": 70.861, "J_D": 1084.257448518753, "W_D_1KI": 8.146815359852841, "J_D_1KI": 0.9366308760465442}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output
index 266a604..a8e5825 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.13720321655273438}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.13405466079711914}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     497,     987,  ..., 4998984,
-                            4999490, 5000000]),
-       col_indices=tensor([  31,   32,   48,  ..., 9945, 9978, 9990]),
-       values=tensor([0.2379, 0.1839, 0.8156,  ..., 0.3545, 0.1897, 0.0490]),
+tensor(crow_indices=tensor([      0,     480,     945,  ..., 4998944,
+                            4999481, 5000000]),
+       col_indices=tensor([   1,   12,   19,  ..., 9947, 9963, 9973]),
+       values=tensor([0.5890, 0.9205, 0.0614,  ..., 0.7220, 0.9709, 0.4172]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.7954, 0.2457, 0.2337,  ..., 0.1008, 0.2602, 0.7172])
+tensor([0.5818, 0.9691, 0.7591,  ..., 0.9152, 0.5043, 0.7665])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,39 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 0.13720321655273438 seconds
+Time: 0.13405466079711914 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7652', '-ss', '10000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.025035858154297}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7832', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.454247951507568}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     488,     990,  ..., 4999027,
+tensor(crow_indices=tensor([      0,     485,    1011,  ..., 4998959,
+                            4999487, 5000000]),
+       col_indices=tensor([   3,   54,   65,  ..., 9920, 9953, 9995]),
+       values=tensor([0.3879, 0.5951, 0.7663,  ..., 0.8899, 0.2864, 0.6094]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7416, 0.0102, 0.0449,  ..., 0.4285, 0.7394, 0.6178])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 9.454247951507568 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8698', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.610017538070679}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     506,     993,  ..., 4999012,
                             4999510, 5000000]),
-       col_indices=tensor([  13,   24,   30,  ..., 9939, 9983, 9997]),
-       values=tensor([0.8521, 0.2131, 0.0790,  ..., 0.0763, 0.7991, 0.5452]),
+       col_indices=tensor([  26,   45,   57,  ..., 9916, 9958, 9969]),
+       values=tensor([0.1934, 0.7889, 0.4552,  ..., 0.2644, 0.1399, 0.0800]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.7324, 0.5581, 0.6877,  ..., 0.3893, 0.7172, 0.4223])
+tensor([0.4812, 0.2509, 0.7085,  ..., 0.2318, 0.2285, 0.0711])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +56,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 9.025035858154297 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8902', '-ss', '10000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.860300064086914}
+Time: 10.610017538070679 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     512,     999,  ..., 4999039,
-                            4999553, 5000000]),
-       col_indices=tensor([  40,   52,   78,  ..., 9943, 9982, 9986]),
-       values=tensor([0.6271, 0.3251, 0.6536,  ..., 0.8006, 0.2414, 0.7322]),
+tensor(crow_indices=tensor([      0,     506,     993,  ..., 4999012,
+                            4999510, 5000000]),
+       col_indices=tensor([  26,   45,   57,  ..., 9916, 9958, 9969]),
+       values=tensor([0.1934, 0.7889, 0.4552,  ..., 0.2644, 0.1399, 0.0800]),
        size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.7511, 0.5744, 0.2092,  ..., 0.0810, 0.1870, 0.5605])
+tensor([0.4812, 0.2509, 0.7085,  ..., 0.2318, 0.2285, 0.0711])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,30 +73,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000000
 Density: 0.05
-Time: 10.860300064086914 seconds
+Time: 10.610017538070679 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     512,     999,  ..., 4999039,
-                            4999553, 5000000]),
-       col_indices=tensor([  40,   52,   78,  ..., 9943, 9982, 9986]),
-       values=tensor([0.6271, 0.3251, 0.6536,  ..., 0.8006, 0.2414, 0.7322]),
-       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.7511, 0.5744, 0.2092,  ..., 0.0810, 0.1870, 0.5605])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 5000000
-Density: 0.05
-Time: 10.860300064086914 seconds
-
-[18.66, 18.42, 18.15, 17.87, 18.05, 18.01, 18.31, 17.87, 17.97, 18.0]
-[84.52]
-15.854851961135864
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8902, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.860300064086914, 'TIME_S_1KI': 1.2199842803961933, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1340.0520877552033, 'W': 84.52}
-[18.66, 18.42, 18.15, 17.87, 18.05, 18.01, 18.31, 17.87, 17.97, 18.0, 18.28, 17.95, 18.41, 17.97, 19.13, 18.12, 18.3, 17.86, 18.09, 17.92]
-326.91
-16.3455
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8902, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.860300064086914, 'TIME_S_1KI': 1.2199842803961933, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1340.0520877552033, 'W': 84.52, 'J_1KI': 150.53382248429602, 'W_1KI': 9.49449561896203, 'W_D': 68.1745, 'J_D': 1080.896605024457, 'W_D_1KI': 7.658335205571781, 'J_D_1KI': 0.8602937773053}
+[19.53, 18.93, 18.62, 18.4, 18.88, 18.89, 18.55, 18.63, 18.57, 18.76]
+[87.67]
+15.301187515258789
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8698, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.610017538070679, 'TIME_S_1KI': 1.2198226647586432, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1341.4551094627382, 'W': 87.67}
+[19.53, 18.93, 18.62, 18.4, 18.88, 18.89, 18.55, 18.63, 18.57, 18.76, 18.75, 18.47, 18.63, 18.5, 18.62, 18.29, 18.85, 18.58, 19.0, 18.5]
+336.17999999999995
+16.808999999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8698, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.610017538070679, 'TIME_S_1KI': 1.2198226647586432, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1341.4551094627382, 'W': 87.67, 'J_1KI': 154.22569665011937, 'W_1KI': 10.079328581283054, 'W_D': 70.861, 'J_D': 1084.257448518753, 'W_D_1KI': 8.146815359852841, 'J_D_1KI': 0.9366308760465442}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json
index 4142b7d..c89ffcb 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2952, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.514222383499146, "TIME_S_1KI": 3.5617284496948325, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1409.0968302583694, "W": 80.82, "J_1KI": 477.3363246132688, "W_1KI": 27.378048780487802, "W_D": 64.16999999999999, "J_D": 1118.8040534234044, "W_D_1KI": 21.737804878048774, "J_D_1KI": 7.363755039989422}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2720, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.711690187454224, "TIME_S_1KI": 3.9381213924464054, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1312.412057888508, "W": 83.95, "J_1KI": 482.5044330472455, "W_1KI": 30.863970588235293, "W_D": 66.99475000000001, "J_D": 1047.3462503302694, "W_D_1KI": 24.630422794117653, "J_D_1KI": 9.055302497837372}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output
index f33aa65..0a03f8e 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.3909003734588623}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.3859541416168213}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      946,     1929,  ...,  9998013,
-                             9999030, 10000000]),
-       col_indices=tensor([   1,   29,   66,  ..., 9951, 9961, 9963]),
-       values=tensor([0.1920, 0.8019, 0.0618,  ..., 0.8349, 0.9652, 0.3956]),
+tensor(crow_indices=tensor([       0,     1029,     2000,  ...,  9998003,
+                             9998985, 10000000]),
+       col_indices=tensor([   0,   28,   29,  ..., 9954, 9987, 9996]),
+       values=tensor([0.6829, 0.1416, 0.5262,  ..., 0.8908, 0.2734, 0.8338]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.5191, 0.4835, 0.4753,  ..., 0.1633, 0.4541, 0.9422])
+tensor([0.3530, 0.8966, 0.4993,  ..., 0.7172, 0.3219, 0.6793])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 0.3909003734588623 seconds
+Time: 0.3859541416168213 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2686', '-ss', '10000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.552263259887695}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2720', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.711690187454224}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1009,     2066,  ...,  9998034,
-                             9999022, 10000000]),
-       col_indices=tensor([   1,    6,   11,  ..., 9982, 9996, 9999]),
-       values=tensor([0.5825, 0.5025, 0.2695,  ..., 0.8369, 0.3596, 0.5616]),
+tensor(crow_indices=tensor([       0,      981,     1980,  ...,  9997989,
+                             9998997, 10000000]),
+       col_indices=tensor([  12,   27,   39,  ..., 9985, 9991, 9993]),
+       values=tensor([0.3850, 0.3757, 0.0662,  ..., 0.5591, 0.0330, 0.0089]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.8229, 0.3714, 0.9138,  ..., 0.5827, 0.9903, 0.1706])
+tensor([0.5245, 0.1235, 0.9662,  ..., 0.6225, 0.7512, 0.9707])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 9.552263259887695 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2952', '-ss', '10000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.514222383499146}
+Time: 10.711690187454224 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1012,     2027,  ...,  9998023,
-                             9999029, 10000000]),
-       col_indices=tensor([  20,   21,   65,  ..., 9939, 9966, 9982]),
-       values=tensor([0.6149, 0.2165, 0.5741,  ..., 0.9222, 0.5603, 0.9724]),
+tensor(crow_indices=tensor([       0,      981,     1980,  ...,  9997989,
+                             9998997, 10000000]),
+       col_indices=tensor([  12,   27,   39,  ..., 9985, 9991, 9993]),
+       values=tensor([0.3850, 0.3757, 0.0662,  ..., 0.5591, 0.0330, 0.0089]),
        size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1720, 0.3458, 0.5186,  ..., 0.7493, 0.6588, 0.1643])
+tensor([0.5245, 0.1235, 0.9662,  ..., 0.6225, 0.7512, 0.9707])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,30 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 10000000
 Density: 0.1
-Time: 10.514222383499146 seconds
+Time: 10.711690187454224 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1012,     2027,  ...,  9998023,
-                             9999029, 10000000]),
-       col_indices=tensor([  20,   21,   65,  ..., 9939, 9966, 9982]),
-       values=tensor([0.6149, 0.2165, 0.5741,  ..., 0.9222, 0.5603, 0.9724]),
-       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.1720, 0.3458, 0.5186,  ..., 0.7493, 0.6588, 0.1643])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 10000000
-Density: 0.1
-Time: 10.514222383499146 seconds
-
-[18.22, 18.02, 18.22, 18.83, 18.28, 18.32, 18.36, 18.07, 18.03, 17.97]
-[80.82]
-17.435001611709595
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2952, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.514222383499146, 'TIME_S_1KI': 3.5617284496948325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1409.0968302583694, 'W': 80.82}
-[18.22, 18.02, 18.22, 18.83, 18.28, 18.32, 18.36, 18.07, 18.03, 17.97, 19.01, 19.01, 17.78, 18.5, 20.32, 20.23, 18.29, 18.21, 17.96, 17.94]
-333.0
-16.65
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2952, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.514222383499146, 'TIME_S_1KI': 3.5617284496948325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1409.0968302583694, 'W': 80.82, 'J_1KI': 477.3363246132688, 'W_1KI': 27.378048780487802, 'W_D': 64.16999999999999, 'J_D': 1118.8040534234044, 'W_D_1KI': 21.737804878048774, 'J_D_1KI': 7.363755039989422}
+[22.49, 19.33, 18.98, 18.95, 18.77, 18.44, 19.01, 18.66, 18.62, 18.34]
+[83.95]
+15.633258581161499
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2720, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.711690187454224, 'TIME_S_1KI': 3.9381213924464054, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1312.412057888508, 'W': 83.95}
+[22.49, 19.33, 18.98, 18.95, 18.77, 18.44, 19.01, 18.66, 18.62, 18.34, 18.86, 18.43, 18.64, 18.64, 18.49, 19.07, 18.7, 18.78, 18.49, 18.52]
+339.10499999999996
+16.95525
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2720, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.711690187454224, 'TIME_S_1KI': 3.9381213924464054, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1312.412057888508, 'W': 83.95, 'J_1KI': 482.5044330472455, 'W_1KI': 30.863970588235293, 'W_D': 66.99475000000001, 'J_D': 1047.3462503302694, 'W_D_1KI': 24.630422794117653, 'J_D_1KI': 9.055302497837372}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.json
index 047807c..9c915c5 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1497, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.375812530517578, "TIME_S_1KI": 6.931070494667721, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2016.6524360942842, "W": 63.56, "J_1KI": 1347.1292158278452, "W_1KI": 42.458249832999336, "W_D": 46.965500000000006, "J_D": 1490.1367210098506, "W_D_1KI": 31.373079492317977, "J_D_1KI": 20.95730093007213}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1480, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.938852071762085, "TIME_S_1KI": 7.391116264704111, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1528.7213900470733, "W": 80.94, "J_1KI": 1032.9198581399144, "W_1KI": 54.689189189189186, "W_D": 63.9845, "J_D": 1208.4812673766612, "W_D_1KI": 43.232770270270265, "J_D_1KI": 29.211331263696124}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.output
index e1c6993..4b03a7c 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.7011280059814453}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.7089850902557373}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     1997,     4011,  ..., 19995978,
-                            19998029, 20000000]),
-       col_indices=tensor([   0,    6,   10,  ..., 9977, 9981, 9997]),
-       values=tensor([0.1409, 0.7742, 0.3684,  ..., 0.6455, 0.2528, 0.1779]),
+tensor(crow_indices=tensor([       0,     1931,     3950,  ..., 19996023,
+                            19998022, 20000000]),
+       col_indices=tensor([  10,   17,   25,  ..., 9994, 9995, 9996]),
+       values=tensor([0.0457, 0.3054, 0.6832,  ..., 0.8985, 0.1646, 0.8277]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.4126, 0.0545, 0.9867,  ..., 0.1672, 0.9147, 0.2153])
+tensor([0.2768, 0.9830, 0.6147,  ..., 0.3597, 0.6699, 0.4242])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 0.7011280059814453 seconds
+Time: 0.7089850902557373 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1497', '-ss', '10000', '-sd', '0.2']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.375812530517578}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1480', '-ss', '10000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.938852071762085}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2004,     3927,  ..., 19995948,
-                            19997998, 20000000]),
-       col_indices=tensor([   0,    7,    8,  ..., 9981, 9983, 9993]),
-       values=tensor([0.6769, 0.3987, 0.0257,  ..., 0.1977, 0.2040, 0.8027]),
+tensor(crow_indices=tensor([       0,     2034,     4022,  ..., 19995925,
+                            19997982, 20000000]),
+       col_indices=tensor([   2,   15,   18,  ..., 9987, 9998, 9999]),
+       values=tensor([0.7872, 0.2298, 0.4521,  ..., 0.3351, 0.9033, 0.4825]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.3084, 0.6863, 0.1716,  ..., 0.2861, 0.5214, 0.4353])
+tensor([0.4230, 0.2852, 0.9332,  ..., 0.3899, 0.8304, 0.1579])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,16 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 10.375812530517578 seconds
+Time: 10.938852071762085 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2004,     3927,  ..., 19995948,
-                            19997998, 20000000]),
-       col_indices=tensor([   0,    7,    8,  ..., 9981, 9983, 9993]),
-       values=tensor([0.6769, 0.3987, 0.0257,  ..., 0.1977, 0.2040, 0.8027]),
+tensor(crow_indices=tensor([       0,     2034,     4022,  ..., 19995925,
+                            19997982, 20000000]),
+       col_indices=tensor([   2,   15,   18,  ..., 9987, 9998, 9999]),
+       values=tensor([0.7872, 0.2298, 0.4521,  ..., 0.3351, 0.9033, 0.4825]),
        size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
-tensor([0.3084, 0.6863, 0.1716,  ..., 0.2861, 0.5214, 0.4353])
+tensor([0.4230, 0.2852, 0.9332,  ..., 0.3899, 0.8304, 0.1579])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,13 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 20000000
 Density: 0.2
-Time: 10.375812530517578 seconds
+Time: 10.938852071762085 seconds
 
-[18.42, 17.88, 18.24, 17.8, 17.91, 17.84, 18.25, 18.2, 17.93, 17.84]
-[63.56]
-31.728326559066772
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1497, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.375812530517578, 'TIME_S_1KI': 6.931070494667721, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2016.6524360942842, 'W': 63.56}
-[18.42, 17.88, 18.24, 17.8, 17.91, 17.84, 18.25, 18.2, 17.93, 17.84, 18.76, 17.93, 18.32, 21.74, 19.38, 18.29, 18.56, 18.06, 18.67, 18.76]
-331.89
-16.5945
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1497, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.375812530517578, 'TIME_S_1KI': 6.931070494667721, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2016.6524360942842, 'W': 63.56, 'J_1KI': 1347.1292158278452, 'W_1KI': 42.458249832999336, 'W_D': 46.965500000000006, 'J_D': 1490.1367210098506, 'W_D_1KI': 31.373079492317977, 'J_D_1KI': 20.95730093007213}
+[18.93, 18.5, 18.56, 18.73, 18.62, 21.16, 20.02, 18.59, 19.0, 18.39]
+[80.94]
+18.887094020843506
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1480, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.938852071762085, 'TIME_S_1KI': 7.391116264704111, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1528.7213900470733, 'W': 80.94}
+[18.93, 18.5, 18.56, 18.73, 18.62, 21.16, 20.02, 18.59, 19.0, 18.39, 19.32, 18.67, 18.63, 18.35, 18.48, 18.43, 18.55, 18.62, 18.66, 18.44]
+339.11
+16.9555
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1480, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.938852071762085, 'TIME_S_1KI': 7.391116264704111, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1528.7213900470733, 'W': 80.94, 'J_1KI': 1032.9198581399144, 'W_1KI': 54.689189189189186, 'W_D': 63.9845, 'J_D': 1208.4812673766612, 'W_D_1KI': 43.232770270270265, 'J_D_1KI': 29.211331263696124}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.json
index b205393..3429945 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 949, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.941918134689331, "TIME_S_1KI": 11.529945347407093, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3564.162018098831, "W": 52.14, "J_1KI": 3755.7028641715815, "W_1KI": 54.94204425711275, "W_D": 35.83725, "J_D": 2449.7461696032283, "W_D_1KI": 37.763171759747095, "J_D_1KI": 39.79259405663551}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1005, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.332000494003296, "TIME_S_1KI": 10.28059750647094, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1650.0738431143761, "W": 78.11, "J_1KI": 1641.8645205118169, "W_1KI": 77.72139303482587, "W_D": 39.656000000000006, "J_D": 837.733047273636, "W_D_1KI": 39.4587064676617, "J_D_1KI": 39.262394495185774}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.output
index d986f3d..8a579df 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.9010562896728516}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.0439045429229736}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2954,     5989,  ..., 29993992,
-                            29996937, 30000000]),
-       col_indices=tensor([   2,    3,    4,  ..., 9991, 9993, 9999]),
-       values=tensor([0.8641, 0.5764, 0.3491,  ..., 0.5822, 0.6256, 0.4859]),
+tensor(crow_indices=tensor([       0,     3022,     5961,  ..., 29994092,
+                            29997073, 30000000]),
+       col_indices=tensor([  11,   13,   15,  ..., 9992, 9993, 9996]),
+       values=tensor([0.4005, 0.5190, 0.3009,  ..., 0.2678, 0.6328, 0.6513]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.5324, 0.9227, 0.4901,  ..., 0.4747, 0.1770, 0.2536])
+tensor([0.1783, 0.2981, 0.9794,  ..., 0.1699, 0.0540, 0.0711])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -16,19 +16,19 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 1.9010562896728516 seconds
+Time: 1.0439045429229736 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '552', '-ss', '10000', '-sd', '0.3']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 6.5163209438323975}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1005', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.332000494003296}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     3039,     6048,  ..., 29994088,
-                            29997031, 30000000]),
-       col_indices=tensor([   5,   17,   19,  ..., 9983, 9988, 9994]),
-       values=tensor([0.5897, 0.1355, 0.4586,  ..., 0.2138, 0.9666, 0.8141]),
+tensor(crow_indices=tensor([       0,     2967,     5964,  ..., 29993953,
+                            29997003, 30000000]),
+       col_indices=tensor([   0,    6,   12,  ..., 9987, 9996, 9999]),
+       values=tensor([0.1521, 0.9589, 0.9546,  ..., 0.2072, 0.8415, 0.4716]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.3973, 0.5491, 0.6398,  ..., 0.0595, 0.1069, 0.4910])
+tensor([0.1527, 0.9738, 0.6748,  ..., 0.1588, 0.8399, 0.1982])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -36,19 +36,16 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 6.5163209438323975 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '889', '-ss', '10000', '-sd', '0.3']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.831693172454834}
+Time: 10.332000494003296 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     3022,     6006,  ..., 29994154,
-                            29996992, 30000000]),
-       col_indices=tensor([   2,    5,   11,  ..., 9993, 9994, 9997]),
-       values=tensor([0.4549, 0.7646, 0.3501,  ..., 0.7301, 0.5346, 0.2783]),
+tensor(crow_indices=tensor([       0,     2967,     5964,  ..., 29993953,
+                            29997003, 30000000]),
+       col_indices=tensor([   0,    6,   12,  ..., 9987, 9996, 9999]),
+       values=tensor([0.1521, 0.9589, 0.9546,  ..., 0.2072, 0.8415, 0.4716]),
        size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.0238, 0.3406, 0.5500,  ..., 0.0227, 0.0108, 0.7785])
+tensor([0.1527, 0.9738, 0.6748,  ..., 0.1588, 0.8399, 0.1982])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -56,50 +53,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 30000000
 Density: 0.3
-Time: 9.831693172454834 seconds
+Time: 10.332000494003296 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '949', '-ss', '10000', '-sd', '0.3']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.941918134689331}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     3007,     5955,  ..., 29993893,
-                            29996918, 30000000]),
-       col_indices=tensor([   0,    1,    2,  ..., 9996, 9997, 9998]),
-       values=tensor([0.2348, 0.5341, 0.6896,  ..., 0.7208, 0.8300, 0.7790]),
-       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.7258, 0.9160, 0.6029,  ..., 0.0530, 0.7513, 0.2296])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 30000000
-Density: 0.3
-Time: 10.941918134689331 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     3007,     5955,  ..., 29993893,
-                            29996918, 30000000]),
-       col_indices=tensor([   0,    1,    2,  ..., 9996, 9997, 9998]),
-       values=tensor([0.2348, 0.5341, 0.6896,  ..., 0.7208, 0.8300, 0.7790]),
-       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
-tensor([0.7258, 0.9160, 0.6029,  ..., 0.0530, 0.7513, 0.2296])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([10000, 10000])
-Rows: 10000
-Size: 100000000
-NNZ: 30000000
-Density: 0.3
-Time: 10.941918134689331 seconds
-
-[18.62, 18.03, 18.0, 17.99, 18.26, 17.85, 18.27, 17.94, 18.3, 18.09]
-[52.14]
-68.35753774642944
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 949, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.941918134689331, 'TIME_S_1KI': 11.529945347407093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3564.162018098831, 'W': 52.14}
-[18.62, 18.03, 18.0, 17.99, 18.26, 17.85, 18.27, 17.94, 18.3, 18.09, 18.58, 17.83, 17.98, 17.87, 18.18, 18.09, 18.02, 17.83, 18.92, 18.1]
-326.055
-16.30275
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 949, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.941918134689331, 'TIME_S_1KI': 11.529945347407093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3564.162018098831, 'W': 52.14, 'J_1KI': 3755.7028641715815, 'W_1KI': 54.94204425711275, 'W_D': 35.83725, 'J_D': 2449.7461696032283, 'W_D_1KI': 37.763171759747095, 'J_D_1KI': 39.79259405663551}
+[18.66, 20.38, 43.8, 42.6, 42.06, 45.08, 46.1, 44.23, 43.43, 43.61]
+[78.11]
+21.125001192092896
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1005, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.332000494003296, 'TIME_S_1KI': 10.28059750647094, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1650.0738431143761, 'W': 78.11}
+[18.66, 20.38, 43.8, 42.6, 42.06, 45.08, 46.1, 44.23, 43.43, 43.61, 47.07, 44.12, 45.97, 47.08, 46.91, 45.01, 44.24, 46.19, 44.65, 45.12]
+769.0799999999999
+38.45399999999999
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1005, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.332000494003296, 'TIME_S_1KI': 10.28059750647094, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1650.0738431143761, 'W': 78.11, 'J_1KI': 1641.8645205118169, 'W_1KI': 77.72139303482587, 'W_D': 39.656000000000006, 'J_D': 837.733047273636, 'W_D_1KI': 39.4587064676617, 'J_D_1KI': 39.262394495185774}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.4.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.4.json
new file mode 100644
index 0000000..19adb0f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 780, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.529170036315918, "TIME_S_1KI": 13.498935943994766, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1779.8577748131752, "W": 75.39, "J_1KI": 2281.8689420681735, "W_1KI": 96.65384615384616, "W_D": 58.513, "J_D": 1381.4142190959453, "W_D_1KI": 75.01666666666667, "J_D_1KI": 96.17521367521367}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.4.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.4.output
new file mode 100644
index 0000000..bf4ac07
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.4.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 1.3460514545440674}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3928,     7878,  ..., 39991924,
+                            39996011, 40000000]),
+       col_indices=tensor([   3,    4,    5,  ..., 9989, 9994, 9999]),
+       values=tensor([0.6477, 0.4724, 0.8443,  ..., 0.0902, 0.8701, 0.4257]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.5531, 0.3437, 0.9958,  ..., 0.3243, 0.6186, 0.7377])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 1.3460514545440674 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '780', '-ss', '10000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 40000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.529170036315918}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3921,     7937,  ..., 39992035,
+                            39996055, 40000000]),
+       col_indices=tensor([   1,    2,    3,  ..., 9989, 9990, 9998]),
+       values=tensor([0.7086, 0.2169, 0.2366,  ..., 0.5166, 0.6786, 0.0291]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.2287, 0.7303, 0.8328,  ..., 0.0492, 0.5795, 0.5066])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 10.529170036315918 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3921,     7937,  ..., 39992035,
+                            39996055, 40000000]),
+       col_indices=tensor([   1,    2,    3,  ..., 9989, 9990, 9998]),
+       values=tensor([0.7086, 0.2169, 0.2366,  ..., 0.5166, 0.6786, 0.0291]),
+       size=(10000, 10000), nnz=40000000, layout=torch.sparse_csr)
+tensor([0.2287, 0.7303, 0.8328,  ..., 0.0492, 0.5795, 0.5066])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 40000000
+Density: 0.4
+Time: 10.529170036315918 seconds
+
+[19.09, 18.66, 18.59, 18.91, 18.66, 18.68, 19.36, 18.92, 18.61, 18.43]
+[75.39]
+23.60867190361023
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 780, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.529170036315918, 'TIME_S_1KI': 13.498935943994766, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1779.8577748131752, 'W': 75.39}
+[19.09, 18.66, 18.59, 18.91, 18.66, 18.68, 19.36, 18.92, 18.61, 18.43, 19.13, 18.43, 18.7, 18.63, 18.66, 18.43, 18.54, 18.93, 19.04, 18.93]
+337.5400000000001
+16.877000000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 780, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 40000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.529170036315918, 'TIME_S_1KI': 13.498935943994766, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1779.8577748131752, 'W': 75.39, 'J_1KI': 2281.8689420681735, 'W_1KI': 96.65384615384616, 'W_D': 58.513, 'J_D': 1381.4142190959453, 'W_D_1KI': 75.01666666666667, 'J_D_1KI': 96.17521367521367}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.5.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.5.json
new file mode 100644
index 0000000..6163120
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 637, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.681277513504028, "TIME_S_1KI": 16.76809656751025, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1913.3989510321617, "W": 73.81, "J_1KI": 3003.7660141792176, "W_1KI": 115.87127158555731, "W_D": 56.800250000000005, "J_D": 1472.4500578290822, "W_D_1KI": 89.16836734693878, "J_D_1KI": 139.98173837823984}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.5.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.5.output
new file mode 100644
index 0000000..dce3a0d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_0.5.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 1.6458442211151123}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     4989,    10016,  ..., 49989995,
+                            49995029, 50000000]),
+       col_indices=tensor([   2,    3,    4,  ..., 9996, 9998, 9999]),
+       values=tensor([0.7496, 0.9774, 0.8983,  ..., 0.1532, 0.4365, 0.5482]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.5310, 0.4242, 0.2635,  ..., 0.7577, 0.7181, 0.5961])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 1.6458442211151123 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '637', '-ss', '10000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 50000000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.681277513504028}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     5020,     9944,  ..., 49990121,
+                            49995010, 50000000]),
+       col_indices=tensor([   0,    3,    4,  ..., 9992, 9994, 9999]),
+       values=tensor([0.6012, 0.4574, 0.7579,  ..., 0.9185, 0.5177, 0.4374]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.0792, 0.5429, 0.9377,  ..., 0.0686, 0.6722, 0.7827])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 10.681277513504028 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     5020,     9944,  ..., 49990121,
+                            49995010, 50000000]),
+       col_indices=tensor([   0,    3,    4,  ..., 9992, 9994, 9999]),
+       values=tensor([0.6012, 0.4574, 0.7579,  ..., 0.9185, 0.5177, 0.4374]),
+       size=(10000, 10000), nnz=50000000, layout=torch.sparse_csr)
+tensor([0.0792, 0.5429, 0.9377,  ..., 0.0686, 0.6722, 0.7827])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 50000000
+Density: 0.5
+Time: 10.681277513504028 seconds
+
+[19.19, 18.64, 18.62, 18.5, 18.66, 18.4, 18.46, 22.93, 19.17, 18.75]
+[73.81]
+25.92330241203308
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 637, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.681277513504028, 'TIME_S_1KI': 16.76809656751025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1913.3989510321617, 'W': 73.81}
+[19.19, 18.64, 18.62, 18.5, 18.66, 18.4, 18.46, 22.93, 19.17, 18.75, 19.6, 18.26, 18.95, 18.49, 18.71, 18.4, 19.05, 18.45, 18.45, 18.57]
+340.195
+17.00975
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 637, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 50000000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.681277513504028, 'TIME_S_1KI': 16.76809656751025, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1913.3989510321617, 'W': 73.81, 'J_1KI': 3003.7660141792176, 'W_1KI': 115.87127158555731, 'W_D': 56.800250000000005, 'J_D': 1472.4500578290822, 'W_D_1KI': 89.16836734693878, 'J_D_1KI': 139.98173837823984}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json
index 00c8b98..c9e0d89 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 284305, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.350545883178711, "TIME_S_1KI": 0.03640648558125503, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1004.1447986841201, "W": 73.05, "J_1KI": 3.5319280304043903, "W_1KI": 0.2569423682313009, "W_D": 56.574, "J_D": 777.6658157529831, "W_D_1KI": 0.19899052074356766, "J_D_1KI": 0.0006999191739278861}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 283407, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.425815343856812, "TIME_S_1KI": 0.03678743059930351, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1115.7269095683098, "W": 81.61, "J_1KI": 3.9368361034424333, "W_1KI": 0.2879604244073012, "W_D": 64.691, "J_D": 884.4196729185581, "W_D_1KI": 0.22826182839520548, "J_D_1KI": 0.0008054205732222757}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output
index cbcf9b7..a197da3 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output
@@ -1,266 +1,373 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.019997835159301758}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.019604206085205078}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ...,  999, 1000, 1000]),
-       col_indices=tensor([7109, 2385, 2417, 5435, 3828, 4759, 3881, 3125, 6359,
-                           8100, 8295, 5616, 9929, 4079, 7787,  359, 8328, 6707,
-                           7136,  309, 5171, 1453, 2735, 9306, 3785, 4541, 8081,
-                           2235,   61, 4688, 6276, 2644, 5471,  837, 3908, 9598,
-                           9530, 9506, 8707,   10, 4532,  534, 4561, 3707, 1054,
-                           2351, 8898,  694, 8085, 1481, 2176, 6847, 2046, 5123,
-                           1350, 2400, 2135, 4037, 9549, 4749,  612, 1214, 9582,
-                           9826, 1033, 8057, 3787, 6733, 5484, 5013,  513, 3997,
-                           9224,  768, 6259, 8198, 4081, 8587, 7654, 6997, 3629,
-                           5266, 8440, 8098, 4353, 7259, 6119, 7369, 9095, 5623,
-                           7383, 8433, 7910, 8608, 4139, 6690, 2987, 5688, 7896,
-                           7911, 6242, 6429,  722, 2362, 2947, 7072, 2748, 1401,
-                           7063, 9804, 2718, 4440, 4754, 9463, 9666, 7272, 8681,
-                           5892, 7815, 4523,  800, 9689, 6398,  686, 9595, 7223,
-                            252, 5697,  861, 6369, 1019, 2052, 8774, 3827, 3582,
-                           5597, 1734, 6986, 7447, 6272, 3623, 3565, 4417,  277,
-                           3843, 3333, 5620, 6442, 4003, 7391, 9554, 8526, 2101,
-                           9854, 8955, 4012, 5691, 3570, 9292, 3509, 3148, 5405,
-                           2599, 5760, 6655, 1397, 5002,  378, 9560, 8896, 7138,
-                           8297, 8567, 8541, 8058, 9553, 2217,  475,  973, 6966,
-                           8382, 9130, 3653, 6730, 5404, 1171, 6230, 4421, 8787,
-                           1013, 7148, 6496, 6029,   92, 1710, 3227, 4417, 1415,
-                           6028, 7960, 1123, 8741, 7693, 8010, 7518, 4472, 6141,
-                           7642, 3828, 9726, 5089, 1835, 1514, 2431, 8797, 5042,
-                            676, 3698, 5937, 3628, 6068, 1887, 5909, 6973, 5973,
-                            477, 5895, 1885, 7449, 3691, 1472,  679, 7389, 1532,
-                            192, 1918, 9427, 2430, 3048, 5494, 2607, 9231, 1796,
-                           5499, 9054, 3543, 8995, 5396, 1535,  682, 2719, 1631,
-                           5250, 5969,  556, 4778, 6509,  839, 3254, 5655, 3541,
-                           9791, 8791, 8465, 9659,  643, 5662, 6522, 1226, 6110,
-                           8738, 2729, 9937, 1594, 5216, 1576,  322,  138, 1174,
-                           8026,  182, 2386, 8814, 3055, 5706, 1632, 3053, 1250,
-                           5293, 4479, 2534, 9457, 8636, 4963, 8810, 6513, 3303,
-                           2068, 7365, 4824, 6966, 3195, 1321, 1454, 3116, 1891,
-                           4602,  103, 1064, 1824, 4653, 5493, 2856, 7214, 4796,
-                           8079,  259, 3544, 8673, 5026, 1149, 8318, 1462, 4443,
-                           1549, 5100, 9230, 1260, 6291,  510, 2869, 7765,  852,
-                           2488, 6706, 4128, 5493, 7619, 4287, 6605, 7113, 7503,
-                           9635, 3819, 3422, 9417, 6701, 4678, 7292, 2009, 2745,
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-                           3403,  953, 7193, 6806,  597, 9712, 7122, 7344, 9620,
-                           5762, 2940, 6890, 5706, 4675, 1376, 4625, 5803, 9790,
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-                           6971, 3549, 5585, 2412, 5888, 4457, 2799, 9818, 9618,
-                           2750, 2922,  131, 8551, 3306,  311, 2869, 5197, 1613,
-                           4841, 1155, 9841, 5868, 9266, 5281, 1337, 8753, 4768,
-                           9229, 9711, 9344, 3792, 7689, 9243, 3075, 8565, 7580,
-                           5999, 5247, 6273, 1505, 5520, 7307, 2076, 4532, 2693,
-                           6116, 7270, 3433, 8912, 5182, 9345, 8104, 6702,  450,
-                           9103, 8031, 6959, 6210, 5641, 9318, 4784,  626, 6063,
-                           6803, 5977, 7007, 8105, 2597, 6168, 9640, 5148, 1688,
-                           1007, 3856, 2440, 1610,   46, 3388, 8832, 9487,   77,
-                           9414,  713, 2259, 3015, 2404, 4677, 2375, 9203, 6270,
-                           6686, 5362, 8403,  149, 4599, 8799, 3044, 9352, 6320,
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-                           8241, 7310, 2664, 1140, 2638, 6331, 2836, 2339, 5416,
-                           6386, 5007, 3424, 4140, 8418, 2768, 7025, 8519, 4071,
-                           2786, 7259, 6812, 4479, 9408, 3790, 6230, 7656, 9695,
-                           7286,  975, 8931, 3543,  222, 7839, 9094, 3012, 9084,
-                           5657, 4120, 1509, 8871, 3510, 6988, 7621, 3914, 9254,
-                           7142, 2284, 5420,  893, 5854, 8200, 2689,  257, 9233,
-                            367, 2764, 2893, 1881, 3564, 7955, 9774, 2290, 4849,
-                           2776, 7436, 5689, 7041, 6283, 9905, 9032, 4403,  634,
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-                           3164, 8523, 4532, 1524, 5876, 2337, 1910, 6825, 1701,
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+                      4.4106e-02, 3.5476e-01, 6.5408e-02, 1.0350e-01,
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+                      4.7396e-01, 2.0444e-01, 1.8101e-01, 8.1941e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.8851, 0.6493, 0.1793,  ..., 0.2200, 0.7121, 0.0570])
+tensor([0.2083, 0.4700, 0.0751,  ..., 0.0945, 0.0306, 0.1951])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -268,271 +375,378 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 0.019997835159301758 seconds
+Time: 0.019604206085205078 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52505', '-ss', '10000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.9391217231750488}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53559', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.9843134880065918}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      5.9396e-01, 1.8746e-01, 4.6977e-01, 3.5594e-01,
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+                      1.9852e-01, 7.9404e-01, 2.0545e-02, 2.0999e-01]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.5397, 0.8345, 0.2583,  ..., 0.2923, 0.3741, 0.4815])
+tensor([0.2091, 0.1216, 0.0010,  ..., 0.3031, 0.8707, 0.7868])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -540,378 +754,271 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 1.9391217231750488 seconds
+Time: 1.9843134880065918 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '284305', '-ss', '10000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.350545883178711}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '283407', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.425815343856812}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      0.4296, 0.7266, 0.0168, 0.7668, 0.4531, 0.9406]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4825, 0.6787, 0.3927,  ..., 0.9418, 0.0965, 0.0788])
+tensor([0.4142, 0.5109, 0.3445,  ..., 0.2930, 0.0409, 0.1153])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -919,375 +1026,268 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.350545883178711 seconds
+Time: 10.425815343856812 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.6983, 0.4238, 0.5047, 0.1497, 0.9380, 0.6580, 0.5332,
+                      0.5670, 0.4759, 0.3379, 0.7770, 0.4989, 0.9628, 0.8008,
+                      0.0691, 0.1395, 0.8825, 0.8428, 0.2869, 0.5446, 0.7076,
+                      0.9507, 0.7554, 0.2652, 0.2555, 0.3540, 0.4539, 0.0023,
+                      0.9956, 0.2987, 0.1515, 0.8710, 0.6854, 0.8705, 0.2345,
+                      0.6768, 0.8055, 0.8135, 0.4333, 0.9453, 0.5409, 0.2969,
+                      0.4296, 0.7266, 0.0168, 0.7668, 0.4531, 0.9406]),
        size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
-tensor([0.4825, 0.6787, 0.3927,  ..., 0.9418, 0.0965, 0.0788])
+tensor([0.4142, 0.5109, 0.3445,  ..., 0.2930, 0.0409, 0.1153])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -1295,13 +1295,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 1000
 Density: 1e-05
-Time: 10.350545883178711 seconds
+Time: 10.425815343856812 seconds
 
-[18.41, 22.21, 18.1, 18.25, 17.88, 18.06, 18.18, 18.5, 18.02, 17.84]
-[73.05]
-13.74599313735962
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 284305, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.350545883178711, 'TIME_S_1KI': 0.03640648558125503, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1004.1447986841201, 'W': 73.05}
-[18.41, 22.21, 18.1, 18.25, 17.88, 18.06, 18.18, 18.5, 18.02, 17.84, 18.25, 17.97, 18.0, 17.86, 18.03, 17.9, 18.06, 17.99, 17.95, 18.62]
-329.52
-16.476
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 284305, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.350545883178711, 'TIME_S_1KI': 0.03640648558125503, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1004.1447986841201, 'W': 73.05, 'J_1KI': 3.5319280304043903, 'W_1KI': 0.2569423682313009, 'W_D': 56.574, 'J_D': 777.6658157529831, 'W_D_1KI': 0.19899052074356766, 'J_D_1KI': 0.0006999191739278861}
+[19.9, 18.99, 18.67, 18.75, 18.75, 18.68, 18.82, 18.43, 18.71, 18.96]
+[81.61]
+13.671448469161987
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 283407, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.425815343856812, 'TIME_S_1KI': 0.03678743059930351, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1115.7269095683098, 'W': 81.61}
+[19.9, 18.99, 18.67, 18.75, 18.75, 18.68, 18.82, 18.43, 18.71, 18.96, 19.65, 18.49, 18.9, 18.52, 19.02, 18.68, 19.05, 18.58, 18.82, 18.53]
+338.38
+16.919
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 283407, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.425815343856812, 'TIME_S_1KI': 0.03678743059930351, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1115.7269095683098, 'W': 81.61, 'J_1KI': 3.9368361034424333, 'W_1KI': 0.2879604244073012, 'W_D': 64.691, 'J_D': 884.4196729185581, 'W_D_1KI': 0.22826182839520548, 'J_D_1KI': 0.0008054205732222757}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.json
index 3fe7967..b04c1f7 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 264429, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.455932378768921, "TIME_S_1KI": 0.03954154944718212, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1008.905144047737, "W": 73.49, "J_1KI": 3.8154103522977323, "W_1KI": 0.2779195927829398, "W_D": 57.09475, "J_D": 783.8234722155332, "W_D_1KI": 0.21591712709271677, "J_D_1KI": 0.0008165410264861901}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 270916, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.355923652648926, "TIME_S_1KI": 0.038225588937711046, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1135.3567011880875, "W": 82.26, "J_1KI": 4.190807118029528, "W_1KI": 0.3036365515510343, "W_D": 65.19800000000001, "J_D": 899.8661099448206, "W_D_1KI": 0.24065762081235517, "J_D_1KI": 0.0008883108447354721}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.output
index 39a6146..b8a2d05 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.01993107795715332}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.021791696548461914}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    1,    1,  ..., 4997, 4998, 5000]),
-       col_indices=tensor([9328, 8573, 6400,  ..., 9443, 3853, 6322]),
-       values=tensor([0.9995, 0.8210, 0.4187,  ..., 0.1342, 0.0596, 0.9033]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4997, 5000, 5000]),
+       col_indices=tensor([4472, 6601, 2920,  ..., 1201, 3298, 9929]),
+       values=tensor([0.0277, 0.0355, 0.7076,  ..., 0.9866, 0.1330, 0.6698]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.6106, 0.0696, 0.9188,  ..., 0.7595, 0.3313, 0.0671])
+tensor([0.1564, 0.7861, 0.3014,  ..., 0.2808, 0.5767, 0.0131])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -15,18 +15,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 0.01993107795715332 seconds
+Time: 0.021791696548461914 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52681', '-ss', '10000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.0918641090393066}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '48183', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.8674421310424805}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 4998, 4998, 5000]),
-       col_indices=tensor([2599, 3812, 3885,  ...,  206, 4492, 5501]),
-       values=tensor([0.4008, 0.6783, 0.6051,  ..., 0.8606, 0.8114, 0.5557]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 4998, 4999, 5000]),
+       col_indices=tensor([3289, 9092, 5142,  ..., 3019, 5284, 7745]),
+       values=tensor([0.4883, 0.6873, 0.5973,  ..., 0.5290, 0.7749, 0.6785]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.4634, 0.0164, 0.5073,  ..., 0.3776, 0.2676, 0.0715])
+tensor([0.4543, 0.9740, 0.6974,  ..., 0.6831, 0.0081, 0.6533])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -34,18 +34,18 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 2.0918641090393066 seconds
+Time: 1.8674421310424805 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '264429', '-ss', '10000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.455932378768921}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '270916', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.355923652648926}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([3612, 1318, 1874,  ..., 9308, 8111, 3978]),
-       values=tensor([0.0263, 0.8701, 0.1587,  ..., 0.1521, 0.0294, 0.0367]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 4999, 4999, 5000]),
+       col_indices=tensor([7630, 8923, 2667,  ..., 8010, 8482,  224]),
+       values=tensor([0.8705, 0.1590, 0.0738,  ..., 0.5587, 0.1310, 0.4625]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.4791, 0.9785, 0.1725,  ..., 0.5898, 0.0968, 0.4149])
+tensor([0.8091, 0.7111, 0.4284,  ..., 0.5849, 0.8536, 0.5126])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -53,15 +53,15 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.455932378768921 seconds
+Time: 10.355923652648926 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
-       col_indices=tensor([3612, 1318, 1874,  ..., 9308, 8111, 3978]),
-       values=tensor([0.0263, 0.8701, 0.1587,  ..., 0.1521, 0.0294, 0.0367]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 4999, 4999, 5000]),
+       col_indices=tensor([7630, 8923, 2667,  ..., 8010, 8482,  224]),
+       values=tensor([0.8705, 0.1590, 0.0738,  ..., 0.5587, 0.1310, 0.4625]),
        size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
-tensor([0.4791, 0.9785, 0.1725,  ..., 0.5898, 0.0968, 0.4149])
+tensor([0.8091, 0.7111, 0.4284,  ..., 0.5849, 0.8536, 0.5126])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([10000, 10000])
@@ -69,13 +69,13 @@ Rows: 10000
 Size: 100000000
 NNZ: 5000
 Density: 5e-05
-Time: 10.455932378768921 seconds
+Time: 10.355923652648926 seconds
 
-[19.02, 17.97, 18.1, 18.25, 17.93, 18.7, 18.16, 18.15, 18.22, 17.98]
-[73.49]
-13.728468418121338
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 264429, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.455932378768921, 'TIME_S_1KI': 0.03954154944718212, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1008.905144047737, 'W': 73.49}
-[19.02, 17.97, 18.1, 18.25, 17.93, 18.7, 18.16, 18.15, 18.22, 17.98, 18.41, 18.7, 18.23, 17.8, 17.94, 18.94, 18.01, 18.04, 18.02, 18.08]
-327.905
-16.395249999999997
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 264429, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.455932378768921, 'TIME_S_1KI': 0.03954154944718212, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1008.905144047737, 'W': 73.49, 'J_1KI': 3.8154103522977323, 'W_1KI': 0.2779195927829398, 'W_D': 57.09475, 'J_D': 783.8234722155332, 'W_D_1KI': 0.21591712709271677, 'J_D_1KI': 0.0008165410264861901}
+[18.87, 18.62, 18.42, 18.47, 18.37, 18.72, 18.49, 18.34, 18.5, 18.86]
+[82.26]
+13.802050828933716
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 270916, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.355923652648926, 'TIME_S_1KI': 0.038225588937711046, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1135.3567011880875, 'W': 82.26}
+[18.87, 18.62, 18.42, 18.47, 18.37, 18.72, 18.49, 18.34, 18.5, 18.86, 19.06, 19.43, 18.48, 18.51, 18.63, 18.3, 20.63, 18.66, 22.78, 18.99]
+341.24
+17.062
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 270916, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.355923652648926, 'TIME_S_1KI': 0.038225588937711046, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1135.3567011880875, 'W': 82.26, 'J_1KI': 4.190807118029528, 'W_1KI': 0.3036365515510343, 'W_D': 65.19800000000001, 'J_D': 899.8661099448206, 'W_D_1KI': 0.24065762081235517, 'J_D_1KI': 0.0008883108447354721}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.json
index 35c6334..86ca136 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 693, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.433570146560669, "TIME_S_1KI": 15.055656777143822, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2886.4447816610336, "W": 54.5, "J_1KI": 4165.1439850808565, "W_1KI": 78.64357864357864, "W_D": 38.16675, "J_D": 2021.3984655130505, "W_D_1KI": 55.074675324675326, "J_D_1KI": 79.47283596634246}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 746, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.84559440612793, "TIME_S_1KI": 14.53833030312055, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1694.9268332147599, "W": 78.11, "J_1KI": 2272.019883665898, "W_1KI": 104.70509383378015, "W_D": 61.11425, "J_D": 1326.1321497477293, "W_D_1KI": 81.92258713136728, "J_D_1KI": 109.81580044419206}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.output
index f2efbf0..cb05528 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.8274271488189697}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.4070062637329102}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       57,       98,  ..., 24999897,
-                            24999949, 25000000]),
-       col_indices=tensor([  2496,   8518,  12544,  ..., 449306, 467869,
-                           486714]),
-       values=tensor([0.2667, 0.8213, 0.8309,  ..., 0.8074, 0.6926, 0.7796]),
+tensor(crow_indices=tensor([       0,       41,       84,  ..., 24999913,
+                            24999952, 25000000]),
+       col_indices=tensor([ 61234,  85858,  98849,  ..., 469430, 496166,
+                           499540]),
+       values=tensor([0.5443, 0.1922, 0.4452,  ..., 0.7461, 0.7373, 0.7127]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.0179, 0.7693, 0.5874,  ..., 0.4128, 0.7472, 0.6195])
+tensor([0.0687, 0.4582, 0.0033,  ..., 0.9956, 0.4509, 0.1129])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 1.8274271488189697 seconds
+Time: 1.4070062637329102 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '574', '-ss', '500000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.696394205093384}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '746', '-ss', '500000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.84559440612793}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       45,       88,  ..., 24999889,
-                            24999948, 25000000]),
-       col_indices=tensor([ 21942,  37292,  56785,  ..., 479111, 486535,
-                           489318]),
-       values=tensor([0.4753, 0.7614, 0.3285,  ..., 0.3162, 0.0061, 0.9591]),
+tensor(crow_indices=tensor([       0,       51,      116,  ..., 24999893,
+                            24999955, 25000000]),
+       col_indices=tensor([  6279,  16149,  17234,  ..., 473800, 479653,
+                           499129]),
+       values=tensor([0.3557, 0.5902, 0.8620,  ..., 0.9429, 0.0947, 0.4761]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.2582, 0.0329, 0.5597,  ..., 0.0495, 0.5298, 0.4237])
+tensor([0.0901, 0.6319, 0.4091,  ..., 0.4625, 0.3509, 0.9371])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,20 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 8.696394205093384 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '693', '-ss', '500000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.433570146560669}
+Time: 10.84559440612793 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       38,       84,  ..., 24999899,
-                            24999948, 25000000]),
-       col_indices=tensor([ 30660,  43953,  94811,  ..., 484319, 487924,
-                           499108]),
-       values=tensor([0.4696, 0.5982, 0.2681,  ..., 0.1240, 0.7008, 0.8579]),
+tensor(crow_indices=tensor([       0,       51,      116,  ..., 24999893,
+                            24999955, 25000000]),
+       col_indices=tensor([  6279,  16149,  17234,  ..., 473800, 479653,
+                           499129]),
+       values=tensor([0.3557, 0.5902, 0.8620,  ..., 0.9429, 0.0947, 0.4761]),
        size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.3176, 0.2592, 0.6200,  ..., 0.0886, 0.2852, 0.1534])
+tensor([0.0901, 0.6319, 0.4091,  ..., 0.4625, 0.3509, 0.9371])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -59,31 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 25000000
 Density: 0.0001
-Time: 10.433570146560669 seconds
+Time: 10.84559440612793 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       38,       84,  ..., 24999899,
-                            24999948, 25000000]),
-       col_indices=tensor([ 30660,  43953,  94811,  ..., 484319, 487924,
-                           499108]),
-       values=tensor([0.4696, 0.5982, 0.2681,  ..., 0.1240, 0.7008, 0.8579]),
-       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.3176, 0.2592, 0.6200,  ..., 0.0886, 0.2852, 0.1534])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([500000, 500000])
-Rows: 500000
-Size: 250000000000
-NNZ: 25000000
-Density: 0.0001
-Time: 10.433570146560669 seconds
-
-[18.16, 18.09, 17.95, 18.13, 17.83, 18.01, 18.2, 17.99, 18.23, 17.93]
-[54.5]
-52.962289571762085
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.433570146560669, 'TIME_S_1KI': 15.055656777143822, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2886.4447816610336, 'W': 54.5}
-[18.16, 18.09, 17.95, 18.13, 17.83, 18.01, 18.2, 17.99, 18.23, 17.93, 18.48, 17.91, 18.03, 18.08, 18.03, 18.09, 18.34, 19.26, 18.16, 18.1]
-326.66499999999996
-16.33325
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.433570146560669, 'TIME_S_1KI': 15.055656777143822, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2886.4447816610336, 'W': 54.5, 'J_1KI': 4165.1439850808565, 'W_1KI': 78.64357864357864, 'W_D': 38.16675, 'J_D': 2021.3984655130505, 'W_D_1KI': 55.074675324675326, 'J_D_1KI': 79.47283596634246}
+[19.36, 18.51, 18.89, 21.88, 19.02, 18.5, 18.86, 18.41, 18.63, 18.48]
+[78.11]
+21.69922971725464
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 746, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.84559440612793, 'TIME_S_1KI': 14.53833030312055, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1694.9268332147599, 'W': 78.11}
+[19.36, 18.51, 18.89, 21.88, 19.02, 18.5, 18.86, 18.41, 18.63, 18.48, 19.29, 18.38, 18.34, 18.5, 19.12, 18.34, 19.58, 18.39, 18.81, 18.38]
+339.915
+16.99575
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 746, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.84559440612793, 'TIME_S_1KI': 14.53833030312055, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1694.9268332147599, 'W': 78.11, 'J_1KI': 2272.019883665898, 'W_1KI': 104.70509383378015, 'W_D': 61.11425, 'J_D': 1326.1321497477293, 'W_D_1KI': 81.92258713136728, 'J_D_1KI': 109.81580044419206}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json
index c977413..f553808 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8054, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.420526504516602, "TIME_S_1KI": 1.293832444067122, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1257.315396785736, "W": 87.34, "J_1KI": 156.11067752492377, "W_1KI": 10.844300968462875, "W_D": 70.58375000000001, "J_D": 1016.0984158217908, "W_D_1KI": 8.763813012167867, "J_D_1KI": 1.0881317372942474}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 7784, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.579402923583984, "TIME_S_1KI": 1.3591216499979424, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1294.7073955726623, "W": 88.97, "J_1KI": 166.32931597798847, "W_1KI": 11.429856115107913, "W_D": 72.1655, "J_D": 1050.1652979116438, "W_D_1KI": 9.27100462487153, "J_D_1KI": 1.1910334821263526}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output
index 2324509..623dbdf 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.1425309181213379}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.14815759658813477}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       5,       8,  ..., 2499994,
-                            2499996, 2500000]),
-       col_indices=tensor([ 55676, 267462, 335220,  ...,  59414, 387658,
-                           467981]),
-       values=tensor([0.3669, 0.3244, 0.1572,  ..., 0.0615, 0.1330, 0.1317]),
+tensor(crow_indices=tensor([      0,       6,      16,  ..., 2499993,
+                            2499995, 2500000]),
+       col_indices=tensor([  3766, 237018, 253848,  ..., 331484, 410452,
+                           411334]),
+       values=tensor([0.6777, 0.9195, 0.2043,  ..., 0.7681, 0.5254, 0.8125]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.2232, 0.7011, 0.4607,  ..., 0.0263, 0.5751, 0.5574])
+tensor([0.0134, 0.5178, 0.5077,  ..., 0.9311, 0.1963, 0.6173])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 0.1425309181213379 seconds
+Time: 0.14815759658813477 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7366', '-ss', '500000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.601978540420532}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7087', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.558655261993408}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       5,      17,  ..., 2499992,
-                            2499998, 2500000]),
-       col_indices=tensor([ 78904, 197792, 264056,  ..., 387862, 102468,
-                           277870]),
-       values=tensor([0.6219, 0.1671, 0.9871,  ..., 0.4185, 0.9305, 0.2778]),
+tensor(crow_indices=tensor([      0,       3,       5,  ..., 2499987,
+                            2499995, 2500000]),
+       col_indices=tensor([448569, 459977, 493128,  ..., 361590, 442935,
+                           473939]),
+       values=tensor([0.6275, 0.9076, 0.6066,  ..., 0.7922, 0.8137, 0.6712]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0210, 0.3595, 0.6743,  ..., 0.0036, 0.8741, 0.0347])
+tensor([0.4543, 0.7339, 0.9628,  ..., 0.5995, 0.5397, 0.7491])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,20 +38,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 9.601978540420532 seconds
+Time: 9.558655261993408 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8054', '-ss', '500000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.420526504516602}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7784', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.579402923583984}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       5,       7,  ..., 2499986,
-                            2499993, 2500000]),
-       col_indices=tensor([136514, 185390, 204506,  ..., 365577, 371722,
-                           449843]),
-       values=tensor([0.2143, 0.0864, 0.9943,  ..., 0.6371, 0.6570, 0.8441]),
+tensor(crow_indices=tensor([      0,       3,       9,  ..., 2499990,
+                            2499994, 2500000]),
+       col_indices=tensor([  5991, 210893, 321972,  ..., 434208, 435414,
+                           497706]),
+       values=tensor([0.4625, 0.6458, 0.2981,  ..., 0.9507, 0.2530, 0.6760]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0733, 0.2994, 0.4999,  ..., 0.8006, 0.1699, 0.8850])
+tensor([0.6802, 0.7796, 0.9205,  ..., 0.5425, 0.2003, 0.4676])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -59,17 +59,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.420526504516602 seconds
+Time: 10.579402923583984 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,       5,       7,  ..., 2499986,
-                            2499993, 2500000]),
-       col_indices=tensor([136514, 185390, 204506,  ..., 365577, 371722,
-                           449843]),
-       values=tensor([0.2143, 0.0864, 0.9943,  ..., 0.6371, 0.6570, 0.8441]),
+tensor(crow_indices=tensor([      0,       3,       9,  ..., 2499990,
+                            2499994, 2500000]),
+       col_indices=tensor([  5991, 210893, 321972,  ..., 434208, 435414,
+                           497706]),
+       values=tensor([0.4625, 0.6458, 0.2981,  ..., 0.9507, 0.2530, 0.6760]),
        size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0733, 0.2994, 0.4999,  ..., 0.8006, 0.1699, 0.8850])
+tensor([0.6802, 0.7796, 0.9205,  ..., 0.5425, 0.2003, 0.4676])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -77,13 +77,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 2500000
 Density: 1e-05
-Time: 10.420526504516602 seconds
+Time: 10.579402923583984 seconds
 
-[18.47, 17.91, 18.18, 18.64, 18.09, 18.11, 18.17, 18.18, 22.06, 17.96]
-[87.34]
-14.395642280578613
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8054, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.420526504516602, 'TIME_S_1KI': 1.293832444067122, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1257.315396785736, 'W': 87.34}
-[18.47, 17.91, 18.18, 18.64, 18.09, 18.11, 18.17, 18.18, 22.06, 17.96, 18.46, 18.6, 18.06, 19.85, 20.28, 18.18, 18.02, 17.82, 18.61, 17.84]
-335.125
-16.75625
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8054, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.420526504516602, 'TIME_S_1KI': 1.293832444067122, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1257.315396785736, 'W': 87.34, 'J_1KI': 156.11067752492377, 'W_1KI': 10.844300968462875, 'W_D': 70.58375000000001, 'J_D': 1016.0984158217908, 'W_D_1KI': 8.763813012167867, 'J_D_1KI': 1.0881317372942474}
+[19.1, 18.44, 18.85, 18.36, 18.5, 18.61, 18.93, 18.44, 18.66, 18.57]
+[88.97]
+14.552179336547852
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 7784, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.579402923583984, 'TIME_S_1KI': 1.3591216499979424, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1294.7073955726623, 'W': 88.97}
+[19.1, 18.44, 18.85, 18.36, 18.5, 18.61, 18.93, 18.44, 18.66, 18.57, 18.8, 18.43, 18.68, 18.91, 18.54, 19.38, 18.58, 18.68, 18.67, 18.39]
+336.09000000000003
+16.8045
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 7784, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.579402923583984, 'TIME_S_1KI': 1.3591216499979424, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1294.7073955726623, 'W': 88.97, 'J_1KI': 166.32931597798847, 'W_1KI': 11.429856115107913, 'W_D': 72.1655, 'J_D': 1050.1652979116438, 'W_D_1KI': 9.27100462487153, 'J_D_1KI': 1.1910334821263526}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.json
index 72c367a..af927a9 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1363, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.215147256851196, "TIME_S_1KI": 7.494605470910636, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1461.7694299888612, "W": 77.68, "J_1KI": 1072.4647322001917, "W_1KI": 56.99192956713133, "W_D": 61.057, "J_D": 1148.960557245493, "W_D_1KI": 44.796038151137196, "J_D_1KI": 32.865765334656786}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1296, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.415658712387085, "TIME_S_1KI": 8.036773697829542, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1467.0796593379973, "W": 82.82, "J_1KI": 1132.0059099830225, "W_1KI": 63.90432098765432, "W_D": 66.01575, "J_D": 1169.4079210449456, "W_D_1KI": 50.9380787037037, "J_D_1KI": 39.30407307384545}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.output
index 097beff..1ac7539 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.output
@@ -1,15 +1,15 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.7700090408325195}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.8099560737609863}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       24,       55,  ..., 12499948,
-                            12499975, 12500000]),
-       col_indices=tensor([  4417,  27723,  55822,  ..., 442008, 448310,
-                           496598]),
-       values=tensor([0.6543, 0.3065, 0.8363,  ..., 0.0104, 0.9892, 0.0257]),
+tensor(crow_indices=tensor([       0,       20,       43,  ..., 12499954,
+                            12499972, 12500000]),
+       col_indices=tensor([ 15140, 122304, 132861,  ..., 413745, 434718,
+                           449928]),
+       values=tensor([0.7713, 0.2886, 0.8294,  ..., 0.6043, 0.3703, 0.8077]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.5546, 0.9568, 0.6697,  ..., 0.4050, 0.9738, 0.6596])
+tensor([0.4264, 0.3783, 0.7665,  ..., 0.2411, 0.5032, 0.4857])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -17,20 +17,20 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 0.7700090408325195 seconds
+Time: 0.8099560737609863 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1363', '-ss', '500000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.215147256851196}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1296', '-ss', '500000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.415658712387085}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       32,       58,  ..., 12499956,
+tensor(crow_indices=tensor([       0,       27,       50,  ..., 12499957,
                             12499981, 12500000]),
-       col_indices=tensor([  7736,  12237,  33305,  ..., 443222, 470958,
-                           475326]),
-       values=tensor([0.9583, 0.2636, 0.0225,  ..., 0.4084, 0.8296, 0.8114]),
+       col_indices=tensor([ 22700,  42704,  60062,  ..., 407983, 461451,
+                           481435]),
+       values=tensor([0.7364, 0.7571, 0.4841,  ..., 0.0707, 0.3766, 0.3990]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.4848, 0.9833, 0.6868,  ..., 0.4430, 0.1817, 0.0586])
+tensor([0.2903, 0.4833, 0.2839,  ..., 0.8559, 0.6564, 0.0108])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -38,17 +38,17 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 10.215147256851196 seconds
+Time: 10.415658712387085 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,       32,       58,  ..., 12499956,
+tensor(crow_indices=tensor([       0,       27,       50,  ..., 12499957,
                             12499981, 12500000]),
-       col_indices=tensor([  7736,  12237,  33305,  ..., 443222, 470958,
-                           475326]),
-       values=tensor([0.9583, 0.2636, 0.0225,  ..., 0.4084, 0.8296, 0.8114]),
+       col_indices=tensor([ 22700,  42704,  60062,  ..., 407983, 461451,
+                           481435]),
+       values=tensor([0.7364, 0.7571, 0.4841,  ..., 0.0707, 0.3766, 0.3990]),
        size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.4848, 0.9833, 0.6868,  ..., 0.4430, 0.1817, 0.0586])
+tensor([0.2903, 0.4833, 0.2839,  ..., 0.8559, 0.6564, 0.0108])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([500000, 500000])
@@ -56,13 +56,13 @@ Rows: 500000
 Size: 250000000000
 NNZ: 12500000
 Density: 5e-05
-Time: 10.215147256851196 seconds
+Time: 10.415658712387085 seconds
 
-[18.64, 18.68, 18.32, 18.11, 18.55, 17.92, 17.92, 19.0, 18.5, 17.99]
-[77.68]
-18.817835092544556
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1363, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.215147256851196, 'TIME_S_1KI': 7.494605470910636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1461.7694299888612, 'W': 77.68}
-[18.64, 18.68, 18.32, 18.11, 18.55, 17.92, 17.92, 19.0, 18.5, 17.99, 18.42, 18.14, 18.35, 18.41, 18.1, 18.07, 18.11, 21.75, 17.97, 18.07]
-332.46000000000004
-16.623
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1363, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.215147256851196, 'TIME_S_1KI': 7.494605470910636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1461.7694299888612, 'W': 77.68, 'J_1KI': 1072.4647322001917, 'W_1KI': 56.99192956713133, 'W_D': 61.057, 'J_D': 1148.960557245493, 'W_D_1KI': 44.796038151137196, 'J_D_1KI': 32.865765334656786}
+[19.7, 18.76, 18.79, 18.44, 19.07, 18.28, 18.71, 18.42, 18.71, 18.44]
+[82.82]
+17.71407461166382
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1296, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.415658712387085, 'TIME_S_1KI': 8.036773697829542, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1467.0796593379973, 'W': 82.82}
+[19.7, 18.76, 18.79, 18.44, 19.07, 18.28, 18.71, 18.42, 18.71, 18.44, 19.25, 18.74, 18.64, 18.32, 18.52, 18.38, 18.43, 18.41, 19.07, 19.4]
+336.08500000000004
+16.804250000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1296, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.415658712387085, 'TIME_S_1KI': 8.036773697829542, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1467.0796593379973, 'W': 82.82, 'J_1KI': 1132.0059099830225, 'W_1KI': 63.90432098765432, 'W_D': 66.01575, 'J_D': 1169.4079210449456, 'W_D_1KI': 50.9380787037037, 'J_D_1KI': 39.30407307384545}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json
index 393ceed..8c451f8 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 80365, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.785846710205078, "TIME_S_1KI": 0.1342107473428119, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1142.0150223541261, "W": 83.68, "J_1KI": 14.210353043664854, "W_1KI": 1.041249300068438, "W_D": 67.33675000000001, "J_D": 918.9720370041133, "W_D_1KI": 0.8378865177627077, "J_D_1KI": 0.01042601278868547}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 78692, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.352098941802979, "TIME_S_1KI": 0.13155211383371854, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1251.0309797000884, "W": 89.96, "J_1KI": 15.897816546791143, "W_1KI": 1.1431911757230722, "W_D": 73.1585, "J_D": 1017.380501649499, "W_D_1KI": 0.9296815432318406, "J_D_1KI": 0.011814181152237084}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output
index ace565c..4d0d66d 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.031140804290771484}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.03153657913208008}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,     10,  ..., 249988, 249996,
+tensor(crow_indices=tensor([     0,      7,     15,  ..., 249984, 249990,
                             250000]),
-       col_indices=tensor([ 3938, 11827, 18410,  ..., 25331, 39292, 43613]),
-       values=tensor([0.0696, 0.4480, 0.5528,  ..., 0.5051, 0.2445, 0.3952]),
+       col_indices=tensor([ 2492,  9440, 17114,  ..., 45352, 46897, 49157]),
+       values=tensor([0.2702, 0.2914, 0.3123,  ..., 0.0040, 0.1038, 0.9095]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.1610, 0.2872, 0.6159,  ..., 0.3941, 0.7048, 0.2210])
+tensor([0.7068, 0.5554, 0.2496,  ..., 0.6351, 0.9308, 0.5843])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 0.031140804290771484 seconds
+Time: 0.03153657913208008 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33717', '-ss', '50000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 4.405243873596191}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33294', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 4.442471742630005}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      8,     16,  ..., 249992, 249993,
+tensor(crow_indices=tensor([     0,      9,     14,  ..., 249990, 249992,
                             250000]),
-       col_indices=tensor([ 4798,  5191,  8748,  ..., 31389, 41207, 45142]),
-       values=tensor([0.4595, 0.5274, 0.3770,  ..., 0.1787, 0.1620, 0.3761]),
+       col_indices=tensor([11099, 16101, 18653,  ..., 16723, 26003, 26144]),
+       values=tensor([0.7576, 0.9790, 0.7742,  ..., 0.4957, 0.9421, 0.1630]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.7848, 0.4939, 0.9259,  ..., 0.5597, 0.9743, 0.2454])
+tensor([0.5654, 0.1751, 0.3696,  ..., 0.4101, 0.6979, 0.6769])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 4.405243873596191 seconds
+Time: 4.442471742630005 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '80365', '-ss', '50000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.785846710205078}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '78692', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.352098941802979}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,      6,  ..., 249987, 249993,
+tensor(crow_indices=tensor([     0,     13,     18,  ..., 249987, 249992,
                             250000]),
-       col_indices=tensor([ 3036, 18920, 36950,  ..., 33008, 35825, 42083]),
-       values=tensor([0.1216, 0.1218, 0.6996,  ..., 0.2614, 0.2596, 0.0475]),
+       col_indices=tensor([ 2685,  9199, 16962,  ..., 29672, 31794, 49403]),
+       values=tensor([0.9434, 0.9131, 0.3740,  ..., 0.2777, 0.1729, 0.4549]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0022, 0.5368, 0.2941,  ..., 0.2951, 0.8515, 0.2587])
+tensor([0.8203, 0.0251, 0.4654,  ..., 0.7560, 0.1695, 0.4972])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.785846710205078 seconds
+Time: 10.352098941802979 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,      6,  ..., 249987, 249993,
+tensor(crow_indices=tensor([     0,     13,     18,  ..., 249987, 249992,
                             250000]),
-       col_indices=tensor([ 3036, 18920, 36950,  ..., 33008, 35825, 42083]),
-       values=tensor([0.1216, 0.1218, 0.6996,  ..., 0.2614, 0.2596, 0.0475]),
+       col_indices=tensor([ 2685,  9199, 16962,  ..., 29672, 31794, 49403]),
+       values=tensor([0.9434, 0.9131, 0.3740,  ..., 0.2777, 0.1729, 0.4549]),
        size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.0022, 0.5368, 0.2941,  ..., 0.2951, 0.8515, 0.2587])
+tensor([0.8203, 0.0251, 0.4654,  ..., 0.7560, 0.1695, 0.4972])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 250000
 Density: 0.0001
-Time: 10.785846710205078 seconds
+Time: 10.352098941802979 seconds
 
-[18.57, 17.87, 18.3, 17.99, 18.1, 18.03, 18.33, 18.16, 18.09, 17.89]
-[83.68]
-13.647407054901123
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 80365, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.785846710205078, 'TIME_S_1KI': 0.1342107473428119, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1142.0150223541261, 'W': 83.68}
-[18.57, 17.87, 18.3, 17.99, 18.1, 18.03, 18.33, 18.16, 18.09, 17.89, 18.23, 17.91, 18.25, 19.1, 18.2, 17.88, 18.14, 18.11, 18.11, 17.9]
-326.865
-16.34325
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 80365, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.785846710205078, 'TIME_S_1KI': 0.1342107473428119, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1142.0150223541261, 'W': 83.68, 'J_1KI': 14.210353043664854, 'W_1KI': 1.041249300068438, 'W_D': 67.33675000000001, 'J_D': 918.9720370041133, 'W_D_1KI': 0.8378865177627077, 'J_D_1KI': 0.01042601278868547}
+[19.65, 18.44, 19.1, 18.35, 19.07, 18.63, 18.43, 18.35, 18.65, 18.3]
+[89.96]
+13.906524896621704
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 78692, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.352098941802979, 'TIME_S_1KI': 0.13155211383371854, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1251.0309797000884, 'W': 89.96}
+[19.65, 18.44, 19.1, 18.35, 19.07, 18.63, 18.43, 18.35, 18.65, 18.3, 19.16, 18.59, 18.84, 18.4, 18.48, 18.5, 19.04, 18.48, 18.87, 18.51]
+336.03
+16.801499999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 78692, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.352098941802979, 'TIME_S_1KI': 0.13155211383371854, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1251.0309797000884, 'W': 89.96, 'J_1KI': 15.897816546791143, 'W_1KI': 1.1431911757230722, 'W_D': 73.1585, 'J_D': 1017.380501649499, 'W_D_1KI': 0.9296815432318406, 'J_D_1KI': 0.011814181152237084}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json
index 3d88fb8..ca43d5e 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 17258, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.52219271659851, "TIME_S_1KI": 0.6096994273147822, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1290.1128985500336, "W": 87.73, "J_1KI": 74.75448479256191, "W_1KI": 5.083439564260054, "W_D": 71.3875, "J_D": 1049.7883796334268, "W_D_1KI": 4.136487426121219, "J_D_1KI": 0.23968521416857222}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 16563, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.481314182281494, "TIME_S_1KI": 0.6328149599880151, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1300.5569376850128, "W": 89.09, "J_1KI": 78.52182199390285, "W_1KI": 5.378856487351325, "W_D": 71.86, "J_D": 1049.0293135261536, "W_D_1KI": 4.338586004950795, "J_D_1KI": 0.26194445480594064}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output
index c067392..40e4261 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output
@@ -1,34 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.07522106170654297}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.0770113468170166}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      54,      95,  ..., 2499899,
-                            2499951, 2500000]),
-       col_indices=tensor([  245,   316,   650,  ..., 46425, 47933, 49262]),
-       values=tensor([0.3598, 0.8732, 0.2112,  ..., 0.2076, 0.2855, 0.8514]),
-       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6227, 0.9680, 0.6855,  ..., 0.8971, 0.9917, 0.2060])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 2500000
-Density: 0.001
-Time: 0.07522106170654297 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '13958', '-ss', '50000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.492224931716919}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      52,      97,  ..., 2499905,
+tensor(crow_indices=tensor([      0,      57,      97,  ..., 2499901,
                             2499950, 2500000]),
-       col_indices=tensor([ 3061,  5035,  6476,  ..., 48999, 49661, 49813]),
-       values=tensor([0.5243, 0.0379, 0.3507,  ..., 0.2954, 0.9764, 0.8519]),
+       col_indices=tensor([  124,   175,   906,  ..., 48953, 49099, 49775]),
+       values=tensor([0.5729, 0.6603, 0.4257,  ..., 0.7714, 0.6486, 0.1048]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.8088, 0.8530, 0.3688,  ..., 0.5327, 0.1148, 0.7333])
+tensor([0.1490, 0.9796, 0.6323,  ..., 0.8258, 0.0173, 0.9887])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 8.492224931716919 seconds
+Time: 0.0770113468170166 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '17258', '-ss', '50000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.52219271659851}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '13634', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.642810106277466}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      60,      98,  ..., 2499898,
-                            2499955, 2500000]),
-       col_indices=tensor([  336,   813,  2467,  ..., 44805, 45101, 46338]),
-       values=tensor([0.1989, 0.3364, 0.1097,  ..., 0.3897, 0.4637, 0.0665]),
+tensor(crow_indices=tensor([      0,      44,      96,  ..., 2499886,
+                            2499947, 2500000]),
+       col_indices=tensor([ 2781,  2792,  2952,  ..., 47835, 48355, 49738]),
+       values=tensor([0.6657, 0.8068, 0.2679,  ..., 0.1283, 0.7173, 0.1870]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6060, 0.7426, 0.0681,  ..., 0.1484, 0.6293, 0.7345])
+tensor([0.0925, 0.3234, 0.5984,  ..., 0.6757, 0.5158, 0.8086])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.52219271659851 seconds
+Time: 8.642810106277466 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '16563', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.481314182281494}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,      60,      98,  ..., 2499898,
-                            2499955, 2500000]),
-       col_indices=tensor([  336,   813,  2467,  ..., 44805, 45101, 46338]),
-       values=tensor([0.1989, 0.3364, 0.1097,  ..., 0.3897, 0.4637, 0.0665]),
+tensor(crow_indices=tensor([      0,      49,      98,  ..., 2499924,
+                            2499965, 2500000]),
+       col_indices=tensor([ 2415,  5474,  6582,  ..., 47823, 47849, 49372]),
+       values=tensor([0.1268, 0.5761, 0.4335,  ..., 0.2798, 0.1362, 0.5670]),
        size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.6060, 0.7426, 0.0681,  ..., 0.1484, 0.6293, 0.7345])
+tensor([0.4866, 0.7985, 0.0883,  ..., 0.5992, 0.0522, 0.8201])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +56,30 @@ Rows: 50000
 Size: 2500000000
 NNZ: 2500000
 Density: 0.001
-Time: 10.52219271659851 seconds
+Time: 10.481314182281494 seconds
 
-[18.24, 17.95, 18.03, 17.9, 18.31, 18.2, 18.96, 17.99, 17.74, 18.24]
-[87.73]
-14.705492973327637
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17258, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.52219271659851, 'TIME_S_1KI': 0.6096994273147822, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1290.1128985500336, 'W': 87.73}
-[18.24, 17.95, 18.03, 17.9, 18.31, 18.2, 18.96, 17.99, 17.74, 18.24, 18.79, 18.06, 18.38, 18.13, 18.12, 18.05, 18.08, 18.09, 18.27, 17.91]
-326.85
-16.3425
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17258, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.52219271659851, 'TIME_S_1KI': 0.6096994273147822, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1290.1128985500336, 'W': 87.73, 'J_1KI': 74.75448479256191, 'W_1KI': 5.083439564260054, 'W_D': 71.3875, 'J_D': 1049.7883796334268, 'W_D_1KI': 4.136487426121219, 'J_D_1KI': 0.23968521416857222}
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      49,      98,  ..., 2499924,
+                            2499965, 2500000]),
+       col_indices=tensor([ 2415,  5474,  6582,  ..., 47823, 47849, 49372]),
+       values=tensor([0.1268, 0.5761, 0.4335,  ..., 0.2798, 0.1362, 0.5670]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4866, 0.7985, 0.0883,  ..., 0.5992, 0.0522, 0.8201])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.481314182281494 seconds
+
+[18.81, 19.03, 18.91, 18.88, 21.0, 18.65, 19.63, 19.12, 18.72, 18.6]
+[89.09]
+14.598237037658691
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 16563, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.481314182281494, 'TIME_S_1KI': 0.6328149599880151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.5569376850128, 'W': 89.09}
+[18.81, 19.03, 18.91, 18.88, 21.0, 18.65, 19.63, 19.12, 18.72, 18.6, 19.47, 18.46, 18.88, 18.42, 18.93, 18.66, 18.63, 21.61, 19.39, 18.48]
+344.6
+17.23
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 16563, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.481314182281494, 'TIME_S_1KI': 0.6328149599880151, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1300.5569376850128, 'W': 89.09, 'J_1KI': 78.52182199390285, 'W_1KI': 5.378856487351325, 'W_D': 71.86, 'J_D': 1049.0293135261536, 'W_D_1KI': 4.338586004950795, 'J_D_1KI': 0.26194445480594064}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.json
index 00dd221..babdca8 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1116, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.347081184387207, "TIME_S_1KI": 9.27157812221076, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2825.247347984314, "W": 53.89, "J_1KI": 2531.5836451472346, "W_1KI": 48.288530465949826, "W_D": 37.37625, "J_D": 1959.4943624067305, "W_D_1KI": 33.491263440860216, "J_D_1KI": 30.010092688942844}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1167, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.299102067947388, "TIME_S_1KI": 8.82528026387951, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1704.976960287094, "W": 77.39, "J_1KI": 1460.9913969897977, "W_1KI": 66.31533847472151, "W_D": 60.3825, "J_D": 1330.2851958203316, "W_D_1KI": 51.741645244215945, "J_D_1KI": 44.33731383394682}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.output
index 1b20dcd..744df53 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.output
@@ -1,15 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 1.2946875095367432}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.8992819786071777}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      488,      979,  ..., 24999008,
-                            24999504, 25000000]),
-       col_indices=tensor([  111,   301,   401,  ..., 49766, 49891, 49958]),
-       values=tensor([2.3418e-01, 9.8131e-01, 8.1298e-03,  ...,
-                      6.0106e-01, 4.2789e-04, 8.6966e-01]),
+tensor(crow_indices=tensor([       0,      475,     1011,  ..., 24998974,
+                            24999527, 25000000]),
+       col_indices=tensor([  168,   172,   198,  ..., 49907, 49922, 49927]),
+       values=tensor([0.0665, 0.3170, 0.5936,  ..., 0.6846, 0.2811, 0.3676]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.1063, 0.9255, 0.6653,  ..., 0.3278, 0.7920, 0.4701])
+tensor([0.7181, 0.4244, 0.4435,  ..., 0.9840, 0.0888, 0.6399])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -17,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 1.2946875095367432 seconds
+Time: 0.8992819786071777 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '811', '-ss', '50000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.6258299350738525}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1167', '-ss', '50000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.299102067947388}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      521,     1028,  ..., 24999005,
-                            24999496, 25000000]),
-       col_indices=tensor([  169,   333,   382,  ..., 49620, 49646, 49746]),
-       values=tensor([0.1336, 0.2367, 0.6093,  ..., 0.7411, 0.2218, 0.9154]),
+tensor(crow_indices=tensor([       0,      466,      988,  ..., 24999020,
+                            24999475, 25000000]),
+       col_indices=tensor([   26,    43,   133,  ..., 49742, 49774, 49815]),
+       values=tensor([0.5412, 0.2221, 0.8751,  ..., 0.6785, 0.2543, 0.9299]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.7321, 0.9416, 0.5259,  ..., 0.9099, 0.7583, 0.2580])
+tensor([0.6108, 0.5492, 0.3892,  ..., 0.2620, 0.5593, 0.4517])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -37,19 +36,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 7.6258299350738525 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1116', '-ss', '50000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.347081184387207}
+Time: 10.299102067947388 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      467,      956,  ..., 24998994,
-                            24999500, 25000000]),
-       col_indices=tensor([   21,   163,   165,  ..., 49855, 49860, 49938]),
-       values=tensor([0.3050, 0.9077, 0.0930,  ..., 0.0680, 0.0415, 0.3010]),
+tensor(crow_indices=tensor([       0,      466,      988,  ..., 24999020,
+                            24999475, 25000000]),
+       col_indices=tensor([   26,    43,   133,  ..., 49742, 49774, 49815]),
+       values=tensor([0.5412, 0.2221, 0.8751,  ..., 0.6785, 0.2543, 0.9299]),
        size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.5141, 0.6637, 0.6626,  ..., 0.2332, 0.8993, 0.6899])
+tensor([0.6108, 0.5492, 0.3892,  ..., 0.2620, 0.5593, 0.4517])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -57,30 +53,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000000
 Density: 0.01
-Time: 10.347081184387207 seconds
+Time: 10.299102067947388 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,      467,      956,  ..., 24998994,
-                            24999500, 25000000]),
-       col_indices=tensor([   21,   163,   165,  ..., 49855, 49860, 49938]),
-       values=tensor([0.3050, 0.9077, 0.0930,  ..., 0.0680, 0.0415, 0.3010]),
-       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
-tensor([0.5141, 0.6637, 0.6626,  ..., 0.2332, 0.8993, 0.6899])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 25000000
-Density: 0.01
-Time: 10.347081184387207 seconds
-
-[18.41, 17.99, 18.33, 18.02, 17.96, 17.91, 21.56, 18.23, 18.08, 17.76]
-[53.89]
-52.42618942260742
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1116, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.347081184387207, 'TIME_S_1KI': 9.27157812221076, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2825.247347984314, 'W': 53.89}
-[18.41, 17.99, 18.33, 18.02, 17.96, 17.91, 21.56, 18.23, 18.08, 17.76, 18.54, 18.29, 18.11, 17.98, 18.09, 18.71, 18.38, 18.01, 18.26, 18.02]
-330.27500000000003
-16.51375
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1116, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.347081184387207, 'TIME_S_1KI': 9.27157812221076, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2825.247347984314, 'W': 53.89, 'J_1KI': 2531.5836451472346, 'W_1KI': 48.288530465949826, 'W_D': 37.37625, 'J_D': 1959.4943624067305, 'W_D_1KI': 33.491263440860216, 'J_D_1KI': 30.010092688942844}
+[18.97, 18.46, 18.59, 18.54, 18.42, 18.73, 22.71, 18.61, 18.6, 18.87]
+[77.39]
+22.030972480773926
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1167, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.299102067947388, 'TIME_S_1KI': 8.82528026387951, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1704.976960287094, 'W': 77.39}
+[18.97, 18.46, 18.59, 18.54, 18.42, 18.73, 22.71, 18.61, 18.6, 18.87, 18.92, 18.63, 18.51, 18.39, 18.7, 19.25, 18.58, 18.87, 18.56, 19.24]
+340.15
+17.0075
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1167, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.299102067947388, 'TIME_S_1KI': 8.82528026387951, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1704.976960287094, 'W': 77.39, 'J_1KI': 1460.9913969897977, 'W_1KI': 66.31533847472151, 'W_D': 60.3825, 'J_D': 1330.2851958203316, 'W_D_1KI': 51.741645244215945, 'J_D_1KI': 44.33731383394682}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.05.json
new file mode 100644
index 0000000..a21684b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 245, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.408657312393188, "TIME_S_1KI": 42.484315560788524, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3208.5461443829536, "W": 63.89, "J_1KI": 13096.106711767157, "W_1KI": 260.7755102040816, "W_D": 47.0305, "J_D": 2361.8646023384335, "W_D_1KI": 191.96122448979594, "J_D_1KI": 783.5152019991672}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.05.output
new file mode 100644
index 0000000..36c63f1
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_0.05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 4.282553195953369}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2509,      5000,  ...,
+                            124994889, 124997381, 125000000]),
+       col_indices=tensor([   39,    67,   120,  ..., 49971, 49987, 49999]),
+       values=tensor([0.1239, 0.6091, 0.1309,  ..., 0.2312, 0.6997, 0.4588]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.7734, 0.0486, 0.5881,  ..., 0.1213, 0.2314, 0.3704])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 4.282553195953369 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '245', '-ss', '50000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.408657312393188}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2469,      4879,  ...,
+                            124994934, 124997542, 125000000]),
+       col_indices=tensor([    3,    19,    65,  ..., 49948, 49961, 49973]),
+       values=tensor([0.8915, 0.8778, 0.4113,  ..., 0.0094, 0.1568, 0.0935]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.4183, 0.8255, 0.8618,  ..., 0.9842, 0.9207, 0.5380])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 10.408657312393188 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([        0,      2469,      4879,  ...,
+                            124994934, 124997542, 125000000]),
+       col_indices=tensor([    3,    19,    65,  ..., 49948, 49961, 49973]),
+       values=tensor([0.8915, 0.8778, 0.4113,  ..., 0.0094, 0.1568, 0.0935]),
+       size=(50000, 50000), nnz=125000000, layout=torch.sparse_csr)
+tensor([0.4183, 0.8255, 0.8618,  ..., 0.9842, 0.9207, 0.5380])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000000
+Density: 0.05
+Time: 10.408657312393188 seconds
+
+[19.87, 19.45, 19.2, 18.52, 18.89, 18.52, 19.03, 18.42, 18.52, 18.49]
+[63.89]
+50.21984887123108
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 245, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.408657312393188, 'TIME_S_1KI': 42.484315560788524, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3208.5461443829536, 'W': 63.89}
+[19.87, 19.45, 19.2, 18.52, 18.89, 18.52, 19.03, 18.42, 18.52, 18.49, 19.18, 18.4, 18.51, 18.38, 18.95, 18.47, 18.59, 18.61, 18.77, 18.38]
+337.18999999999994
+16.859499999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 245, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.408657312393188, 'TIME_S_1KI': 42.484315560788524, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3208.5461443829536, 'W': 63.89, 'J_1KI': 13096.106711767157, 'W_1KI': 260.7755102040816, 'W_D': 47.0305, 'J_D': 2361.8646023384335, 'W_D_1KI': 191.96122448979594, 'J_D_1KI': 783.5152019991672}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json
index 3827350..ffdee89 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 114513, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.690773725509644, "TIME_S_1KI": 0.09335860317614283, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1104.0986349916457, "W": 76.13, "J_1KI": 9.641688148870832, "W_1KI": 0.6648153484757189, "W_D": 59.82149999999999, "J_D": 867.5796202962398, "W_D_1KI": 0.5223992035838725, "J_D_1KI": 0.004561920511940762}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 111596, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.293413400650024, "TIME_S_1KI": 0.09223819313102642, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1166.8953761601447, "W": 84.57, "J_1KI": 10.456426540020653, "W_1KI": 0.7578228610344456, "W_D": 67.21674999999999, "J_D": 927.4555371350644, "W_D_1KI": 0.6023222158500304, "J_D_1KI": 0.005397345925033428}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output
index 1524e8c..0d924d9 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.025936126708984375}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.02682971954345703}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([20193,  9953, 42880,  ..., 12429,  7497, 42914]),
-       values=tensor([0.2197, 0.8269, 0.1857,  ..., 0.3505, 0.1509, 0.9771]),
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([25652, 38257,  1169,  ...,  2722, 11737, 19460]),
+       values=tensor([0.9159, 0.4620, 0.8343,  ..., 0.9065, 0.0560, 0.0364]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.1918, 0.1249, 0.1152,  ..., 0.8034, 0.5794, 0.7689])
+tensor([0.9019, 0.0029, 0.3566,  ..., 0.1355, 0.7017, 0.7582])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -15,18 +15,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 0.025936126708984375 seconds
+Time: 0.02682971954345703 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '40484', '-ss', '50000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.938389301300049}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '39135', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.6821634769439697}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     2,  ..., 24997, 24998, 25000]),
-       col_indices=tensor([10460, 10153,  3528,  ..., 24271,  7757, 10191]),
-       values=tensor([0.9506, 0.4415, 0.5851,  ..., 0.4538, 0.6997, 0.3353]),
+tensor(crow_indices=tensor([    0,     1,     4,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([ 1978, 22305, 34616,  ..., 21626, 43006,  7893]),
+       values=tensor([0.7658, 0.0431, 0.6752,  ..., 0.0137, 0.8831, 0.2221]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8879, 0.8298, 0.7702,  ..., 0.5204, 0.0041, 0.9281])
+tensor([0.4558, 0.0497, 0.4534,  ..., 0.8709, 0.2398, 0.0989])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -34,18 +34,18 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 3.938389301300049 seconds
+Time: 3.6821634769439697 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '107932', '-ss', '50000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.896521091461182}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '111596', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.293413400650024}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 24999, 25000]),
-       col_indices=tensor([28383, 18616, 44948,  ..., 18982, 36427, 31817]),
-       values=tensor([0.7119, 0.6692, 0.1695,  ..., 0.9276, 0.2360, 0.8672]),
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([38836, 19073, 33792,  ..., 47433, 48865, 18364]),
+       values=tensor([0.8428, 0.5263, 0.8912,  ..., 0.2987, 0.0859, 0.8646]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.3408, 0.5698, 0.4224,  ..., 0.7704, 0.6104, 0.7559])
+tensor([0.6948, 0.0466, 0.9593,  ..., 0.0633, 0.1230, 0.1300])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -53,18 +53,15 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 9.896521091461182 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '114513', '-ss', '50000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.690773725509644}
+Time: 10.293413400650024 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([ 6936, 24119, 42629,  ..., 33437,  2193,  4338]),
-       values=tensor([0.2031, 0.4334, 0.3517,  ..., 0.1037, 0.1982, 0.4022]),
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([38836, 19073, 33792,  ..., 47433, 48865, 18364]),
+       values=tensor([0.8428, 0.5263, 0.8912,  ..., 0.2987, 0.0859, 0.8646]),
        size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0121, 0.3083, 0.6972,  ..., 0.1499, 0.8195, 0.8871])
+tensor([0.6948, 0.0466, 0.9593,  ..., 0.0633, 0.1230, 0.1300])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -72,29 +69,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 25000
 Density: 1e-05
-Time: 10.690773725509644 seconds
+Time: 10.293413400650024 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
-       col_indices=tensor([ 6936, 24119, 42629,  ..., 33437,  2193,  4338]),
-       values=tensor([0.2031, 0.4334, 0.3517,  ..., 0.1037, 0.1982, 0.4022]),
-       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.0121, 0.3083, 0.6972,  ..., 0.1499, 0.8195, 0.8871])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([50000, 50000])
-Rows: 50000
-Size: 2500000000
-NNZ: 25000
-Density: 1e-05
-Time: 10.690773725509644 seconds
-
-[18.43, 18.05, 18.18, 17.94, 17.98, 18.17, 18.18, 17.96, 18.08, 18.28]
-[76.13]
-14.502806186676025
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 114513, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.690773725509644, 'TIME_S_1KI': 0.09335860317614283, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1104.0986349916457, 'W': 76.13}
-[18.43, 18.05, 18.18, 17.94, 17.98, 18.17, 18.18, 17.96, 18.08, 18.28, 18.27, 17.98, 18.37, 18.47, 18.2, 17.9, 18.1, 18.12, 18.02, 17.96]
-326.17
-16.308500000000002
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 114513, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.690773725509644, 'TIME_S_1KI': 0.09335860317614283, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1104.0986349916457, 'W': 76.13, 'J_1KI': 9.641688148870832, 'W_1KI': 0.6648153484757189, 'W_D': 59.82149999999999, 'J_D': 867.5796202962398, 'W_D_1KI': 0.5223992035838725, 'J_D_1KI': 0.004561920511940762}
+[20.33, 18.49, 18.87, 18.45, 18.55, 18.56, 18.9, 23.02, 19.39, 19.55]
+[84.57]
+13.797982454299927
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 111596, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.293413400650024, 'TIME_S_1KI': 0.09223819313102642, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1166.8953761601447, 'W': 84.57}
+[20.33, 18.49, 18.87, 18.45, 18.55, 18.56, 18.9, 23.02, 19.39, 19.55, 19.17, 18.55, 18.35, 22.92, 19.21, 18.36, 19.07, 18.44, 19.11, 18.6]
+347.065
+17.35325
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 111596, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.293413400650024, 'TIME_S_1KI': 0.09223819313102642, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1166.8953761601447, 'W': 84.57, 'J_1KI': 10.456426540020653, 'W_1KI': 0.7578228610344456, 'W_D': 67.21674999999999, 'J_D': 927.4555371350644, 'W_D_1KI': 0.6023222158500304, 'J_D_1KI': 0.005397345925033428}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.json
index 7635079..19bec8b 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 87123, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.505861759185791, "TIME_S_1KI": 0.12058654728585783, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1083.1826461410521, "W": 80.24, "J_1KI": 12.43279783915903, "W_1KI": 0.9209967517188342, "W_D": 63.674249999999994, "J_D": 859.5568619896172, "W_D_1KI": 0.7308546537653662, "J_D_1KI": 0.00838876822154157}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 87046, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.124921798706055, "TIME_S_1KI": 0.11631691058412856, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1227.6771955490112, "W": 88.8, "J_1KI": 14.103774964375287, "W_1KI": 1.020150265376927, "W_D": 72.0095, "J_D": 995.5452816766501, "W_D_1KI": 0.8272580015164396, "J_D_1KI": 0.009503687722772323}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.output
index 089ad96..a720845 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.029273509979248047}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.029453277587890625}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      5,      7,  ..., 124995, 124998,
+tensor(crow_indices=tensor([     0,      3,      6,  ..., 124996, 124999,
                             125000]),
-       col_indices=tensor([  551, 18742, 22548,  ..., 32794, 16422, 37041]),
-       values=tensor([0.7421, 0.1633, 0.4685,  ..., 0.4955, 0.1690, 0.9373]),
+       col_indices=tensor([11626, 18674, 38151,  ..., 18667, 46031, 40236]),
+       values=tensor([0.7612, 0.8098, 0.0566,  ..., 0.5099, 0.8438, 0.7248]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.5671, 0.1718, 0.7006,  ..., 0.0603, 0.4651, 0.4190])
+tensor([0.8787, 0.0380, 0.9789,  ..., 0.5227, 0.6173, 0.3832])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -16,19 +16,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 0.029273509979248047 seconds
+Time: 0.029453277587890625 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '35868', '-ss', '50000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.322763681411743}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '35649', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.3001580238342285}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      6,      7,  ..., 124993, 124996,
+tensor(crow_indices=tensor([     0,      1,      4,  ..., 124996, 124999,
                             125000]),
-       col_indices=tensor([10292, 17103, 19384,  ..., 21480, 22459, 30474]),
-       values=tensor([0.3992, 0.1013, 0.2691,  ..., 0.1460, 0.2288, 0.5075]),
+       col_indices=tensor([ 8274,    53,  3255,  ..., 22982, 41193,  1491]),
+       values=tensor([0.6827, 0.3956, 0.5638,  ..., 0.4920, 0.3870, 0.5841]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.9479, 0.9531, 0.4260,  ..., 0.3198, 0.2541, 0.8697])
+tensor([0.8552, 0.7816, 0.1886,  ..., 0.7134, 0.7031, 0.0804])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -36,19 +36,19 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 4.322763681411743 seconds
+Time: 4.3001580238342285 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '87123', '-ss', '50000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.505861759185791}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '87046', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.124921798706055}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      2,  ..., 124993, 124998,
+tensor(crow_indices=tensor([     0,      2,      8,  ..., 124997, 124998,
                             125000]),
-       col_indices=tensor([ 2935, 11122, 10966,  ..., 49613,  4331, 15007]),
-       values=tensor([0.3574, 0.4392, 0.6710,  ..., 0.1033, 0.1015, 0.5102]),
+       col_indices=tensor([39599, 48767,  2471,  ..., 11144,  1728, 14804]),
+       values=tensor([0.9256, 0.8305, 0.6433,  ..., 0.8812, 0.1393, 0.1023]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.0826, 0.0461, 0.6873,  ..., 0.1063, 0.7746, 0.5118])
+tensor([0.5907, 0.4322, 0.8713,  ..., 0.0184, 0.2881, 0.6574])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -56,16 +56,16 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.505861759185791 seconds
+Time: 10.124921798706055 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,      0,      2,  ..., 124993, 124998,
+tensor(crow_indices=tensor([     0,      2,      8,  ..., 124997, 124998,
                             125000]),
-       col_indices=tensor([ 2935, 11122, 10966,  ..., 49613,  4331, 15007]),
-       values=tensor([0.3574, 0.4392, 0.6710,  ..., 0.1033, 0.1015, 0.5102]),
+       col_indices=tensor([39599, 48767,  2471,  ..., 11144,  1728, 14804]),
+       values=tensor([0.9256, 0.8305, 0.6433,  ..., 0.8812, 0.1393, 0.1023]),
        size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
-tensor([0.0826, 0.0461, 0.6873,  ..., 0.1063, 0.7746, 0.5118])
+tensor([0.5907, 0.4322, 0.8713,  ..., 0.0184, 0.2881, 0.6574])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([50000, 50000])
@@ -73,13 +73,13 @@ Rows: 50000
 Size: 2500000000
 NNZ: 125000
 Density: 5e-05
-Time: 10.505861759185791 seconds
+Time: 10.124921798706055 seconds
 
-[18.58, 18.04, 18.0, 17.91, 17.9, 20.59, 18.05, 18.07, 17.88, 18.18]
-[80.24]
-13.499285221099854
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87123, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.505861759185791, 'TIME_S_1KI': 0.12058654728585783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1083.1826461410521, 'W': 80.24}
-[18.58, 18.04, 18.0, 17.91, 17.9, 20.59, 18.05, 18.07, 17.88, 18.18, 18.74, 21.37, 18.26, 18.13, 18.12, 17.84, 18.06, 17.91, 18.35, 18.17]
-331.315
-16.56575
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87123, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.505861759185791, 'TIME_S_1KI': 0.12058654728585783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1083.1826461410521, 'W': 80.24, 'J_1KI': 12.43279783915903, 'W_1KI': 0.9209967517188342, 'W_D': 63.674249999999994, 'J_D': 859.5568619896172, 'W_D_1KI': 0.7308546537653662, 'J_D_1KI': 0.00838876822154157}
+[18.89, 18.63, 18.4, 18.48, 18.59, 18.44, 18.53, 18.56, 18.51, 18.56]
+[88.8]
+13.825193643569946
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87046, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.124921798706055, 'TIME_S_1KI': 0.11631691058412856, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1227.6771955490112, 'W': 88.8}
+[18.89, 18.63, 18.4, 18.48, 18.59, 18.44, 18.53, 18.56, 18.51, 18.56, 18.7, 18.82, 18.63, 18.49, 18.69, 18.87, 18.78, 19.66, 18.44, 18.43]
+335.81000000000006
+16.7905
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87046, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.124921798706055, 'TIME_S_1KI': 0.11631691058412856, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1227.6771955490112, 'W': 88.8, 'J_1KI': 14.103774964375287, 'W_1KI': 1.020150265376927, 'W_D': 72.0095, 'J_D': 995.5452816766501, 'W_D_1KI': 0.8272580015164396, 'J_D_1KI': 0.009503687722772323}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json
index c76a143..97f2f8f 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 334626, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.109118700027466, "TIME_S_1KI": 0.030210200940833844, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1025.1804789018631, "W": 73.29, "J_1KI": 3.063660561049838, "W_1KI": 0.219020637965968, "W_D": 56.847750000000005, "J_D": 795.1862951220274, "W_D_1KI": 0.16988443814885876, "J_D_1KI": 0.0005076845139016656}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 338825, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.060312271118164, "TIME_S_1KI": 0.029691764985222944, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1132.0499188041686, "W": 81.52, "J_1KI": 3.341105050702187, "W_1KI": 0.24059617796797755, "W_D": 64.5615, "J_D": 896.5510406388044, "W_D_1KI": 0.19054526673061312, "J_D_1KI": 0.000562370742213866}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output
index 81b5928..2440778 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.020055532455444336}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.0212860107421875}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2500, 2500]),
-       col_indices=tensor([2122, 3396, 4900,  ..., 3006, 1251, 2017]),
-       values=tensor([0.1868, 0.0259, 0.3673,  ..., 0.0909, 0.5358, 0.3608]),
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([ 862, 3282, 1685,  ...,  886, 3494,  391]),
+       values=tensor([0.7655, 0.9778, 0.6082,  ..., 0.3892, 0.0884, 0.1248]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.5194, 0.1996, 0.4802,  ..., 0.7962, 0.4168, 0.5561])
+tensor([0.5013, 0.5983, 0.1757,  ..., 0.3100, 0.7614, 0.7422])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 0.020055532455444336 seconds
+Time: 0.0212860107421875 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52354', '-ss', '5000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.6427783966064453}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '49328', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.528644323348999}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([   0,    0,    0,  ..., 2498, 2499, 2500]),
-       col_indices=tensor([2755, 4785,  642,  ...,  761, 1671, 4009]),
-       values=tensor([0.6216, 0.3711, 0.5927,  ..., 0.4412, 0.8122, 0.2675]),
+       col_indices=tensor([1829,  588, 1447,  ..., 3492,   17,  289]),
+       values=tensor([0.9440, 0.4060, 0.2996,  ..., 0.0921, 0.1221, 0.2711]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.4766, 0.5997, 0.2696,  ..., 0.3490, 0.2681, 0.0383])
+tensor([0.8335, 0.5679, 0.9472,  ..., 0.1841, 0.8163, 0.9061])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 1.6427783966064453 seconds
+Time: 1.528644323348999 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '334626', '-ss', '5000', '-sd', '0.0001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.109118700027466}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '338825', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.060312271118164}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([3166, 2984, 4242,  ..., 1801,  191, 2968]),
-       values=tensor([0.8061, 0.6429, 0.9344,  ..., 0.0259, 0.3545, 0.7535]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([2907, 4652, 3947,  ..., 4388, 1313, 2255]),
+       values=tensor([0.3379, 0.0847, 0.0414,  ..., 0.5545, 0.2311, 0.7207]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.9083, 0.2521, 0.7196,  ..., 0.0784, 0.7825, 0.2390])
+tensor([0.3290, 0.4647, 0.9549,  ..., 0.7359, 0.9189, 0.2327])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.109118700027466 seconds
+Time: 10.060312271118164 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
-       col_indices=tensor([3166, 2984, 4242,  ..., 1801,  191, 2968]),
-       values=tensor([0.8061, 0.6429, 0.9344,  ..., 0.0259, 0.3545, 0.7535]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([2907, 4652, 3947,  ..., 4388, 1313, 2255]),
+       values=tensor([0.3379, 0.0847, 0.0414,  ..., 0.5545, 0.2311, 0.7207]),
        size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
-tensor([0.9083, 0.2521, 0.7196,  ..., 0.0784, 0.7825, 0.2390])
+tensor([0.3290, 0.4647, 0.9549,  ..., 0.7359, 0.9189, 0.2327])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500
 Density: 0.0001
-Time: 10.109118700027466 seconds
+Time: 10.060312271118164 seconds
 
-[20.29, 18.13, 18.23, 18.27, 18.36, 17.99, 18.37, 18.01, 18.07, 17.87]
-[73.29]
-13.987999439239502
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 334626, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.109118700027466, 'TIME_S_1KI': 0.030210200940833844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1025.1804789018631, 'W': 73.29}
-[20.29, 18.13, 18.23, 18.27, 18.36, 17.99, 18.37, 18.01, 18.07, 17.87, 18.97, 18.06, 18.34, 18.0, 18.27, 18.07, 18.01, 18.54, 18.55, 18.02]
-328.845
-16.44225
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 334626, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.109118700027466, 'TIME_S_1KI': 0.030210200940833844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1025.1804789018631, 'W': 73.29, 'J_1KI': 3.063660561049838, 'W_1KI': 0.219020637965968, 'W_D': 56.847750000000005, 'J_D': 795.1862951220274, 'W_D_1KI': 0.16988443814885876, 'J_D_1KI': 0.0005076845139016656}
+[18.81, 19.85, 21.12, 18.64, 18.63, 18.55, 18.61, 18.66, 18.48, 18.77]
+[81.52]
+13.886775255203247
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 338825, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.060312271118164, 'TIME_S_1KI': 0.029691764985222944, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1132.0499188041686, 'W': 81.52}
+[18.81, 19.85, 21.12, 18.64, 18.63, 18.55, 18.61, 18.66, 18.48, 18.77, 18.91, 18.57, 18.97, 18.52, 18.57, 18.47, 18.75, 18.68, 18.38, 18.95]
+339.16999999999996
+16.958499999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 338825, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.060312271118164, 'TIME_S_1KI': 0.029691764985222944, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1132.0499188041686, 'W': 81.52, 'J_1KI': 3.341105050702187, 'W_1KI': 0.24059617796797755, 'W_D': 64.5615, 'J_D': 896.5510406388044, 'W_D_1KI': 0.19054526673061312, 'J_D_1KI': 0.000562370742213866}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json
index 417052f..0ffe450 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 248882, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.432960033416748, "TIME_S_1KI": 0.041919303257836035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1049.6097160863876, "W": 75.02, "J_1KI": 4.2172986237911445, "W_1KI": 0.3014279859531826, "W_D": 58.4725, "J_D": 818.0925636345148, "W_D_1KI": 0.2349406546074043, "J_D_1KI": 0.0009439841153936576}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 240786, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.539137125015259, "TIME_S_1KI": 0.04376972550320724, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1149.7912251281737, "W": 82.49, "J_1KI": 4.775158128496564, "W_1KI": 0.3425863629945262, "W_D": 65.42224999999999, "J_D": 911.8914896125792, "W_D_1KI": 0.2717028813967589, "J_D_1KI": 0.001128399829710859}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output
index 121e30b..b4f6567 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.018964052200317383}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.019769668579101562}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,    14,  ..., 24990, 24995, 25000]),
-       col_indices=tensor([ 484, 2538, 3016,  ..., 2694, 4380, 4909]),
-       values=tensor([0.3483, 0.1743, 0.1939,  ..., 0.2265, 0.8602, 0.7977]),
+tensor(crow_indices=tensor([    0,    12,    15,  ..., 24990, 24996, 25000]),
+       col_indices=tensor([  63,  354,  650,  ..., 3532, 4153, 4665]),
+       values=tensor([0.4182, 0.3317, 0.3643,  ..., 0.9464, 0.1095, 0.7227]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.6164, 0.4905, 0.7241,  ..., 0.3672, 0.4239, 0.9077])
+tensor([0.5240, 0.7741, 0.0525,  ..., 0.8468, 0.5656, 0.2849])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 0.018964052200317383 seconds
+Time: 0.019769668579101562 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '55367', '-ss', '5000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.335857391357422}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53111', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.316013813018799}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,     4,     9,  ..., 24986, 24994, 25000]),
-       col_indices=tensor([1163, 1240, 1422,  ..., 4522, 4571, 4830]),
-       values=tensor([0.2729, 0.7937, 0.6768,  ..., 0.2019, 0.8649, 0.0759]),
+tensor(crow_indices=tensor([    0,     4,     9,  ..., 24986, 24993, 25000]),
+       col_indices=tensor([1400, 1599, 2589,  ..., 2553, 2849, 3160]),
+       values=tensor([0.3612, 0.8374, 0.3348,  ..., 0.4551, 0.5749, 0.2985]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.9266, 0.1779, 0.1408,  ..., 0.8566, 0.7762, 0.0695])
+tensor([0.7196, 0.9731, 0.9838,  ..., 0.2317, 0.3300, 0.5980])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 2.335857391357422 seconds
+Time: 2.316013813018799 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '248882', '-ss', '5000', '-sd', '0.001']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.432960033416748}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '240786', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.539137125015259}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    10,    15,  ..., 24986, 24991, 25000]),
-       col_indices=tensor([ 131,  267,  600,  ..., 3068, 3643, 3839]),
-       values=tensor([0.7605, 0.7816, 0.2401,  ..., 0.7557, 0.2099, 0.5290]),
+tensor(crow_indices=tensor([    0,     7,    12,  ..., 24989, 24993, 25000]),
+       col_indices=tensor([ 225,  485, 1185,  ..., 1376, 2470, 3978]),
+       values=tensor([0.3572, 0.7250, 0.4986,  ..., 0.6618, 0.0645, 0.8832]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8976, 0.4401, 0.6879,  ..., 0.0741, 0.3573, 0.5052])
+tensor([0.0319, 0.1498, 0.5778,  ..., 0.9700, 0.0104, 0.1348])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.432960033416748 seconds
+Time: 10.539137125015259 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([    0,    10,    15,  ..., 24986, 24991, 25000]),
-       col_indices=tensor([ 131,  267,  600,  ..., 3068, 3643, 3839]),
-       values=tensor([0.7605, 0.7816, 0.2401,  ..., 0.7557, 0.2099, 0.5290]),
+tensor(crow_indices=tensor([    0,     7,    12,  ..., 24989, 24993, 25000]),
+       col_indices=tensor([ 225,  485, 1185,  ..., 1376, 2470, 3978]),
+       values=tensor([0.3572, 0.7250, 0.4986,  ..., 0.6618, 0.0645, 0.8832]),
        size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
-tensor([0.8976, 0.4401, 0.6879,  ..., 0.0741, 0.3573, 0.5052])
+tensor([0.0319, 0.1498, 0.5778,  ..., 0.9700, 0.0104, 0.1348])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 25000
 Density: 0.001
-Time: 10.432960033416748 seconds
+Time: 10.539137125015259 seconds
 
-[18.55, 18.1, 18.04, 18.77, 18.18, 18.14, 18.08, 18.33, 18.19, 18.07]
-[75.02]
-13.991065263748169
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 248882, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.432960033416748, 'TIME_S_1KI': 0.041919303257836035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.6097160863876, 'W': 75.02}
-[18.55, 18.1, 18.04, 18.77, 18.18, 18.14, 18.08, 18.33, 18.19, 18.07, 18.74, 17.95, 18.05, 18.34, 18.14, 19.14, 18.15, 18.14, 20.44, 18.18]
-330.95
-16.5475
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 248882, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.432960033416748, 'TIME_S_1KI': 0.041919303257836035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.6097160863876, 'W': 75.02, 'J_1KI': 4.2172986237911445, 'W_1KI': 0.3014279859531826, 'W_D': 58.4725, 'J_D': 818.0925636345148, 'W_D_1KI': 0.2349406546074043, 'J_D_1KI': 0.0009439841153936576}
+[18.8, 18.42, 18.61, 20.73, 19.73, 18.52, 19.41, 18.51, 18.9, 18.66]
+[82.49]
+13.938552856445312
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 240786, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.539137125015259, 'TIME_S_1KI': 0.04376972550320724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1149.7912251281737, 'W': 82.49}
+[18.8, 18.42, 18.61, 20.73, 19.73, 18.52, 19.41, 18.51, 18.9, 18.66, 19.9, 18.56, 19.41, 18.77, 19.35, 18.71, 18.58, 18.48, 18.71, 18.55]
+341.355
+17.06775
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 240786, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.539137125015259, 'TIME_S_1KI': 0.04376972550320724, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1149.7912251281737, 'W': 82.49, 'J_1KI': 4.775158128496564, 'W_1KI': 0.3425863629945262, 'W_D': 65.42224999999999, 'J_D': 911.8914896125792, 'W_D_1KI': 0.2717028813967589, 'J_D_1KI': 0.001128399829710859}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json
index e80d2db..3d2093d 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 163647, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.69955587387085, "TIME_S_1KI": 0.06538192495964393, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1158.7786533069611, "W": 84.04, "J_1KI": 7.080964840827887, "W_1KI": 0.5135443973919473, "W_D": 67.71475000000001, "J_D": 933.6792814614178, "W_D_1KI": 0.41378546505588254, "J_D_1KI": 0.002528524599020346}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 164896, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.690629959106445, "TIME_S_1KI": 0.06483256088144312, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1271.159963607788, "W": 90.0, "J_1KI": 7.708858696437683, "W_1KI": 0.545798563943334, "W_D": 73.195, "J_D": 1033.8061504030227, "W_D_1KI": 0.44388584319813695, "J_D_1KI": 0.0026919139530257674}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output
index 483c400..f4564a4 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.021794557571411133}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.023497343063354492}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     52,    109,  ..., 249903, 249954,
+tensor(crow_indices=tensor([     0,     52,    106,  ..., 249919, 249958,
                             250000]),
-       col_indices=tensor([  37,   94,  126,  ..., 4726, 4735, 4938]),
-       values=tensor([0.6014, 0.9404, 0.8499,  ..., 0.7854, 0.8553, 0.6608]),
+       col_indices=tensor([  86,  197,  474,  ..., 4720, 4843, 4964]),
+       values=tensor([0.1328, 0.0055, 0.0846,  ..., 0.6221, 0.9046, 0.0540]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8748, 0.2409, 0.9137,  ..., 0.2396, 0.5569, 0.4924])
+tensor([0.1198, 0.0635, 0.3662,  ..., 0.5079, 0.7170, 0.6953])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 0.021794557571411133 seconds
+Time: 0.023497343063354492 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '48177', '-ss', '5000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.0911431312561035}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '44685', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.845383405685425}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     42,    103,  ..., 249895, 249947,
+tensor(crow_indices=tensor([     0,     41,     90,  ..., 249910, 249957,
                             250000]),
-       col_indices=tensor([ 128,  146,  225,  ..., 4883, 4900, 4933]),
-       values=tensor([0.9316, 0.8049, 0.8923,  ..., 0.5892, 0.9266, 0.8295]),
+       col_indices=tensor([ 107,  286,  590,  ..., 4505, 4711, 4778]),
+       values=tensor([0.0122, 0.5794, 0.5888,  ..., 0.5900, 0.1134, 0.8897]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.8118, 0.4201, 0.8184,  ..., 0.0833, 0.7283, 0.6165])
+tensor([0.3990, 0.8037, 0.0608,  ..., 0.0444, 0.2711, 0.1109])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 3.0911431312561035 seconds
+Time: 2.845383405685425 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '163647', '-ss', '5000', '-sd', '0.01']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.69955587387085}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '164896', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.690629959106445}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     65,    110,  ..., 249906, 249948,
+tensor(crow_indices=tensor([     0,     63,    113,  ..., 249898, 249954,
                             250000]),
-       col_indices=tensor([  32,  165,  212,  ..., 4365, 4391, 4539]),
-       values=tensor([0.5399, 0.4522, 0.1183,  ..., 0.3103, 0.6929, 0.7632]),
+       col_indices=tensor([  50,  132,  187,  ..., 4892, 4896, 4898]),
+       values=tensor([0.7316, 0.7125, 0.7015,  ..., 0.2059, 0.7394, 0.1021]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3241, 0.6966, 0.4101,  ..., 0.4425, 0.6108, 0.0322])
+tensor([0.9239, 0.8999, 0.9114,  ..., 0.0588, 0.7389, 0.7470])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.69955587387085 seconds
+Time: 10.690629959106445 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([     0,     65,    110,  ..., 249906, 249948,
+tensor(crow_indices=tensor([     0,     63,    113,  ..., 249898, 249954,
                             250000]),
-       col_indices=tensor([  32,  165,  212,  ..., 4365, 4391, 4539]),
-       values=tensor([0.5399, 0.4522, 0.1183,  ..., 0.3103, 0.6929, 0.7632]),
+       col_indices=tensor([  50,  132,  187,  ..., 4892, 4896, 4898]),
+       values=tensor([0.7316, 0.7125, 0.7015,  ..., 0.2059, 0.7394, 0.1021]),
        size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
-tensor([0.3241, 0.6966, 0.4101,  ..., 0.4425, 0.6108, 0.0322])
+tensor([0.9239, 0.8999, 0.9114,  ..., 0.0588, 0.7389, 0.7470])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250000
 Density: 0.01
-Time: 10.69955587387085 seconds
+Time: 10.690629959106445 seconds
 
-[18.19, 18.02, 18.18, 18.28, 17.94, 18.08, 18.33, 18.44, 17.82, 18.2]
-[84.04]
-13.788418054580688
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 163647, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.69955587387085, 'TIME_S_1KI': 0.06538192495964393, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1158.7786533069611, 'W': 84.04}
-[18.19, 18.02, 18.18, 18.28, 17.94, 18.08, 18.33, 18.44, 17.82, 18.2, 18.17, 18.08, 18.2, 17.87, 17.92, 18.23, 18.75, 17.8, 18.09, 18.39]
-326.505
-16.32525
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 163647, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.69955587387085, 'TIME_S_1KI': 0.06538192495964393, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1158.7786533069611, 'W': 84.04, 'J_1KI': 7.080964840827887, 'W_1KI': 0.5135443973919473, 'W_D': 67.71475000000001, 'J_D': 933.6792814614178, 'W_D_1KI': 0.41378546505588254, 'J_D_1KI': 0.002528524599020346}
+[18.92, 18.71, 18.61, 18.55, 18.75, 18.47, 18.65, 18.47, 18.78, 18.74]
+[90.0]
+14.12399959564209
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 164896, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.690629959106445, 'TIME_S_1KI': 0.06483256088144312, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1271.159963607788, 'W': 90.0}
+[18.92, 18.71, 18.61, 18.55, 18.75, 18.47, 18.65, 18.47, 18.78, 18.74, 18.9, 18.39, 18.87, 18.47, 19.4, 18.46, 18.89, 18.43, 18.68, 18.48]
+336.1
+16.805
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 164896, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.690629959106445, 'TIME_S_1KI': 0.06483256088144312, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1271.159963607788, 'W': 90.0, 'J_1KI': 7.708858696437683, 'W_1KI': 0.545798563943334, 'W_D': 73.195, 'J_D': 1033.8061504030227, 'W_D_1KI': 0.44388584319813695, 'J_D_1KI': 0.0026919139530257674}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json
index 868cce2..e091508 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 46354, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.460933923721313, "TIME_S_1KI": 0.22567489156753062, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1249.411584854126, "W": 87.62, "J_1KI": 26.95369514721763, "W_1KI": 1.8902360098373387, "W_D": 71.2095, "J_D": 1015.4071473598481, "W_D_1KI": 1.5362104672735903, "J_D_1KI": 0.03314083935094254}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 44184, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.627149820327759, "TIME_S_1KI": 0.24052032003276658, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1296.8053016662598, "W": 90.25, "J_1KI": 29.350110937585097, "W_1KI": 2.0425946043816765, "W_D": 73.50450000000001, "J_D": 1056.188645942688, "W_D_1KI": 1.6635999456816948, "J_D_1KI": 0.03765163737284299}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output
index 056b89d..02790e9 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03854489326477051}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03829145431518555}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     268,     547,  ..., 1249522,
-                            1249752, 1250000]),
-       col_indices=tensor([  55,   88,   92,  ..., 4943, 4993, 4995]),
-       values=tensor([0.0205, 0.9162, 0.9855,  ..., 0.3784, 0.4892, 0.4396]),
+tensor(crow_indices=tensor([      0,     264,     533,  ..., 1249509,
+                            1249753, 1250000]),
+       col_indices=tensor([   6,   18,   20,  ..., 4982, 4996, 4998]),
+       values=tensor([0.1975, 0.6349, 0.2442,  ..., 0.9605, 0.8086, 0.6932]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.2348, 0.0514, 0.2847,  ..., 0.2791, 0.0660, 0.1214])
+tensor([0.7749, 0.3521, 0.9557,  ..., 0.7060, 0.2617, 0.4155])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 0.03854489326477051 seconds
+Time: 0.03829145431518555 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '27240', '-ss', '5000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 6.170231342315674}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '27421', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 6.516348361968994}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     248,     482,  ..., 1249549,
-                            1249769, 1250000]),
-       col_indices=tensor([  15,   21,   37,  ..., 4863, 4919, 4974]),
-       values=tensor([0.5137, 0.1549, 0.4159,  ..., 0.2935, 0.1856, 0.8128]),
+tensor(crow_indices=tensor([      0,     264,     511,  ..., 1249491,
+                            1249738, 1250000]),
+       col_indices=tensor([  39,   40,   44,  ..., 4968, 4977, 4997]),
+       values=tensor([0.1995, 0.5311, 0.6633,  ..., 0.9739, 0.8364, 0.4535]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.7661, 0.7592, 0.8422,  ..., 0.1260, 0.8520, 0.6179])
+tensor([0.6752, 0.8408, 0.9129,  ..., 0.7977, 0.9220, 0.0568])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 6.170231342315674 seconds
+Time: 6.516348361968994 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '46354', '-ss', '5000', '-sd', '0.05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.460933923721313}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '44184', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.627149820327759}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     262,     517,  ..., 1249525,
-                            1249760, 1250000]),
-       col_indices=tensor([   1,   93,  112,  ..., 4957, 4968, 4975]),
-       values=tensor([0.1837, 0.8744, 0.5620,  ..., 0.5820, 0.1645, 0.1343]),
+tensor(crow_indices=tensor([      0,     251,     483,  ..., 1249489,
+                            1249737, 1250000]),
+       col_indices=tensor([   4,    6,   44,  ..., 4966, 4973, 4995]),
+       values=tensor([0.2877, 0.3585, 0.1690,  ..., 0.3030, 0.5649, 0.3335]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.3700, 0.8319, 0.5287,  ..., 0.8208, 0.8249, 0.9051])
+tensor([0.1025, 0.5335, 0.0276,  ..., 0.7210, 0.1987, 0.0972])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.460933923721313 seconds
+Time: 10.627149820327759 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     262,     517,  ..., 1249525,
-                            1249760, 1250000]),
-       col_indices=tensor([   1,   93,  112,  ..., 4957, 4968, 4975]),
-       values=tensor([0.1837, 0.8744, 0.5620,  ..., 0.5820, 0.1645, 0.1343]),
+tensor(crow_indices=tensor([      0,     251,     483,  ..., 1249489,
+                            1249737, 1250000]),
+       col_indices=tensor([   4,    6,   44,  ..., 4966, 4973, 4995]),
+       values=tensor([0.2877, 0.3585, 0.1690,  ..., 0.3030, 0.5649, 0.3335]),
        size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
-tensor([0.3700, 0.8319, 0.5287,  ..., 0.8208, 0.8249, 0.9051])
+tensor([0.1025, 0.5335, 0.0276,  ..., 0.7210, 0.1987, 0.0972])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250000
 Density: 0.05
-Time: 10.460933923721313 seconds
+Time: 10.627149820327759 seconds
 
-[18.2, 17.97, 18.75, 20.89, 18.05, 18.04, 17.92, 18.15, 18.09, 18.27]
-[87.62]
-14.25943374633789
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46354, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.460933923721313, 'TIME_S_1KI': 0.22567489156753062, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1249.411584854126, 'W': 87.62}
-[18.2, 17.97, 18.75, 20.89, 18.05, 18.04, 17.92, 18.15, 18.09, 18.27, 18.35, 17.98, 17.96, 18.31, 17.83, 17.89, 18.02, 18.18, 17.85, 17.84]
-328.21000000000004
-16.410500000000003
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46354, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.460933923721313, 'TIME_S_1KI': 0.22567489156753062, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1249.411584854126, 'W': 87.62, 'J_1KI': 26.95369514721763, 'W_1KI': 1.8902360098373387, 'W_D': 71.2095, 'J_D': 1015.4071473598481, 'W_D_1KI': 1.5362104672735903, 'J_D_1KI': 0.03314083935094254}
+[18.92, 18.52, 18.65, 18.58, 18.51, 18.51, 19.18, 18.35, 18.69, 18.45]
+[90.25]
+14.369033813476562
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 44184, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.627149820327759, 'TIME_S_1KI': 0.24052032003276658, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1296.8053016662598, 'W': 90.25}
+[18.92, 18.52, 18.65, 18.58, 18.51, 18.51, 19.18, 18.35, 18.69, 18.45, 18.88, 18.64, 18.5, 18.52, 18.57, 18.39, 18.74, 18.57, 18.42, 18.89]
+334.90999999999997
+16.7455
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 44184, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.627149820327759, 'TIME_S_1KI': 0.24052032003276658, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1296.8053016662598, 'W': 90.25, 'J_1KI': 29.350110937585097, 'W_1KI': 2.0425946043816765, 'W_D': 73.50450000000001, 'J_D': 1056.188645942688, 'W_D_1KI': 1.6635999456816948, 'J_D_1KI': 0.03765163737284299}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json
index 30ee0bd..f04e653 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 19580, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.499455213546753, "TIME_S_1KI": 0.5362336676990169, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1272.879629366398, "W": 87.53, "J_1KI": 65.00917412494371, "W_1KI": 4.470377936670071, "W_D": 70.8715, "J_D": 1030.6282263525725, "W_D_1KI": 3.6195863125638406, "J_D_1KI": 0.18486140513604907}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 18964, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.52883791923523, "TIME_S_1KI": 0.5552013245747326, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1289.2344594287874, "W": 89.18, "J_1KI": 67.98325561214867, "W_1KI": 4.702594389369332, "W_D": 72.16050000000001, "J_D": 1043.1913344876768, "W_D_1KI": 3.8051307740982923, "J_D_1KI": 0.20065022010642755}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output
index e7e4d14..84960d6 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.06696105003356934}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.06770515441894531}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     497,     967,  ..., 2499018,
-                            2499482, 2500000]),
-       col_indices=tensor([   1,    5,   13,  ..., 4954, 4967, 4978]),
-       values=tensor([0.9405, 0.9423, 0.1495,  ..., 0.5665, 0.9976, 0.7425]),
+tensor(crow_indices=tensor([      0,     479,     947,  ..., 2499022,
+                            2499522, 2500000]),
+       col_indices=tensor([  24,   25,   38,  ..., 4963, 4965, 4978]),
+       values=tensor([0.7367, 0.1744, 0.5199,  ..., 0.8158, 0.8777, 0.0978]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.7464, 0.9379, 0.0503,  ..., 0.7608, 0.2384, 0.8928])
+tensor([0.8006, 0.3041, 0.8106,  ..., 0.5156, 0.6299, 0.1996])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 0.06696105003356934 seconds
+Time: 0.06770515441894531 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '15680', '-ss', '5000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.408252954483032}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '15508', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.586194038391113}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     497,     993,  ..., 2499009,
-                            2499527, 2500000]),
-       col_indices=tensor([  20,   30,   45,  ..., 4969, 4978, 4985]),
-       values=tensor([0.7997, 0.6173, 0.7759,  ..., 0.4558, 0.1544, 0.5880]),
+tensor(crow_indices=tensor([      0,     486,    1021,  ..., 2499001,
+                            2499503, 2500000]),
+       col_indices=tensor([   6,   22,   32,  ..., 4948, 4967, 4987]),
+       values=tensor([0.7770, 0.4847, 0.4860,  ..., 0.5070, 0.8737, 0.9662]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.0537, 0.7107, 0.3297,  ..., 0.5168, 0.3739, 0.3386])
+tensor([0.4938, 0.2706, 0.5992,  ..., 0.2629, 0.2006, 0.7946])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 8.408252954483032 seconds
+Time: 8.586194038391113 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '19580', '-ss', '5000', '-sd', '0.1']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.499455213546753}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '18964', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.52883791923523}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     479,    1002,  ..., 2498942,
-                            2499462, 2500000]),
-       col_indices=tensor([   2,    9,   12,  ..., 4986, 4993, 4996]),
-       values=tensor([0.9703, 0.1385, 0.8259,  ..., 0.8503, 0.3399, 0.6161]),
+tensor(crow_indices=tensor([      0,     476,     998,  ..., 2498989,
+                            2499506, 2500000]),
+       col_indices=tensor([   0,   10,   15,  ..., 4984, 4997, 4999]),
+       values=tensor([0.5072, 0.7041, 0.1877,  ..., 0.5081, 0.5481, 0.3350]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4198, 0.0425, 0.0591,  ..., 0.3064, 0.5860, 0.9864])
+tensor([0.9938, 0.7251, 0.4236,  ..., 0.4094, 0.3045, 0.8450])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.499455213546753 seconds
+Time: 10.52883791923523 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     479,    1002,  ..., 2498942,
-                            2499462, 2500000]),
-       col_indices=tensor([   2,    9,   12,  ..., 4986, 4993, 4996]),
-       values=tensor([0.9703, 0.1385, 0.8259,  ..., 0.8503, 0.3399, 0.6161]),
+tensor(crow_indices=tensor([      0,     476,     998,  ..., 2498989,
+                            2499506, 2500000]),
+       col_indices=tensor([   0,   10,   15,  ..., 4984, 4997, 4999]),
+       values=tensor([0.5072, 0.7041, 0.1877,  ..., 0.5081, 0.5481, 0.3350]),
        size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
-tensor([0.4198, 0.0425, 0.0591,  ..., 0.3064, 0.5860, 0.9864])
+tensor([0.9938, 0.7251, 0.4236,  ..., 0.4094, 0.3045, 0.8450])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 2500000
 Density: 0.1
-Time: 10.499455213546753 seconds
+Time: 10.52883791923523 seconds
 
-[18.41, 17.85, 18.5, 18.03, 18.16, 17.87, 17.95, 21.49, 18.34, 18.02]
-[87.53]
-14.54220986366272
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19580, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.499455213546753, 'TIME_S_1KI': 0.5362336676990169, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1272.879629366398, 'W': 87.53}
-[18.41, 17.85, 18.5, 18.03, 18.16, 17.87, 17.95, 21.49, 18.34, 18.02, 18.22, 18.11, 19.31, 20.46, 18.5, 18.28, 17.98, 18.24, 17.73, 18.09]
-333.17
-16.6585
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19580, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.499455213546753, 'TIME_S_1KI': 0.5362336676990169, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1272.879629366398, 'W': 87.53, 'J_1KI': 65.00917412494371, 'W_1KI': 4.470377936670071, 'W_D': 70.8715, 'J_D': 1030.6282263525725, 'W_D_1KI': 3.6195863125638406, 'J_D_1KI': 0.18486140513604907}
+[20.33, 18.52, 18.78, 18.57, 18.89, 19.21, 18.64, 18.34, 22.74, 18.79]
+[89.18]
+14.456542491912842
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 18964, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.52883791923523, 'TIME_S_1KI': 0.5552013245747326, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1289.2344594287874, 'W': 89.18}
+[20.33, 18.52, 18.78, 18.57, 18.89, 19.21, 18.64, 18.34, 22.74, 18.79, 18.95, 18.6, 18.77, 18.62, 18.46, 18.58, 18.64, 18.33, 18.48, 18.37]
+340.39
+17.0195
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 18964, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.52883791923523, 'TIME_S_1KI': 0.5552013245747326, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1289.2344594287874, 'W': 89.18, 'J_1KI': 67.98325561214867, 'W_1KI': 4.702594389369332, 'W_D': 72.16050000000001, 'J_D': 1043.1913344876768, 'W_D_1KI': 3.8051307740982923, 'J_D_1KI': 0.20065022010642755}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.json
index e35fdc8..35375b5 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 9170, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.682870388031006, "TIME_S_1KI": 1.1649804130895316, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1294.0732465410233, "W": 85.83, "J_1KI": 141.1203104188684, "W_1KI": 9.359869138495092, "W_D": 69.25649999999999, "J_D": 1044.1918187005517, "W_D_1KI": 7.552508178844056, "J_D_1KI": 0.8236104884235611}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 9069, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.808780908584595, "TIME_S_1KI": 1.1918382300787953, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1335.8631198358535, "W": 87.82, "J_1KI": 147.2999360277708, "W_1KI": 9.683537324953136, "W_D": 71.07149999999999, "J_D": 1081.09537373507, "W_D_1KI": 7.8367515712868006, "J_D_1KI": 0.8641252146087551}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.output
index 5537faf..9105ea4 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.13022398948669434}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.13054227828979492}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1019,    1995,  ..., 4997994,
-                            4998986, 5000000]),
-       col_indices=tensor([   3,    6,   16,  ..., 4985, 4988, 4992]),
-       values=tensor([0.7011, 0.4191, 0.0262,  ..., 0.8760, 0.6600, 0.8030]),
+tensor(crow_indices=tensor([      0,    1027,    2015,  ..., 4998071,
+                            4999049, 5000000]),
+       col_indices=tensor([   1,    6,    7,  ..., 4987, 4992, 4995]),
+       values=tensor([0.1450, 0.0999, 0.1753,  ..., 0.4732, 0.7755, 0.6556]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.6407, 0.2177, 0.8923,  ..., 0.2240, 0.7772, 0.6410])
+tensor([0.1160, 0.7355, 0.8244,  ..., 0.8238, 0.4173, 0.8898])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 0.13022398948669434 seconds
+Time: 0.13054227828979492 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8063', '-ss', '5000', '-sd', '0.2']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.23195219039917}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8043', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.311188459396362}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,     967,    1963,  ..., 4997972,
-                            4999023, 5000000]),
-       col_indices=tensor([   1,    8,   12,  ..., 4992, 4997, 4998]),
-       values=tensor([0.9256, 0.0587, 0.0398,  ..., 0.7563, 0.3156, 0.8023]),
+tensor(crow_indices=tensor([      0,    1012,    2014,  ..., 4998016,
+                            4999028, 5000000]),
+       col_indices=tensor([   0,    2,    9,  ..., 4995, 4998, 4999]),
+       values=tensor([0.5092, 0.6011, 0.2048,  ..., 0.5892, 0.0267, 0.9458]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.2454, 0.6913, 0.0165,  ..., 0.7228, 0.0396, 0.9796])
+tensor([0.6588, 0.2413, 0.0767,  ..., 0.4428, 0.1929, 0.0330])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 9.23195219039917 seconds
+Time: 9.311188459396362 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9170', '-ss', '5000', '-sd', '0.2']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.682870388031006}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9069', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.808780908584595}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1039,    2045,  ..., 4997951,
-                            4999001, 5000000]),
-       col_indices=tensor([   1,    3,   21,  ..., 4981, 4993, 4997]),
-       values=tensor([0.7269, 0.5102, 0.5857,  ..., 0.9126, 0.4820, 0.1281]),
+tensor(crow_indices=tensor([      0,    1030,    2056,  ..., 4997951,
+                            4998993, 5000000]),
+       col_indices=tensor([   3,    5,    7,  ..., 4980, 4988, 4989]),
+       values=tensor([0.7265, 0.8226, 0.3089,  ..., 0.6002, 0.0899, 0.9267]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.7504, 0.3556, 0.6036,  ..., 0.9776, 0.8116, 0.9002])
+tensor([0.7524, 0.0232, 0.5140,  ..., 0.7543, 0.4328, 0.8193])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.682870388031006 seconds
+Time: 10.808780908584595 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1039,    2045,  ..., 4997951,
-                            4999001, 5000000]),
-       col_indices=tensor([   1,    3,   21,  ..., 4981, 4993, 4997]),
-       values=tensor([0.7269, 0.5102, 0.5857,  ..., 0.9126, 0.4820, 0.1281]),
+tensor(crow_indices=tensor([      0,    1030,    2056,  ..., 4997951,
+                            4998993, 5000000]),
+       col_indices=tensor([   3,    5,    7,  ..., 4980, 4988, 4989]),
+       values=tensor([0.7265, 0.8226, 0.3089,  ..., 0.6002, 0.0899, 0.9267]),
        size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
-tensor([0.7504, 0.3556, 0.6036,  ..., 0.9776, 0.8116, 0.9002])
+tensor([0.7524, 0.0232, 0.5140,  ..., 0.7543, 0.4328, 0.8193])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 5000000
 Density: 0.2
-Time: 10.682870388031006 seconds
+Time: 10.808780908584595 seconds
 
-[18.71, 17.74, 17.83, 18.37, 18.26, 18.61, 17.98, 17.99, 18.09, 22.02]
-[85.83]
-15.07716703414917
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.682870388031006, 'TIME_S_1KI': 1.1649804130895316, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1294.0732465410233, 'W': 85.83}
-[18.71, 17.74, 17.83, 18.37, 18.26, 18.61, 17.98, 17.99, 18.09, 22.02, 18.37, 17.98, 18.13, 19.94, 19.96, 18.01, 18.17, 17.86, 18.08, 17.84]
-331.47
-16.573500000000003
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.682870388031006, 'TIME_S_1KI': 1.1649804130895316, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1294.0732465410233, 'W': 85.83, 'J_1KI': 141.1203104188684, 'W_1KI': 9.359869138495092, 'W_D': 69.25649999999999, 'J_D': 1044.1918187005517, 'W_D_1KI': 7.552508178844056, 'J_D_1KI': 0.8236104884235611}
+[18.91, 19.03, 18.53, 18.92, 18.62, 18.25, 18.44, 18.59, 18.48, 18.72]
+[87.82]
+15.211376905441284
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9069, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.808780908584595, 'TIME_S_1KI': 1.1918382300787953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1335.8631198358535, 'W': 87.82}
+[18.91, 19.03, 18.53, 18.92, 18.62, 18.25, 18.44, 18.59, 18.48, 18.72, 19.14, 18.53, 18.56, 18.45, 19.02, 18.38, 18.48, 18.35, 18.59, 18.73]
+334.97
+16.7485
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9069, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.808780908584595, 'TIME_S_1KI': 1.1918382300787953, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1335.8631198358535, 'W': 87.82, 'J_1KI': 147.2999360277708, 'W_1KI': 9.683537324953136, 'W_D': 71.07149999999999, 'J_D': 1081.09537373507, 'W_D_1KI': 7.8367515712868006, 'J_D_1KI': 0.8641252146087551}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.json
index b09a9ac..f9757bf 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5638, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.486673831939697, "TIME_S_1KI": 1.859998905984338, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1324.2176235437394, "W": 84.45, "J_1KI": 234.87364731176646, "W_1KI": 14.978715856686769, "W_D": 67.81275, "J_D": 1063.3373434098958, "W_D_1KI": 12.027802412202908, "J_D_1KI": 2.1333455857046664}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5531, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.627587795257568, "TIME_S_1KI": 1.9214586503810465, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1356.078349585533, "W": 86.22, "J_1KI": 245.17778875167838, "W_1KI": 15.588501175194358, "W_D": 69.2895, "J_D": 1089.7934447182417, "W_D_1KI": 12.527481468088952, "J_D_1KI": 2.2649577776331498}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.output
index 933e0af..a4de7be 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.19828367233276367}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.20510458946228027}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1476,    3005,  ..., 7497107,
-                            7498540, 7500000]),
-       col_indices=tensor([   1,    2,    5,  ..., 4997, 4998, 4999]),
-       values=tensor([0.6696, 0.7406, 0.6295,  ..., 0.1006, 0.8443, 0.3530]),
+tensor(crow_indices=tensor([      0,    1517,    3037,  ..., 7496968,
+                            7498491, 7500000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4994, 4996, 4998]),
+       values=tensor([0.6481, 0.0124, 0.5642,  ..., 0.1896, 0.9400, 0.5644]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.2303, 0.2102, 0.7986,  ..., 0.7152, 0.6703, 0.2250])
+tensor([0.4075, 0.2598, 0.1028,  ..., 0.3258, 0.6684, 0.5241])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 0.19828367233276367 seconds
+Time: 0.20510458946228027 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5295', '-ss', '5000', '-sd', '0.3']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.860368490219116}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5119', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.716318368911743}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1522,    3011,  ..., 7496960,
-                            7498495, 7500000]),
-       col_indices=tensor([   0,    6,    7,  ..., 4993, 4995, 4996]),
-       values=tensor([0.9810, 0.8874, 0.6033,  ..., 0.0373, 0.4053, 0.1135]),
+tensor(crow_indices=tensor([      0,    1465,    2956,  ..., 7496982,
+                            7498498, 7500000]),
+       col_indices=tensor([   2,    4,    9,  ..., 4994, 4995, 4998]),
+       values=tensor([0.9623, 0.7171, 0.2788,  ..., 0.7182, 0.2393, 0.4893]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.3541, 0.8542, 0.0162,  ..., 0.1880, 0.1632, 0.2816])
+tensor([0.2907, 0.3907, 0.7457,  ..., 0.2444, 0.9787, 0.2284])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 9.860368490219116 seconds
+Time: 9.716318368911743 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5638', '-ss', '5000', '-sd', '0.3']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.486673831939697}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5531', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.627587795257568}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1590,    3134,  ..., 7496893,
-                            7498424, 7500000]),
-       col_indices=tensor([   1,    9,   13,  ..., 4990, 4995, 4996]),
-       values=tensor([0.2379, 0.8284, 0.2265,  ..., 0.1431, 0.4495, 0.9988]),
+tensor(crow_indices=tensor([      0,    1504,    3000,  ..., 7497018,
+                            7498539, 7500000]),
+       col_indices=tensor([   2,   10,   11,  ..., 4989, 4993, 4999]),
+       values=tensor([0.2263, 0.8222, 0.1853,  ..., 0.0907, 0.1187, 0.3742]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.8528, 0.9292, 0.5816,  ..., 0.4785, 0.5785, 0.0165])
+tensor([0.2919, 0.0893, 0.8411,  ..., 0.3428, 0.6296, 0.9732])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,16 +56,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.486673831939697 seconds
+Time: 10.627587795257568 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([      0,    1590,    3134,  ..., 7496893,
-                            7498424, 7500000]),
-       col_indices=tensor([   1,    9,   13,  ..., 4990, 4995, 4996]),
-       values=tensor([0.2379, 0.8284, 0.2265,  ..., 0.1431, 0.4495, 0.9988]),
+tensor(crow_indices=tensor([      0,    1504,    3000,  ..., 7497018,
+                            7498539, 7500000]),
+       col_indices=tensor([   2,   10,   11,  ..., 4989, 4993, 4999]),
+       values=tensor([0.2263, 0.8222, 0.1853,  ..., 0.0907, 0.1187, 0.3742]),
        size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
-tensor([0.8528, 0.9292, 0.5816,  ..., 0.4785, 0.5785, 0.0165])
+tensor([0.2919, 0.0893, 0.8411,  ..., 0.3428, 0.6296, 0.9732])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -73,13 +73,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 7500000
 Density: 0.3
-Time: 10.486673831939697 seconds
+Time: 10.627587795257568 seconds
 
-[18.49, 17.91, 18.44, 17.9, 18.58, 17.81, 17.92, 21.43, 18.39, 17.95]
-[84.45]
-15.680492877960205
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5638, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.486673831939697, 'TIME_S_1KI': 1.859998905984338, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1324.2176235437394, 'W': 84.45}
-[18.49, 17.91, 18.44, 17.9, 18.58, 17.81, 17.92, 21.43, 18.39, 17.95, 18.43, 20.84, 19.1, 18.06, 18.27, 17.75, 18.15, 17.72, 18.16, 17.76]
-332.745
-16.63725
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5638, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.486673831939697, 'TIME_S_1KI': 1.859998905984338, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1324.2176235437394, 'W': 84.45, 'J_1KI': 234.87364731176646, 'W_1KI': 14.978715856686769, 'W_D': 67.81275, 'J_D': 1063.3373434098958, 'W_D_1KI': 12.027802412202908, 'J_D_1KI': 2.1333455857046664}
+[20.89, 18.65, 18.69, 18.66, 18.83, 18.51, 18.64, 18.62, 19.09, 18.57]
+[86.22]
+15.728118181228638
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5531, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.627587795257568, 'TIME_S_1KI': 1.9214586503810465, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1356.078349585533, 'W': 86.22}
+[20.89, 18.65, 18.69, 18.66, 18.83, 18.51, 18.64, 18.62, 19.09, 18.57, 19.24, 18.63, 19.54, 18.41, 18.76, 18.52, 18.91, 18.5, 19.09, 18.42]
+338.60999999999996
+16.9305
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5531, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.627587795257568, 'TIME_S_1KI': 1.9214586503810465, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1356.078349585533, 'W': 86.22, 'J_1KI': 245.17778875167838, 'W_1KI': 15.588501175194358, 'W_D': 69.2895, 'J_D': 1089.7934447182417, 'W_D_1KI': 12.527481468088952, 'J_D_1KI': 2.2649577776331498}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.json
index 6bb4210..b1cc6e6 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2997, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.852682113647461, "TIME_S_1KI": 3.6211818864355894, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1423.3116033363342, "W": 83.38, "J_1KI": 474.9121132253367, "W_1KI": 27.82115448782115, "W_D": 66.859, "J_D": 1141.295160559654, "W_D_1KI": 22.30864197530864, "J_D_1KI": 7.443657649418965}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2769, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.38504934310913, "TIME_S_1KI": 3.7504692463377145, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1383.2232024383545, "W": 84.59, "J_1KI": 499.5388957884993, "W_1KI": 30.54893463344168, "W_D": 67.334, "J_D": 1101.0515558929444, "W_D_1KI": 24.31708197905381, "J_D_1KI": 8.781900317462553}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.output
index 7229c6c..af7b6f9 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.output
@@ -1,14 +1,34 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.37653517723083496}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.37917041778564453}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2029,     4093,  ...,  9995991,
+tensor(crow_indices=tensor([       0,     1948,     3957,  ...,  9995933,
+                             9997945, 10000000]),
+       col_indices=tensor([   8,   10,   11,  ..., 4990, 4993, 4994]),
+       values=tensor([0.8896, 0.7869, 0.6720,  ..., 0.3221, 0.4513, 0.5276]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8022, 0.1843, 0.7407,  ..., 0.1218, 0.5519, 0.4188])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 0.37917041778564453 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2769', '-ss', '5000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.38504934310913}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1983,     3967,  ...,  9995969,
                              9998004, 10000000]),
-       col_indices=tensor([   2,    3,    6,  ..., 4991, 4993, 4995]),
-       values=tensor([0.7344, 0.2463, 0.3008,  ..., 0.7679, 0.9534, 0.0176]),
+       col_indices=tensor([   0,    1,    3,  ..., 4996, 4997, 4999]),
+       values=tensor([0.0447, 0.2201, 0.1579,  ..., 0.6673, 0.7600, 0.5875]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.9829, 0.9205, 0.8754,  ..., 0.7887, 0.3539, 0.3362])
+tensor([0.3136, 0.6559, 0.5006,  ..., 0.2088, 0.2304, 0.3448])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 0.37653517723083496 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2788', '-ss', '5000', '-sd', '0.4']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 9.764836072921753}
+Time: 10.38504934310913 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2035,     4051,  ...,  9995975,
-                             9998000, 10000000]),
-       col_indices=tensor([   1,    7,   13,  ..., 4993, 4995, 4999]),
-       values=tensor([0.5646, 0.0016, 0.0540,  ..., 0.1360, 0.4218, 0.1397]),
+tensor(crow_indices=tensor([       0,     1983,     3967,  ...,  9995969,
+                             9998004, 10000000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4996, 4997, 4999]),
+       values=tensor([0.0447, 0.2201, 0.1579,  ..., 0.6673, 0.7600, 0.5875]),
        size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.2261, 0.1429, 0.2182,  ..., 0.9969, 0.6483, 0.4023])
+tensor([0.3136, 0.6559, 0.5006,  ..., 0.2088, 0.2304, 0.3448])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,50 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 10000000
 Density: 0.4
-Time: 9.764836072921753 seconds
+Time: 10.38504934310913 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2997', '-ss', '5000', '-sd', '0.4']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.852682113647461}
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2037,     4021,  ...,  9995904,
-                             9997943, 10000000]),
-       col_indices=tensor([   4,    9,   10,  ..., 4993, 4994, 4995]),
-       values=tensor([0.7234, 0.7434, 0.9107,  ..., 0.4914, 0.1939, 0.0446]),
-       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.2935, 0.8834, 0.9590,  ..., 0.1952, 0.7236, 0.3428])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 10000000
-Density: 0.4
-Time: 10.852682113647461 seconds
-
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2037,     4021,  ...,  9995904,
-                             9997943, 10000000]),
-       col_indices=tensor([   4,    9,   10,  ..., 4993, 4994, 4995]),
-       values=tensor([0.7234, 0.7434, 0.9107,  ..., 0.4914, 0.1939, 0.0446]),
-       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
-tensor([0.2935, 0.8834, 0.9590,  ..., 0.1952, 0.7236, 0.3428])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 10000000
-Density: 0.4
-Time: 10.852682113647461 seconds
-
-[18.3, 18.02, 18.13, 22.02, 18.21, 18.26, 17.95, 17.94, 17.94, 18.03]
-[83.38]
-17.07017993927002
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2997, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.852682113647461, 'TIME_S_1KI': 3.6211818864355894, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1423.3116033363342, 'W': 83.38}
-[18.3, 18.02, 18.13, 22.02, 18.21, 18.26, 17.95, 17.94, 17.94, 18.03, 18.77, 18.01, 18.52, 18.59, 18.17, 17.81, 18.04, 18.12, 18.22, 17.84]
-330.41999999999996
-16.520999999999997
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2997, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.852682113647461, 'TIME_S_1KI': 3.6211818864355894, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1423.3116033363342, 'W': 83.38, 'J_1KI': 474.9121132253367, 'W_1KI': 27.82115448782115, 'W_D': 66.859, 'J_D': 1141.295160559654, 'W_D_1KI': 22.30864197530864, 'J_D_1KI': 7.443657649418965}
+[18.83, 18.68, 18.58, 18.59, 18.43, 18.65, 18.69, 18.22, 22.98, 18.9]
+[84.59]
+16.352088928222656
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2769, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.38504934310913, 'TIME_S_1KI': 3.7504692463377145, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1383.2232024383545, 'W': 84.59}
+[18.83, 18.68, 18.58, 18.59, 18.43, 18.65, 18.69, 18.22, 22.98, 18.9, 19.07, 18.48, 23.27, 19.52, 18.46, 19.14, 18.96, 18.38, 18.4, 18.58]
+345.12
+17.256
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2769, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.38504934310913, 'TIME_S_1KI': 3.7504692463377145, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1383.2232024383545, 'W': 84.59, 'J_1KI': 499.5388957884993, 'W_1KI': 30.54893463344168, 'W_D': 67.334, 'J_D': 1101.0515558929444, 'W_D_1KI': 24.31708197905381, 'J_D_1KI': 8.781900317462553}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.json
index 65b89f9..2a70fc1 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2368, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.176312685012817, "TIME_S_1KI": 4.297429343333116, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1437.3391367673873, "W": 78.7, "J_1KI": 606.984432756498, "W_1KI": 33.2347972972973, "W_D": 62.306000000000004, "J_D": 1137.9269663968087, "W_D_1KI": 26.311655405405407, "J_D_1KI": 11.111340965120526}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2343, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.741075277328491, "TIME_S_1KI": 4.584325769239646, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1401.9371602535248, "W": 84.04, "J_1KI": 598.3513274662931, "W_1KI": 35.86854460093897, "W_D": 67.28325000000001, "J_D": 1122.4046696528794, "W_D_1KI": 28.71670934699104, "J_D_1KI": 12.256384697819477}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.output
index 4c78e30..ec349ea 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.output
@@ -1,14 +1,14 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.48082637786865234}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.4479796886444092}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2493,     4988,  ..., 12494973,
-                            12497489, 12500000]),
-       col_indices=tensor([   0,    3,    4,  ..., 4995, 4996, 4997]),
-       values=tensor([0.7019, 0.9323, 0.5533,  ..., 0.8475, 0.3948, 0.0670]),
+tensor(crow_indices=tensor([       0,     2552,     5046,  ..., 12495015,
+                            12497490, 12500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4995, 4997, 4998]),
+       values=tensor([0.4993, 0.8383, 0.4900,  ..., 0.5406, 0.0306, 0.9186]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.2037, 0.7210, 0.2412,  ..., 0.0549, 0.3207, 0.0757])
+tensor([0.9628, 0.9871, 0.1356,  ..., 0.1551, 0.9107, 0.4718])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -16,19 +16,19 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 0.48082637786865234 seconds
+Time: 0.4479796886444092 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2183', '-ss', '5000', '-sd', '0.5']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.679495573043823}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2343', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.741075277328491}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2494,     4974,  ..., 12494974,
-                            12497479, 12500000]),
-       col_indices=tensor([   2,    3,    5,  ..., 4990, 4992, 4993]),
-       values=tensor([0.3550, 0.2209, 0.9878,  ..., 0.1100, 0.7010, 0.8735]),
+tensor(crow_indices=tensor([       0,     2523,     4985,  ..., 12495058,
+                            12497538, 12500000]),
+       col_indices=tensor([   0,    2,    3,  ..., 4994, 4996, 4997]),
+       values=tensor([0.7346, 0.1923, 0.5467,  ..., 0.0039, 0.3545, 0.5505]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.0897, 0.5505, 0.7451,  ..., 0.8823, 0.0649, 0.5912])
+tensor([0.4147, 0.5149, 0.6904,  ..., 0.2521, 0.9944, 0.6875])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -36,19 +36,16 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 9.679495573043823 seconds
-
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2368', '-ss', '5000', '-sd', '0.5']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.176312685012817}
+Time: 10.741075277328491 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2500,     5025,  ..., 12495060,
-                            12497549, 12500000]),
-       col_indices=tensor([   3,    7,    9,  ..., 4994, 4997, 4998]),
-       values=tensor([0.3762, 0.0915, 0.3071,  ..., 0.1948, 0.2052, 0.2572]),
+tensor(crow_indices=tensor([       0,     2523,     4985,  ..., 12495058,
+                            12497538, 12500000]),
+       col_indices=tensor([   0,    2,    3,  ..., 4994, 4996, 4997]),
+       values=tensor([0.7346, 0.1923, 0.5467,  ..., 0.0039, 0.3545, 0.5505]),
        size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.2717, 0.6125, 0.0994,  ..., 0.4315, 0.1911, 0.5863])
+tensor([0.4147, 0.5149, 0.6904,  ..., 0.2521, 0.9944, 0.6875])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -56,30 +53,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 12500000
 Density: 0.5
-Time: 10.176312685012817 seconds
+Time: 10.741075277328491 seconds
 
-/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
-  matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([       0,     2500,     5025,  ..., 12495060,
-                            12497549, 12500000]),
-       col_indices=tensor([   3,    7,    9,  ..., 4994, 4997, 4998]),
-       values=tensor([0.3762, 0.0915, 0.3071,  ..., 0.1948, 0.2052, 0.2572]),
-       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
-tensor([0.2717, 0.6125, 0.0994,  ..., 0.4315, 0.1911, 0.5863])
-Matrix Type: synthetic
-Matrix Format: csr
-Shape: torch.Size([5000, 5000])
-Rows: 5000
-Size: 25000000
-NNZ: 12500000
-Density: 0.5
-Time: 10.176312685012817 seconds
-
-[18.38, 18.14, 18.13, 18.09, 18.53, 18.05, 18.22, 17.96, 18.23, 17.96]
-[78.7]
-18.263521432876587
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2368, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.176312685012817, 'TIME_S_1KI': 4.297429343333116, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1437.3391367673873, 'W': 78.7}
-[18.38, 18.14, 18.13, 18.09, 18.53, 18.05, 18.22, 17.96, 18.23, 17.96, 18.81, 17.96, 18.41, 17.98, 18.95, 18.19, 18.23, 17.96, 18.21, 18.13]
-327.88
-16.394
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2368, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.176312685012817, 'TIME_S_1KI': 4.297429343333116, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1437.3391367673873, 'W': 78.7, 'J_1KI': 606.984432756498, 'W_1KI': 33.2347972972973, 'W_D': 62.306000000000004, 'J_D': 1137.9269663968087, 'W_D_1KI': 26.311655405405407, 'J_D_1KI': 11.111340965120526}
+[19.07, 18.64, 18.41, 18.39, 18.48, 18.8, 18.45, 18.44, 18.41, 18.74]
+[84.04]
+16.6817843914032
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2343, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.741075277328491, 'TIME_S_1KI': 4.584325769239646, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1401.9371602535248, 'W': 84.04}
+[19.07, 18.64, 18.41, 18.39, 18.48, 18.8, 18.45, 18.44, 18.41, 18.74, 19.14, 18.63, 18.64, 18.44, 19.12, 18.55, 18.58, 18.45, 18.8, 18.86]
+335.135
+16.75675
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2343, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.741075277328491, 'TIME_S_1KI': 4.584325769239646, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1401.9371602535248, 'W': 84.04, 'J_1KI': 598.3513274662931, 'W_1KI': 35.86854460093897, 'W_D': 67.28325000000001, 'J_D': 1122.4046696528794, 'W_D_1KI': 28.71670934699104, 'J_D_1KI': 12.256384697819477}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json
index 2ac6398..3169aff 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 357325, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.499107599258423, "TIME_S_1KI": 0.029382516194664303, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1011.0901748657226, "W": 72.8, "J_1KI": 2.829609388835717, "W_1KI": 0.20373609459175818, "W_D": 46.4985, "J_D": 645.7991276922226, "W_D_1KI": 0.1301294339886658, "J_D_1KI": 0.0003641766850588842}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 362766, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.475499391555786, "TIME_S_1KI": 0.028876739803498083, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1139.0763648700713, "W": 81.49, "J_1KI": 3.1399755348353247, "W_1KI": 0.22463516426566987, "W_D": 64.764, "J_D": 905.27845986557, "W_D_1KI": 0.17852830750401083, "J_D_1KI": 0.0004921307606115536}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output
index 109e8d2..6c9e726 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output
@@ -1,75 +1,75 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.020415544509887695}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.019411802291870117}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([1333, 4323,  980, 4513,  212, 4800,  425, 2650, 4181,
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-                            918, 1225, 2348, 1665, 3413,  132, 2821,  111,  225,
-                           4735, 1677, 1746, 1399, 3492, 1994, 2191]),
-       values=tensor([0.3602, 0.2302, 0.1896, 0.7717, 0.8956, 0.2078, 0.9487,
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-                      0.1715, 0.2483, 0.6015, 0.5276, 0.6350]),
+       col_indices=tensor([4960, 3833,  633, 4471, 4202, 1676, 1787, 1199, 2306,
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+       values=tensor([0.2869, 0.8959, 0.1230, 0.9181, 0.4916, 0.0949, 0.3853,
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+                      0.3351, 0.2456, 0.3344, 0.3816, 0.2839, 0.4175, 0.6365,
+                      0.3504, 0.9113, 0.8124, 0.7638, 0.9220, 0.3762, 0.0562,
+                      0.4237, 0.6986, 0.8893, 0.4006, 0.4298, 0.5397, 0.7499,
+                      0.6551, 0.8851, 0.7450, 0.5531, 0.2759, 0.8211, 0.1282,
+                      0.1044, 0.9759, 0.0575, 0.3280, 0.1045, 0.1087, 0.6754,
+                      0.1435, 0.0998, 0.4988, 0.8681, 0.0752, 0.4069, 0.8414,
+                      0.3281, 0.8089, 0.9851, 0.4099, 0.4565, 0.6462, 0.9468,
+                      0.9400, 0.7595, 0.9734, 0.7270, 0.7066, 0.1320, 0.7342,
+                      0.7324, 0.9768, 0.2893, 0.1781, 0.1344, 0.5459, 0.0035,
+                      0.5325, 0.6974, 0.3569, 0.7178, 0.6552, 0.2834, 0.8334,
+                      0.2236, 0.3532, 0.7476, 0.4233, 0.3118, 0.7910, 0.8860,
+                      0.7759, 0.9035, 0.9230, 0.9359, 0.3876]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.0146, 0.8199, 0.1846,  ..., 0.5715, 0.8048, 0.4041])
+tensor([0.4538, 0.4285, 0.5081,  ..., 0.4471, 0.6341, 0.2594])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -77,80 +77,107 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 0.020415544509887695 seconds
+Time: 0.019411802291870117 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '51431', '-ss', '5000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.5112977027893066}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '54090', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.56559419631958}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([4110, 4882, 2528,  560, 2850, 1135, 2189,  267,  591,
-                           4248,  392, 4389,  952, 4426,  675,  791, 4134, 2003,
-                            330, 4266, 1795, 3038, 2537, 1414, 4513, 1875, 1420,
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-                           3761, 2384,  847,  509, 4067, 1126, 4356, 2690, 2684,
-                           3064,  181,  819,  409, 3928, 4195, 3862]),
-       values=tensor([0.6256, 0.4971, 0.1060, 0.7139, 0.1823, 0.2509, 0.2673,
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-                      0.8274, 0.9419, 0.8414, 0.0360, 0.8374, 0.9999, 0.3537,
-                      0.3299, 0.5865, 0.7469, 0.2686, 0.3364]),
-       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.4917, 0.3630, 0.9281,  ..., 0.8752, 0.5291, 0.1405])
+       col_indices=tensor([3789, 1243, 4776, 4880, 1902, 1389, 4775, 2798,  986,
+                           2992,  160, 1178, 2753, 1276, 2143, 2125, 2082,  796,
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+                      3.9491e-01, 8.3220e-01]), size=(5000, 5000), nnz=250,
+       layout=torch.sparse_csr)
+tensor([0.8994, 0.1119, 0.9711,  ..., 0.6840, 0.7927, 0.1169])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -158,80 +185,80 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 1.5112977027893066 seconds
+Time: 1.56559419631958 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '357325', '-ss', '5000', '-sd', '1e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.499107599258423}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '362766', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.475499391555786}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
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+                      0.1071, 0.4185, 0.4326, 0.1289, 0.6766, 0.0293, 0.3061,
+                      0.0095, 0.2011, 0.1571, 0.7405, 0.5313, 0.7308, 0.3716,
+                      0.4635, 0.9587, 0.8976, 0.2622, 0.9705, 0.8033, 0.2844,
+                      0.0305, 0.3661, 0.4222, 0.5149, 0.3785]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.5944, 0.7034, 0.9760,  ..., 0.8304, 0.5842, 0.4500])
+tensor([0.4029, 0.7352, 0.6155,  ..., 0.7243, 0.5046, 0.3375])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -239,77 +266,77 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.499107599258423 seconds
+Time: 10.475499391555786 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
 tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
-       col_indices=tensor([2104, 1861,    7, 1520, 3728, 1941, 2107,  138, 2161,
-                           3365,  332, 4735, 2493, 4284,  393, 1314, 1302, 4705,
-                           1583, 2354,  365, 1361, 3891,  149, 1170, 2523, 1316,
-                            504, 3112, 2441, 3025, 3794, 4286, 3194, 1606, 1584,
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-                           2673, 3251, 2341, 1657, 2899, 1405,   28, 3720, 4641,
-                            155, 2571, 1960, 1838, 3742, 1460, 3050, 1966, 3313,
-                           1854, 4564, 1529, 1889, 4664, 3289, 4098, 3070, 1858,
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-                           3053, 2874, 4675, 1142, 2097, 3430, 3192, 3228, 4790,
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-                             66, 3256, 4976, 3641, 1843, 2314, 3228, 1928,  847,
-                           3368, 1129, 1702, 2867, 4161, 4680, 2563,  195,  417,
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-                           2616, 1438, 4125, 4484, 4543, 3714, 1301, 2231, 4571,
-                           1275, 4305, 3280, 4945, 2663, 1185, 1842,  622, 3361,
-                           2569, 1402, 2348, 1829,  182, 3336,  570, 3323,  562,
-                           1935, 4764, 3304, 2399, 1649, 1406, 4405, 2794, 3186,
-                           4464, 4180, 1762, 4406,  310, 3727,  661, 2438, 4818,
-                           1167, 2299, 3410, 2102,  990, 2923, 1131, 3164, 4442,
-                           2744,  348, 2673, 3077, 2766,  827, 2340, 2888, 4315,
-                           1191, 4533, 1425, 1473, 1269, 2758, 4677, 1018, 4467,
-                           4891, 1237,  820, 1622,   44, 4185, 1279, 3969, 4975,
-                           4333, 1750, 1936,  243, 4652, 1623, 3975]),
-       values=tensor([0.9387, 0.4700, 0.2447, 0.6228, 0.4503, 0.1997, 0.0053,
-                      0.5157, 0.4796, 0.4250, 0.8220, 0.3582, 0.4251, 0.4646,
-                      0.9478, 0.0325, 0.0848, 0.5917, 0.2071, 0.4572, 0.9167,
-                      0.3871, 0.6967, 0.0513, 0.7695, 0.7027, 0.3866, 0.0150,
-                      0.1037, 0.9121, 0.5340, 0.5642, 0.8146, 0.9553, 0.8607,
-                      0.1413, 0.3229, 0.0119, 0.0552, 0.9205, 0.4848, 0.8673,
-                      0.6922, 0.3249, 0.7973, 0.5503, 0.3256, 0.5167, 0.2558,
-                      0.6122, 0.0546, 0.7903, 0.3759, 0.7968, 0.2510, 0.1679,
-                      0.4338, 0.6740, 0.2626, 0.4105, 0.4029, 0.3869, 0.1119,
-                      0.9939, 0.6154, 0.1891, 0.7886, 0.8106, 0.2588, 0.8530,
-                      0.1960, 0.5522, 0.9502, 0.2036, 0.8479, 0.5893, 0.2336,
-                      0.5492, 0.4318, 0.6485, 0.5562, 0.9481, 0.9451, 0.8583,
-                      0.7556, 0.1458, 0.7503, 0.1231, 0.2746, 0.0172, 0.6993,
-                      0.4164, 0.7854, 0.6995, 0.8084, 0.5901, 0.9482, 0.9912,
-                      0.8222, 0.8265, 0.6019, 0.5462, 0.6062, 0.4916, 0.3704,
-                      0.9894, 0.1946, 0.9052, 0.1356, 0.8292, 0.4460, 0.3066,
-                      0.5424, 0.9972, 0.4829, 0.2518, 0.5680, 0.4922, 0.3950,
-                      0.5510, 0.1881, 0.6119, 0.3857, 0.5521, 0.8319, 0.6787,
-                      0.8327, 0.0041, 0.6533, 0.9035, 0.3529, 0.2045, 0.8781,
-                      0.7715, 0.7148, 0.2012, 0.4429, 0.6908, 0.0217, 0.9409,
-                      0.2398, 0.9282, 0.8675, 0.2060, 0.8887, 0.5702, 0.9940,
-                      0.9652, 0.4202, 0.5062, 0.1515, 0.6839, 0.1289, 0.7773,
-                      0.8039, 0.1706, 0.5382, 0.6842, 0.4665, 0.8705, 0.9740,
-                      0.3731, 0.3796, 0.0945, 0.8938, 0.9546, 0.7375, 0.4347,
-                      0.0317, 0.0560, 0.1482, 0.2781, 0.1532, 0.0170, 0.3948,
-                      0.1619, 0.3596, 0.1826, 0.6650, 0.4684, 0.2191, 0.9123,
-                      0.0088, 0.2608, 0.7519, 0.3594, 0.8499, 0.1793, 0.6810,
-                      0.4007, 0.7171, 0.6382, 0.5741, 0.6774, 0.9314, 0.4111,
-                      0.3073, 0.3304, 0.1048, 0.8381, 0.8387, 0.6626, 0.9350,
-                      0.3559, 0.0898, 0.9007, 0.8371, 0.0933, 0.7505, 0.1760,
-                      0.8200, 0.6000, 0.8535, 0.8461, 0.8576, 0.1788, 0.4860,
-                      0.8224, 0.8204, 0.6502, 0.3745, 0.9926, 0.3104, 0.0651,
-                      0.2962, 0.9752, 0.3565, 0.2747, 0.0907, 0.8216, 0.2197,
-                      0.9709, 0.0139, 0.1557, 0.9115, 0.1512, 0.0977, 0.5834,
-                      0.2007, 0.9483, 0.8228, 0.2172, 0.6709, 0.2237, 0.7140,
-                      0.3427, 0.4328, 0.7857, 0.0871, 0.8851]),
+       col_indices=tensor([1956, 3431, 4238, 4052,  786, 1266, 4001, 4666, 1092,
+                           3296, 1173, 3822, 1879, 2941,  234, 3811, 4402,  613,
+                           1443, 3709, 2594, 4608, 2613, 4283, 2501, 2445, 1427,
+                           1885, 2181, 4860, 4243, 1214, 4813,  213,  317, 2056,
+                           1188, 2407, 2876, 3155, 1101,  928, 2235, 3824, 2607,
+                           1838, 1357, 2674, 2550, 2106, 3424, 1994,  546, 3354,
+                           2675, 2714, 2261,  610, 3755,  671, 2095, 3697, 1347,
+                            360, 4317, 4158,  117, 2249, 4990,    9,  681, 1881,
+                           3923,  690, 4091, 4107, 2892, 3886,  738, 1353,  208,
+                           2168,  895, 3568, 3595, 4527, 3909, 2258, 3141, 4180,
+                           3592, 2057,  988, 1280, 2222, 2461, 1230, 4436, 4278,
+                            242, 2898, 3677,  486, 2915, 2700, 3313, 1582, 3947,
+                           2304, 3998, 4209, 1784, 1143, 1298, 4100, 1668, 4807,
+                           1129,  194,  547, 3477, 3466, 2653,  743, 1451, 4033,
+                           4459, 4677,  861,  209, 3635, 4103, 4596,  643, 4673,
+                            918, 2069, 3861,  438,   73, 4960, 2624,   85,  282,
+                           1088, 4531, 2175, 2789, 2455, 4369,   79, 3997, 2161,
+                           4382,  981, 4359, 3035, 4336, 2903, 3375, 3402, 3351,
+                           1467, 4310, 1833, 4757,  656, 4630, 2068, 4311, 2912,
+                           3879, 2330, 4316, 2205, 1238, 2693,   24, 3949, 2312,
+                           3596, 1298, 4258,  443, 1549, 4970,  757, 2174, 4522,
+                           1179, 2988, 3936,  197, 3432, 3114, 1592, 3091, 2331,
+                           4463,  471, 4489, 3432, 1070, 2015, 3140,  985, 1046,
+                           1718, 2928, 3116,  850, 3114, 4187,  476, 1975, 2045,
+                            614, 4508,   49, 4308, 3320, 2902, 2360,  679, 1323,
+                            174, 1683,  333, 3655, 3484, 4282, 1715, 4161, 2251,
+                           4716, 3026, 1067, 4926,  819, 4915, 1642, 2499, 4667,
+                           1179,  724, 2553, 2235, 2183, 4346,  882]),
+       values=tensor([0.9579, 0.0390, 0.9157, 0.4104, 0.8526, 0.8581, 0.0404,
+                      0.7977, 0.1127, 0.6398, 0.6320, 0.5466, 0.3249, 0.5353,
+                      0.2779, 0.1888, 0.8800, 0.3646, 0.5797, 0.6102, 0.4524,
+                      0.6672, 0.1933, 0.3172, 0.9142, 0.2116, 0.8694, 0.7671,
+                      0.3860, 0.8538, 0.6306, 0.0812, 0.2634, 0.0747, 0.1165,
+                      0.9209, 0.5169, 0.3874, 0.9909, 0.1687, 0.6244, 0.9682,
+                      0.8180, 0.6958, 0.5743, 0.6762, 0.4274, 0.8734, 0.8720,
+                      0.7096, 0.0314, 0.6237, 0.5408, 0.2599, 0.6993, 0.8694,
+                      0.9325, 0.6629, 0.2586, 0.5189, 0.1311, 0.0244, 0.5487,
+                      0.2381, 0.3764, 0.6005, 0.3517, 0.2902, 0.4481, 0.7095,
+                      0.8595, 0.4167, 0.1455, 0.6941, 0.7561, 0.5053, 0.8598,
+                      0.7230, 0.0345, 0.2736, 0.2353, 0.0978, 0.0877, 0.4407,
+                      0.2045, 0.2192, 0.5832, 0.6619, 0.6681, 0.4330, 0.3404,
+                      0.7309, 0.2497, 0.1260, 0.4309, 0.3463, 0.5670, 0.2970,
+                      0.0325, 0.3660, 0.0477, 0.7916, 0.4884, 0.4378, 0.2655,
+                      0.3694, 0.2809, 0.0571, 0.7346, 0.1636, 0.4691, 0.0250,
+                      0.7714, 0.9482, 0.5488, 0.4704, 0.4720, 0.5671, 0.0525,
+                      0.3009, 0.2823, 0.4234, 0.6898, 0.6608, 0.4215, 0.1505,
+                      0.7591, 0.3435, 0.3440, 0.1149, 0.8423, 0.9694, 0.2894,
+                      0.1485, 0.3692, 0.7112, 0.1061, 0.2705, 0.6923, 0.0732,
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+                      0.9666, 0.4550, 0.0643, 0.0699, 0.5834, 0.9262, 0.5997,
+                      0.8477, 0.8876, 0.3470, 0.5717, 0.3027, 0.1708, 0.3344,
+                      0.6564, 0.5221, 0.9971, 0.5670, 0.6595, 0.4442, 0.6979,
+                      0.0346, 0.7421, 0.0167, 0.0427, 0.0476, 0.9750, 0.8359,
+                      0.0377, 0.5836, 0.0387, 0.6069, 0.4111, 0.5689, 0.8082,
+                      0.2356, 0.9052, 0.6351, 0.6643, 0.1830, 0.8091, 0.9222,
+                      0.0255, 0.1085, 0.2291, 0.7019, 0.5629, 0.9653, 0.1272,
+                      0.0502, 0.4026, 0.3717, 0.7964, 0.3440, 0.2227, 0.9211,
+                      0.0353, 0.8462, 0.1378, 0.9728, 0.9579, 0.9282, 0.2986,
+                      0.0902, 0.8570, 0.3740, 0.3544, 0.0310, 0.9061, 0.7041,
+                      0.8388, 0.2022, 0.8961, 0.9929, 0.7981, 0.4087, 0.5336,
+                      0.1071, 0.4185, 0.4326, 0.1289, 0.6766, 0.0293, 0.3061,
+                      0.0095, 0.2011, 0.1571, 0.7405, 0.5313, 0.7308, 0.3716,
+                      0.4635, 0.9587, 0.8976, 0.2622, 0.9705, 0.8033, 0.2844,
+                      0.0305, 0.3661, 0.4222, 0.5149, 0.3785]),
        size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
-tensor([0.5944, 0.7034, 0.9760,  ..., 0.8304, 0.5842, 0.4500])
+tensor([0.4029, 0.7352, 0.6155,  ..., 0.7243, 0.5046, 0.3375])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -317,13 +344,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 250
 Density: 1e-05
-Time: 10.499107599258423 seconds
+Time: 10.475499391555786 seconds
 
-[18.49, 21.77, 18.79, 18.12, 18.06, 18.03, 18.09, 17.74, 18.25, 17.89]
-[72.8]
-13.888601303100586
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 357325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.499107599258423, 'TIME_S_1KI': 0.029382516194664303, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1011.0901748657226, 'W': 72.8}
-[18.49, 21.77, 18.79, 18.12, 18.06, 18.03, 18.09, 17.74, 18.25, 17.89, 37.95, 41.51, 40.96, 43.46, 47.23, 47.25, 47.57, 33.36, 24.72, 27.91]
-526.03
-26.301499999999997
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 357325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.499107599258423, 'TIME_S_1KI': 0.029382516194664303, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1011.0901748657226, 'W': 72.8, 'J_1KI': 2.829609388835717, 'W_1KI': 0.20373609459175818, 'W_D': 46.4985, 'J_D': 645.7991276922226, 'W_D_1KI': 0.1301294339886658, 'J_D_1KI': 0.0003641766850588842}
+[18.92, 18.43, 18.51, 18.83, 18.51, 18.54, 18.52, 18.62, 18.72, 18.39]
+[81.49]
+13.97811222076416
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 362766, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.475499391555786, 'TIME_S_1KI': 0.028876739803498083, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1139.0763648700713, 'W': 81.49}
+[18.92, 18.43, 18.51, 18.83, 18.51, 18.54, 18.52, 18.62, 18.72, 18.39, 18.92, 18.79, 18.57, 18.36, 18.52, 18.48, 18.7, 18.31, 18.45, 19.09]
+334.52
+16.726
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 362766, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.475499391555786, 'TIME_S_1KI': 0.028876739803498083, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1139.0763648700713, 'W': 81.49, 'J_1KI': 3.1399755348353247, 'W_1KI': 0.22463516426566987, 'W_D': 64.764, 'J_D': 905.27845986557, 'W_D_1KI': 0.17852830750401083, 'J_D_1KI': 0.0004921307606115536}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.json
index e1d6586..f0d7169 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.json
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.json
@@ -1 +1 @@
-{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 344337, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.209988117218018, "TIME_S_1KI": 0.029651150231366417, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 996.8449161434174, "W": 73.42, "J_1KI": 2.894968929111357, "W_1KI": 0.21322135001466586, "W_D": 47.158, "J_D": 640.2780244550705, "W_D_1KI": 0.13695304309441042, "J_D_1KI": 0.0003977296749823877}
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 345167, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.38567328453064, "TIME_S_1KI": 0.030088836083781587, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1109.5468463230131, "W": 81.24, "J_1KI": 3.2145218005284777, "W_1KI": 0.23536433088910583, "W_D": 63.900749999999995, "J_D": 872.7335750883221, "W_D_1KI": 0.18512995158865128, "J_D_1KI": 0.000536348931353957}
diff --git a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.output
index 395a17f..2c66565 100644
--- a/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.output
+++ b/pytorch/output_synthetic_maxcore/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.output
@@ -1,13 +1,13 @@
 ['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.019530296325683594}
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.018830299377441406}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1250, 1250]),
-       col_indices=tensor([2184, 3902, 2442,  ..., 1965, 3430, 1316]),
-       values=tensor([0.1298, 0.8618, 0.5661,  ..., 0.6807, 0.5041, 0.2985]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([3576,  214, 2738,  ..., 1757, 4161, 1906]),
+       values=tensor([0.7403, 0.6577, 0.1056,  ..., 0.5544, 0.8169, 0.7972]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.7563, 0.3154, 0.3739,  ..., 0.6493, 0.1597, 0.7226])
+tensor([0.7748, 0.7223, 0.4756,  ..., 0.9335, 0.0436, 0.9545])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -15,18 +15,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 0.019530296325683594 seconds
+Time: 0.018830299377441406 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53762', '-ss', '5000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.6393845081329346}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '55761', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.696251392364502}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1248, 1249, 1250]),
-       col_indices=tensor([ 543, 3603,  176,  ..., 1860, 3976,  394]),
-       values=tensor([0.3999, 0.3952, 0.5467,  ..., 0.7650, 0.4806, 0.1331]),
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([2614, 1565, 3194,  ..., 1351, 1971, 4664]),
+       values=tensor([0.6203, 0.6869, 0.7061,  ..., 0.5399, 0.9629, 0.8026]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.3582, 0.6649, 0.9739,  ..., 0.1792, 0.8232, 0.7194])
+tensor([0.2679, 0.2756, 0.7180,  ..., 0.6222, 0.5676, 0.0842])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -34,18 +34,18 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 1.6393845081329346 seconds
+Time: 1.696251392364502 seconds
 
-['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '344337', '-ss', '5000', '-sd', '5e-05']
-{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.209988117218018}
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '345167', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.38567328453064}
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1250, 1250]),
-       col_indices=tensor([2469, 1957, 4805,  ...,  986, 4084, 3397]),
-       values=tensor([0.1203, 0.9340, 0.5005,  ..., 0.1600, 0.9840, 0.6585]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([2243, 2702, 4050,  ..., 3862, 1966, 3967]),
+       values=tensor([0.8868, 0.5024, 0.1535,  ..., 0.5829, 0.5937, 0.1171]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.7772, 0.5114, 0.7654,  ..., 0.7246, 0.7942, 0.6121])
+tensor([0.0483, 0.4917, 0.7447,  ..., 0.0369, 0.7186, 0.5491])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -53,15 +53,15 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.209988117218018 seconds
+Time: 10.38567328453064 seconds
 
 /nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
   matrix = matrix.to_sparse_csr().type(torch.float32)
-tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1250, 1250]),
-       col_indices=tensor([2469, 1957, 4805,  ...,  986, 4084, 3397]),
-       values=tensor([0.1203, 0.9340, 0.5005,  ..., 0.1600, 0.9840, 0.6585]),
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([2243, 2702, 4050,  ..., 3862, 1966, 3967]),
+       values=tensor([0.8868, 0.5024, 0.1535,  ..., 0.5829, 0.5937, 0.1171]),
        size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
-tensor([0.7772, 0.5114, 0.7654,  ..., 0.7246, 0.7942, 0.6121])
+tensor([0.0483, 0.4917, 0.7447,  ..., 0.0369, 0.7186, 0.5491])
 Matrix Type: synthetic
 Matrix Format: csr
 Shape: torch.Size([5000, 5000])
@@ -69,13 +69,13 @@ Rows: 5000
 Size: 25000000
 NNZ: 1250
 Density: 5e-05
-Time: 10.209988117218018 seconds
+Time: 10.38567328453064 seconds
 
-[40.04, 39.64, 39.87, 39.84, 41.87, 42.36, 39.49, 37.87, 39.91, 39.97]
-[73.42]
-13.577293872833252
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 344337, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.209988117218018, 'TIME_S_1KI': 0.029651150231366417, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 996.8449161434174, 'W': 73.42}
-[40.04, 39.64, 39.87, 39.84, 41.87, 42.36, 39.49, 37.87, 39.91, 39.97, 18.46, 17.91, 18.96, 18.18, 18.6, 18.04, 18.09, 18.2, 18.22, 17.91]
-525.24
-26.262
-{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 344337, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.209988117218018, 'TIME_S_1KI': 0.029651150231366417, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 996.8449161434174, 'W': 73.42, 'J_1KI': 2.894968929111357, 'W_1KI': 0.21322135001466586, 'W_D': 47.158, 'J_D': 640.2780244550705, 'W_D_1KI': 0.13695304309441042, 'J_D_1KI': 0.0003977296749823877}
+[18.8, 18.54, 18.53, 18.71, 18.61, 18.52, 22.53, 18.9, 18.79, 18.82]
+[81.24]
+13.657642126083374
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 345167, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.38567328453064, 'TIME_S_1KI': 0.030088836083781587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1109.5468463230131, 'W': 81.24}
+[18.8, 18.54, 18.53, 18.71, 18.61, 18.52, 22.53, 18.9, 18.79, 18.82, 19.04, 18.76, 22.81, 20.34, 18.63, 19.47, 18.63, 18.72, 18.67, 18.59]
+346.78499999999997
+17.33925
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 345167, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.38567328453064, 'TIME_S_1KI': 0.030088836083781587, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1109.5468463230131, 'W': 81.24, 'J_1KI': 3.2145218005284777, 'W_1KI': 0.23536433088910583, 'W_D': 63.900749999999995, 'J_D': 872.7335750883221, 'W_D_1KI': 0.18512995158865128, 'J_D_1KI': 0.000536348931353957}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..b76a936
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 690, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.003246307373047, "TIME_S_1KI": 21.74383522807688, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 736.2026811027528, "W": 51.180846035126734, "J_1KI": 1066.960407395294, "W_1KI": 74.1751391813431, "W_D": 35.27984603512673, "J_D": 507.4772937932015, "W_D_1KI": 51.1302116451112, "J_D_1KI": 74.10175600740754}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..175c45d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.520033359527588}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      21,  ...,  999972,
+                             999987, 1000000]),
+       col_indices=tensor([ 5448,  9227, 13530,  ..., 69235, 78462, 82074]),
+       values=tensor([0.3723, 0.4802, 0.5484,  ..., 0.8279, 0.7827, 0.0235]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9343, 0.5585, 0.5996,  ..., 0.1233, 0.3217, 0.3044])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 1.520033359527588 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 690 -ss 100000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 15.003246307373047}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      11,      20,  ...,  999982,
+                             999992, 1000000]),
+       col_indices=tensor([  122,  3109, 10697,  ..., 57820, 90383, 91253]),
+       values=tensor([0.9003, 0.9806, 0.3302,  ..., 0.9034, 0.0249, 0.7877]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6863, 0.9695, 0.8316,  ..., 0.5738, 0.3295, 0.1413])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 15.003246307373047 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      11,      20,  ...,  999982,
+                             999992, 1000000]),
+       col_indices=tensor([  122,  3109, 10697,  ..., 57820, 90383, 91253]),
+       values=tensor([0.9003, 0.9806, 0.3302,  ..., 0.9034, 0.0249, 0.7877]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6863, 0.9695, 0.8316,  ..., 0.5738, 0.3295, 0.1413])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 15.003246307373047 seconds
+
+[17.56, 17.56, 17.72, 17.72, 17.72, 17.92, 17.92, 17.64, 17.48, 17.56]
+[17.24, 17.28, 18.12, 18.12, 20.28, 29.36, 44.04, 63.04, 74.68, 91.04, 91.28, 91.4, 88.68, 90.76]
+14.384339809417725
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 690, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.003246307373047, 'TIME_S_1KI': 21.74383522807688, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 736.2026811027528, 'W': 51.180846035126734}
+[17.56, 17.56, 17.72, 17.72, 17.72, 17.92, 17.92, 17.64, 17.48, 17.56, 17.84, 17.84, 17.6, 17.92, 17.8, 17.8, 17.68, 17.52, 17.16, 17.08]
+318.02000000000004
+15.901000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 690, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 15.003246307373047, 'TIME_S_1KI': 21.74383522807688, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 736.2026811027528, 'W': 51.180846035126734, 'J_1KI': 1066.960407395294, 'W_1KI': 74.1751391813431, 'W_D': 35.27984603512673, 'J_D': 507.4772937932015, 'W_D_1KI': 51.1302116451112, 'J_D_1KI': 74.10175600740754}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..a46b44b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 14.453001976013184, "TIME_S_1KI": 144.53001976013184, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1049.84748960495, "W": 50.98554654857658, "J_1KI": 10498.4748960495, "W_1KI": 509.85546548576576, "W_D": 35.17954654857658, "J_D": 724.3848723731041, "W_D_1KI": 351.7954654857658, "J_D_1KI": 3517.954654857658}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..f3d22b6
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 14.453001976013184}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      101,      216,  ...,  9999796,
+                             9999906, 10000000]),
+       col_indices=tensor([ 2079,  2370,  2404,  ..., 91560, 92604, 94393]),
+       values=tensor([0.9041, 0.3243, 0.4250,  ..., 0.8376, 0.1046, 0.5896]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3984, 0.6112, 0.9728,  ..., 0.2358, 0.4123, 0.2200])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 14.453001976013184 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      101,      216,  ...,  9999796,
+                             9999906, 10000000]),
+       col_indices=tensor([ 2079,  2370,  2404,  ..., 91560, 92604, 94393]),
+       values=tensor([0.9041, 0.3243, 0.4250,  ..., 0.8376, 0.1046, 0.5896]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.3984, 0.6112, 0.9728,  ..., 0.2358, 0.4123, 0.2200])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 14.453001976013184 seconds
+
+[17.32, 17.28, 17.28, 17.36, 17.64, 17.72, 17.68, 17.92, 17.88, 17.72]
+[17.76, 17.96, 18.52, 20.36, 21.52, 25.36, 25.36, 31.2, 31.76, 45.88, 58.28, 65.88, 77.32, 84.48, 83.76, 84.4, 84.16, 84.96, 85.8, 86.16]
+20.5910804271698
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 14.453001976013184, 'TIME_S_1KI': 144.53001976013184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.84748960495, 'W': 50.98554654857658}
+[17.32, 17.28, 17.28, 17.36, 17.64, 17.72, 17.68, 17.92, 17.88, 17.72, 17.16, 17.24, 17.32, 17.32, 17.44, 17.8, 17.76, 17.76, 17.72, 17.8]
+316.12
+15.806000000000001
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 14.453001976013184, 'TIME_S_1KI': 144.53001976013184, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.84748960495, 'W': 50.98554654857658, 'J_1KI': 10498.4748960495, 'W_1KI': 509.85546548576576, 'W_D': 35.17954654857658, 'J_D': 724.3848723731041, 'W_D_1KI': 351.7954654857658, 'J_D_1KI': 3517.954654857658}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..c8834ef
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4862, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 16.123133897781372, "TIME_S_1KI": 3.316152591069801, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1103.8810626983643, "W": 59.41987873604025, "J_1KI": 227.04258796757802, "W_1KI": 12.221283162492854, "W_D": 43.51287873604025, "J_D": 808.3665574879647, "W_D_1KI": 8.949584273146904, "J_D_1KI": 1.8407207472535796}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..7c14ca6
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.3613910675048828}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([27177, 91140, 35351,  ..., 36842, 63353, 40213]),
+       values=tensor([0.8085, 0.0220, 0.0238,  ..., 0.9528, 0.8072, 0.8356]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5173, 0.6337, 0.4770,  ..., 0.8095, 0.3804, 0.7829])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.3613910675048828 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 2905 -ss 100000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.272582054138184}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99996,  99997,
+                            100000]),
+       col_indices=tensor([57428, 26674, 73957,  ..., 55311, 85675, 87326]),
+       values=tensor([0.1133, 0.1214, 0.0575,  ..., 0.3604, 0.8021, 0.3274]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7862, 0.8717, 0.9240,  ..., 0.8758, 0.8236, 0.0748])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 6.272582054138184 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4862 -ss 100000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 16.123133897781372}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      3,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([36017, 48507, 97216,  ..., 44545, 10809,  6488]),
+       values=tensor([0.2196, 0.1379, 0.1607,  ..., 0.1720, 0.9833, 0.1649]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6606, 0.9945, 0.6366,  ..., 0.7352, 0.9827, 0.2989])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 16.123133897781372 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      3,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([36017, 48507, 97216,  ..., 44545, 10809,  6488]),
+       values=tensor([0.2196, 0.1379, 0.1607,  ..., 0.1720, 0.9833, 0.1649]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6606, 0.9945, 0.6366,  ..., 0.7352, 0.9827, 0.2989])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 16.123133897781372 seconds
+
+[17.72, 17.76, 17.68, 17.88, 17.72, 17.52, 17.6, 17.48, 17.6, 17.6]
+[17.72, 17.72, 17.8, 21.08, 22.76, 35.32, 49.56, 66.44, 76.68, 89.56, 88.0, 87.44, 86.52, 85.84, 85.76, 86.6, 86.04, 85.28]
+18.57763910293579
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4862, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 16.123133897781372, 'TIME_S_1KI': 3.316152591069801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1103.8810626983643, 'W': 59.41987873604025}
+[17.72, 17.76, 17.68, 17.88, 17.72, 17.52, 17.6, 17.48, 17.6, 17.6, 17.8, 17.8, 17.76, 17.68, 17.76, 17.88, 17.72, 17.48, 17.52, 17.48]
+318.14
+15.907
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4862, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 16.123133897781372, 'TIME_S_1KI': 3.316152591069801, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1103.8810626983643, 'W': 59.41987873604025, 'J_1KI': 227.04258796757802, 'W_1KI': 12.221283162492854, 'W_D': 43.51287873604025, 'J_D': 808.3665574879647, 'W_D_1KI': 8.949584273146904, 'J_D_1KI': 1.8407207472535796}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..0831cb8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1295, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 13.363306522369385, "TIME_S_1KI": 10.319155615729255, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1148.1477067947387, "W": 58.62479671787461, "J_1KI": 886.600545787443, "W_1KI": 45.27011329565607, "W_D": 42.857796717874606, "J_D": 839.3561048357486, "W_D_1KI": 33.09482372036649, "J_D_1KI": 25.55584843271544}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..fe6c43d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 100000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.8107287883758545}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     12,  ..., 499988, 499996,
+                            500000]),
+       col_indices=tensor([ 2924,  5581, 32898,  ..., 25573, 35176, 44980]),
+       values=tensor([0.4338, 0.2090, 0.8667,  ..., 0.7968, 0.4161, 0.0285]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.7311, 0.8750, 0.4611,  ..., 0.2473, 0.3600, 0.7684])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 0.8107287883758545 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1295 -ss 100000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 13.363306522369385}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 499992, 499997,
+                            500000]),
+       col_indices=tensor([20312, 45798, 57469,  ...,  9915, 72511, 98823]),
+       values=tensor([0.2450, 0.9842, 0.2161,  ..., 0.8948, 0.9103, 0.6478]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.2448, 0.6389, 0.3272,  ..., 0.3843, 0.8480, 0.1017])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 13.363306522369385 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 499992, 499997,
+                            500000]),
+       col_indices=tensor([20312, 45798, 57469,  ...,  9915, 72511, 98823]),
+       values=tensor([0.2450, 0.9842, 0.2161,  ..., 0.8948, 0.9103, 0.6478]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.2448, 0.6389, 0.3272,  ..., 0.3843, 0.8480, 0.1017])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 13.363306522369385 seconds
+
+[17.48, 17.6, 17.56, 17.72, 17.52, 17.48, 17.56, 17.6, 17.6, 17.8]
+[18.0, 17.84, 17.76, 18.88, 19.6, 35.4, 49.36, 64.12, 77.28, 86.8, 84.84, 85.28, 83.64, 83.64, 82.96, 82.4, 81.96, 80.44, 80.16]
+19.58467698097229
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1295, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 13.363306522369385, 'TIME_S_1KI': 10.319155615729255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1148.1477067947387, 'W': 58.62479671787461}
+[17.48, 17.6, 17.56, 17.72, 17.52, 17.48, 17.56, 17.6, 17.6, 17.8, 17.64, 17.44, 17.36, 17.52, 17.36, 17.2, 17.52, 17.44, 17.6, 17.6]
+315.34
+15.767
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1295, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 13.363306522369385, 'TIME_S_1KI': 10.319155615729255, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1148.1477067947387, 'W': 58.62479671787461, 'J_1KI': 886.600545787443, 'W_1KI': 45.27011329565607, 'W_D': 42.857796717874606, 'J_D': 839.3561048357486, 'W_D_1KI': 33.09482372036649, 'J_D_1KI': 25.55584843271544}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..30708d9
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 33188, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.848255395889282, "TIME_S_1KI": 0.3268728274041606, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 283.2797647285461, "W": 19.966696619139725, "J_1KI": 8.535608193580394, "W_1KI": 0.6016239791231688, "W_D": 4.899696619139723, "J_D": 69.51499950075144, "W_D_1KI": 0.14763458536638915, "J_D_1KI": 0.0044484327276843785}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..ce5f5e4
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.039971351623535156}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([5709, 5957, 5382,  ..., 8260, 3428, 9778]),
+       values=tensor([0.1410, 0.8327, 0.5618,  ..., 0.6127, 0.7950, 0.7693]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.9182, 0.7141, 0.2679,  ..., 0.7671, 0.9231, 0.7252])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.039971351623535156 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 26268 -ss 10000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.310471773147583}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([6011, 6260, 4075,  ..., 5576, 1824, 8975]),
+       values=tensor([0.9641, 0.0766, 0.9967,  ..., 0.9539, 0.3769, 0.8002]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.0022, 0.8912, 0.7670,  ..., 0.3905, 0.8453, 0.1961])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 8.310471773147583 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 33188 -ss 10000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.848255395889282}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     4,  ...,  9995,  9997, 10000]),
+       col_indices=tensor([1948, 7195, 8876,  ...,  111, 6612, 9607]),
+       values=tensor([0.9860, 0.8888, 0.2852,  ..., 0.2300, 0.3266, 0.6773]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.8303, 0.1186, 0.7718,  ..., 0.9103, 0.1807, 0.5186])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.848255395889282 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     4,  ...,  9995,  9997, 10000]),
+       col_indices=tensor([1948, 7195, 8876,  ...,  111, 6612, 9607]),
+       values=tensor([0.9860, 0.8888, 0.2852,  ..., 0.2300, 0.3266, 0.6773]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.8303, 0.1186, 0.7718,  ..., 0.9103, 0.1807, 0.5186])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.848255395889282 seconds
+
+[17.16, 17.24, 17.28, 17.28, 17.12, 16.8, 16.4, 16.48, 16.32, 16.6]
+[16.76, 16.84, 20.04, 22.12, 23.96, 24.76, 24.76, 25.32, 22.2, 20.84, 19.88, 20.16, 20.24, 20.04]
+14.187613010406494
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 33188, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.848255395889282, 'TIME_S_1KI': 0.3268728274041606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 283.2797647285461, 'W': 19.966696619139725}
+[17.16, 17.24, 17.28, 17.28, 17.12, 16.8, 16.4, 16.48, 16.32, 16.6, 16.72, 16.48, 16.8, 16.64, 16.64, 16.68, 16.68, 16.56, 16.44, 16.52]
+301.34000000000003
+15.067000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 33188, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.848255395889282, 'TIME_S_1KI': 0.3268728274041606, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 283.2797647285461, 'W': 19.966696619139725, 'J_1KI': 8.535608193580394, 'W_1KI': 0.6016239791231688, 'W_D': 4.899696619139723, 'J_D': 69.51499950075144, 'W_D_1KI': 0.14763458536638915, 'J_D_1KI': 0.0044484327276843785}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..9b3c069
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 4682, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.500732183456421, "TIME_S_1KI": 2.2427877367484883, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 298.49395805358887, "W": 21.02945948615313, "J_1KI": 63.753515175905356, "W_1KI": 4.491554781322753, "W_D": 5.974459486153128, "J_D": 84.80199219703674, "W_D_1KI": 1.2760485873885365, "J_D_1KI": 0.27254348299627007}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..faf5980
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.2965726852416992}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     10,     21,  ...,  99978,  99991,
+                            100000]),
+       col_indices=tensor([ 670, 2215, 2340,  ..., 5626, 6766, 7426]),
+       values=tensor([0.8454, 0.9971, 0.8602,  ..., 0.7286, 0.6853, 0.9650]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8368, 0.0517, 0.8880,  ..., 0.1583, 0.3761, 0.3465])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.2965726852416992 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3540 -ss 10000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 7.9380598068237305}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      9,     19,  ...,  99981,  99990,
+                            100000]),
+       col_indices=tensor([ 218, 1968, 1988,  ..., 6580, 8417, 9626]),
+       values=tensor([0.6495, 0.9774, 0.8878,  ..., 0.7618, 0.8151, 0.2290]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.1753, 0.6932, 0.8031,  ..., 0.2090, 0.4929, 0.1708])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 7.9380598068237305 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4682 -ss 10000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.500732183456421}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     20,  ...,  99977,  99983,
+                            100000]),
+       col_indices=tensor([  32,  224, 1507,  ..., 8865, 9626, 9660]),
+       values=tensor([0.3062, 0.4385, 0.1947,  ..., 0.8116, 0.9937, 0.3510]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0637, 0.2734, 0.9054,  ..., 0.1369, 0.8787, 0.2539])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.500732183456421 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     20,  ...,  99977,  99983,
+                            100000]),
+       col_indices=tensor([  32,  224, 1507,  ..., 8865, 9626, 9660]),
+       values=tensor([0.3062, 0.4385, 0.1947,  ..., 0.8116, 0.9937, 0.3510]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0637, 0.2734, 0.9054,  ..., 0.1369, 0.8787, 0.2539])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.500732183456421 seconds
+
+[16.56, 16.28, 16.36, 16.32, 16.4, 16.32, 16.36, 16.44, 16.36, 16.28]
+[16.24, 15.96, 19.32, 19.32, 21.08, 28.0, 29.28, 30.04, 27.12, 25.96, 20.44, 20.04, 19.96, 19.96]
+14.194086074829102
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.500732183456421, 'TIME_S_1KI': 2.2427877367484883, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 298.49395805358887, 'W': 21.02945948615313}
+[16.56, 16.28, 16.36, 16.32, 16.4, 16.32, 16.36, 16.44, 16.36, 16.28, 16.52, 16.8, 17.04, 17.04, 17.16, 17.48, 17.4, 17.4, 16.84, 16.84]
+301.1
+15.055000000000001
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 4682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.500732183456421, 'TIME_S_1KI': 2.2427877367484883, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 298.49395805358887, 'W': 21.02945948615313, 'J_1KI': 63.753515175905356, 'W_1KI': 4.491554781322753, 'W_D': 5.974459486153128, 'J_D': 84.80199219703674, 'W_D_1KI': 1.2760485873885365, 'J_D_1KI': 0.27254348299627007}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..b135810
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 481, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.463550806045532, "TIME_S_1KI": 21.75374387951254, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 332.4283616256714, "W": 23.410421194984455, "J_1KI": 691.1192549390258, "W_1KI": 48.67031433468701, "W_D": 6.792421194984453, "J_D": 96.45249141454698, "W_D_1KI": 14.121457785830463, "J_D_1KI": 29.35854009528163}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..bf59b23
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.1813583374023438}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     110,     205,  ...,  999812,
+                             999909, 1000000]),
+       col_indices=tensor([ 238,  361,  384,  ..., 9612, 9634, 9698]),
+       values=tensor([0.5750, 0.8151, 0.3516,  ..., 0.9302, 0.8544, 0.3311]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.6330, 0.0586, 0.5281,  ..., 0.1634, 0.9727, 0.9265])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 2.1813583374023438 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 481 -ss 10000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.463550806045532}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      92,     212,  ...,  999818,
+                             999919, 1000000]),
+       col_indices=tensor([ 159,  190,  205,  ..., 9628, 9649, 9961]),
+       values=tensor([0.6162, 0.4289, 0.4486,  ..., 0.9461, 0.6549, 0.6632]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8230, 0.2220, 0.9348,  ..., 0.2779, 0.8915, 0.3439])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.463550806045532 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      92,     212,  ...,  999818,
+                             999919, 1000000]),
+       col_indices=tensor([ 159,  190,  205,  ..., 9628, 9649, 9961]),
+       values=tensor([0.6162, 0.4289, 0.4486,  ..., 0.9461, 0.6549, 0.6632]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8230, 0.2220, 0.9348,  ..., 0.2779, 0.8915, 0.3439])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.463550806045532 seconds
+
+[20.24, 19.08, 17.84, 18.08, 19.12, 19.88, 20.84, 22.4, 22.4, 22.88]
+[22.68, 22.68, 21.44, 22.2, 23.24, 31.92, 32.08, 31.76, 30.52, 23.92, 21.88, 21.88, 22.0, 22.24]
+14.200016260147095
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 481, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.463550806045532, 'TIME_S_1KI': 21.75374387951254, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.4283616256714, 'W': 23.410421194984455}
+[20.24, 19.08, 17.84, 18.08, 19.12, 19.88, 20.84, 22.4, 22.4, 22.88, 16.72, 17.0, 17.08, 16.92, 17.04, 16.84, 16.64, 16.56, 16.56, 16.32]
+332.36
+16.618000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 481, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.463550806045532, 'TIME_S_1KI': 21.75374387951254, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 332.4283616256714, 'W': 23.410421194984455, 'J_1KI': 691.1192549390258, 'W_1KI': 48.67031433468701, 'W_D': 6.792421194984453, 'J_D': 96.45249141454698, 'W_D_1KI': 14.121457785830463, 'J_D_1KI': 29.35854009528163}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..18a5242
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 11.54654312133789, "TIME_S_1KI": 115.4654312133789, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 353.263671875, "W": 21.71035974754772, "J_1KI": 3532.63671875, "W_1KI": 217.1035974754772, "W_D": 6.712359747547719, "J_D": 109.22126021575927, "W_D_1KI": 67.1235974754772, "J_D_1KI": 671.235974754772}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..d49136b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 11.54654312133789}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     487,     979,  ..., 4998949,
+                            4999474, 5000000]),
+       col_indices=tensor([   0,    2,    9,  ..., 9960, 9969, 9987]),
+       values=tensor([0.4699, 0.5377, 0.9444,  ..., 0.8781, 0.2092, 0.8180]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7212, 0.1994, 0.8974,  ..., 0.2164, 0.6888, 0.0461])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 11.54654312133789 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     487,     979,  ..., 4998949,
+                            4999474, 5000000]),
+       col_indices=tensor([   0,    2,    9,  ..., 9960, 9969, 9987]),
+       values=tensor([0.4699, 0.5377, 0.9444,  ..., 0.8781, 0.2092, 0.8180]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7212, 0.1994, 0.8974,  ..., 0.2164, 0.6888, 0.0461])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 11.54654312133789 seconds
+
+[16.56, 16.48, 16.72, 16.72, 16.8, 16.8, 16.64, 16.52, 16.52, 16.48]
+[16.44, 16.64, 17.04, 18.24, 19.64, 27.0, 30.2, 30.0, 30.0, 30.08, 26.08, 22.8, 20.4, 20.48, 20.64, 20.48]
+16.271663665771484
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 11.54654312133789, 'TIME_S_1KI': 115.4654312133789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 353.263671875, 'W': 21.71035974754772}
+[16.56, 16.48, 16.72, 16.72, 16.8, 16.8, 16.64, 16.52, 16.52, 16.48, 16.52, 16.36, 16.6, 16.84, 16.68, 16.68, 16.68, 16.96, 16.76, 16.84]
+299.96000000000004
+14.998000000000001
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 11.54654312133789, 'TIME_S_1KI': 115.4654312133789, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 353.263671875, 'W': 21.71035974754772, 'J_1KI': 3532.63671875, 'W_1KI': 217.1035974754772, 'W_D': 6.712359747547719, 'J_D': 109.22126021575927, 'W_D_1KI': 67.1235974754772, 'J_D_1KI': 671.235974754772}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..94990e6
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.129345178604126, "TIME_S_1KI": 211.29345178604126, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 619.1563564968109, "W": 21.836067076186225, "J_1KI": 6191.563564968109, "W_1KI": 218.36067076186225, "W_D": 6.581067076186224, "J_D": 186.6045519340038, "W_D_1KI": 65.81067076186224, "J_D_1KI": 658.1067076186224}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..12caf81
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 21.129345178604126}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      944,     1951,  ...,  9997982,
+                             9998990, 10000000]),
+       col_indices=tensor([  12,   17,   18,  ..., 9973, 9979, 9988]),
+       values=tensor([0.0454, 0.8748, 0.1892,  ..., 0.1417, 0.8974, 0.6226]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7518, 0.8666, 0.4972,  ..., 0.4338, 0.0856, 0.8852])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 21.129345178604126 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      944,     1951,  ...,  9997982,
+                             9998990, 10000000]),
+       col_indices=tensor([  12,   17,   18,  ..., 9973, 9979, 9988]),
+       values=tensor([0.0454, 0.8748, 0.1892,  ..., 0.1417, 0.8974, 0.6226]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.7518, 0.8666, 0.4972,  ..., 0.4338, 0.0856, 0.8852])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 21.129345178604126 seconds
+
+[16.8, 16.76, 16.76, 16.76, 16.88, 16.92, 16.92, 17.0, 17.04, 17.04]
+[16.88, 17.04, 20.52, 21.6, 23.68, 23.68, 29.08, 30.56, 30.32, 30.08, 28.48, 23.96, 23.32, 20.92, 20.92, 21.08, 21.44, 21.44, 20.92, 20.72, 20.64, 20.4, 20.4, 20.56, 20.44, 20.36, 20.48, 20.68]
+28.354756116867065
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.129345178604126, 'TIME_S_1KI': 211.29345178604126, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 619.1563564968109, 'W': 21.836067076186225}
+[16.8, 16.76, 16.76, 16.76, 16.88, 16.92, 16.92, 17.0, 17.04, 17.04, 17.12, 17.28, 17.24, 17.24, 16.84, 16.64, 16.96, 16.72, 17.12, 17.08]
+305.1
+15.255
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 21.129345178604126, 'TIME_S_1KI': 211.29345178604126, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 619.1563564968109, 'W': 21.836067076186225, 'J_1KI': 6191.563564968109, 'W_1KI': 218.36067076186225, 'W_D': 6.581067076186224, 'J_D': 186.6045519340038, 'W_D_1KI': 65.81067076186224, 'J_D_1KI': 658.1067076186224}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..364c8ea
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.021928787231445, "TIME_S_1KI": 420.21928787231445, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1136.679587249756, "W": 21.603080734566593, "J_1KI": 11366.79587249756, "W_1KI": 216.03080734566592, "W_D": 6.324080734566593, "J_D": 332.751312992096, "W_D_1KI": 63.24080734566592, "J_D_1KI": 632.4080734566592}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..b23f94c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 42.021928787231445}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1977,     4005,  ..., 19996092,
+                            19998061, 20000000]),
+       col_indices=tensor([   0,   21,   29,  ..., 9987, 9990, 9994]),
+       values=tensor([0.7851, 0.1514, 0.2063,  ..., 0.8497, 0.4761, 0.4899]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.7703, 0.2799, 0.3874,  ..., 0.1897, 0.4899, 0.7472])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 42.021928787231445 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1977,     4005,  ..., 19996092,
+                            19998061, 20000000]),
+       col_indices=tensor([   0,   21,   29,  ..., 9987, 9990, 9994]),
+       values=tensor([0.7851, 0.1514, 0.2063,  ..., 0.8497, 0.4761, 0.4899]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.7703, 0.2799, 0.3874,  ..., 0.1897, 0.4899, 0.7472])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 42.021928787231445 seconds
+
+[16.84, 16.68, 16.68, 16.88, 16.96, 17.08, 17.12, 17.24, 17.0, 17.0]
+[16.76, 16.8, 19.84, 20.68, 22.88, 22.88, 24.36, 33.76, 31.92, 32.48, 31.68, 31.64, 25.4, 24.92, 23.64, 20.2, 20.04, 20.04, 20.04, 19.84, 20.12, 20.36, 20.72, 20.76, 20.48, 20.32, 20.4, 20.28, 20.76, 20.84, 20.84, 20.92, 21.0, 20.96, 20.8, 20.8, 20.84, 20.6, 20.56, 20.56, 20.84, 20.76, 20.76, 20.8, 21.0, 21.08, 20.8, 20.96, 20.72, 20.72, 20.72, 20.76]
+52.61655044555664
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.021928787231445, 'TIME_S_1KI': 420.21928787231445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1136.679587249756, 'W': 21.603080734566593}
+[16.84, 16.68, 16.68, 16.88, 16.96, 17.08, 17.12, 17.24, 17.0, 17.0, 17.2, 17.16, 17.08, 16.84, 16.84, 16.8, 17.0, 17.04, 17.16, 17.0]
+305.58
+15.279
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 42.021928787231445, 'TIME_S_1KI': 420.21928787231445, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1136.679587249756, 'W': 21.603080734566593, 'J_1KI': 11366.79587249756, 'W_1KI': 216.03080734566592, 'W_D': 6.324080734566593, 'J_D': 332.751312992096, 'W_D_1KI': 63.24080734566592, 'J_D_1KI': 632.4080734566592}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..f2ba0ed
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 63.62427043914795, "TIME_S_1KI": 636.2427043914795, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1670.8393529319762, "W": 21.725499941161722, "J_1KI": 16708.393529319765, "W_1KI": 217.2549994116172, "W_D": 6.518499941161723, "J_D": 501.3171735184193, "W_D_1KI": 65.18499941161723, "J_D_1KI": 651.8499941161723}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..6258d91
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 63.62427043914795}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2940,     5882,  ..., 29993962,
+                            29997042, 30000000]),
+       col_indices=tensor([   8,   14,   15,  ..., 9977, 9993, 9996]),
+       values=tensor([0.8256, 0.2654, 0.0882,  ..., 0.9659, 0.5243, 0.2720]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.4780, 0.6736, 0.3887,  ..., 0.7294, 0.7408, 0.5543])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 63.62427043914795 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2940,     5882,  ..., 29993962,
+                            29997042, 30000000]),
+       col_indices=tensor([   8,   14,   15,  ..., 9977, 9993, 9996]),
+       values=tensor([0.8256, 0.2654, 0.0882,  ..., 0.9659, 0.5243, 0.2720]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.4780, 0.6736, 0.3887,  ..., 0.7294, 0.7408, 0.5543])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 63.62427043914795 seconds
+
+[16.52, 16.44, 16.52, 16.76, 16.76, 16.68, 17.12, 16.6, 16.6, 16.64]
+[16.76, 16.68, 16.6, 20.16, 21.48, 23.48, 24.68, 31.48, 34.68, 34.64, 35.08, 35.08, 34.68, 24.92, 25.84, 25.44, 24.52, 23.52, 20.92, 20.76, 20.76, 20.88, 20.6, 20.8, 20.8, 20.96, 20.88, 20.6, 20.8, 20.56, 20.44, 20.52, 20.48, 20.8, 20.56, 20.6, 20.6, 20.68, 20.72, 20.68, 20.88, 20.92, 20.8, 20.8, 20.52, 20.56, 20.68, 20.96, 20.8, 20.8, 20.92, 20.8, 20.56, 20.68, 20.8, 20.72, 20.8, 21.12, 21.24, 21.16, 21.12, 21.12, 21.04, 20.6, 20.36, 20.2, 20.4, 20.48, 20.52, 20.88, 20.64, 20.32, 20.4, 20.4, 20.4, 20.4]
+76.90683102607727
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 63.62427043914795, 'TIME_S_1KI': 636.2427043914795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1670.8393529319762, 'W': 21.725499941161722}
+[16.52, 16.44, 16.52, 16.76, 16.76, 16.68, 17.12, 16.6, 16.6, 16.64, 16.32, 16.72, 16.92, 17.2, 17.32, 17.52, 17.16, 17.24, 17.28, 17.12]
+304.14
+15.206999999999999
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 63.62427043914795, 'TIME_S_1KI': 636.2427043914795, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1670.8393529319762, 'W': 21.725499941161722, 'J_1KI': 16708.393529319765, 'W_1KI': 217.2549994116172, 'W_D': 6.518499941161723, 'J_D': 501.3171735184193, 'W_D_1KI': 65.18499941161723, 'J_D_1KI': 651.8499941161723}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..66821f5
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 141552, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.798607587814331, "TIME_S_1KI": 0.07628721309352274, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 269.76709407806396, "W": 18.985490730093364, "J_1KI": 1.9057808725985077, "W_1KI": 0.13412379005661076, "W_D": 4.025490730093365, "J_D": 57.198676185607916, "W_D_1KI": 0.028438246934648505, "J_D_1KI": 0.0002009031799949736}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..2de78b8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1200 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.015316009521484375}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([9112, 5292, 3575, 5961, 5092, 9328, 2019, 8963, 6545,
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+                      0.4603, 0.7024, 0.4804, 0.7634, 0.3067, 0.6368]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8762, 0.1751, 0.9607,  ..., 0.4579, 0.7370, 0.8447])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.015316009521484375 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 68555 -ss 10000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.085230827331543}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([5549, 4311, 3241, 5542, 7620, 1988, 9532, 4491, 7500,
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+                      3.5891e-01, 7.6267e-02, 8.8689e-01, 1.9321e-01,
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+                      5.8028e-01, 9.4390e-01, 1.7118e-01, 7.7669e-01,
+                      1.5969e-01, 1.8190e-01, 7.3046e-01, 7.7660e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4288, 0.6749, 0.2273,  ..., 0.2100, 0.8485, 0.9690])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 5.085230827331543 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 141552 -ss 10000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.798607587814331}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.3464, 0.1206, 0.8110, 0.0102, 0.2242, 0.3161]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0219, 0.9207, 0.3669,  ..., 0.7955, 0.2670, 0.3543])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.798607587814331 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.3464, 0.1206, 0.8110, 0.0102, 0.2242, 0.3161]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.0219, 0.9207, 0.3669,  ..., 0.7955, 0.2670, 0.3543])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.798607587814331 seconds
+
+[16.32, 16.4, 16.24, 16.76, 16.72, 17.12, 17.44, 17.24, 17.36, 17.12]
+[16.8, 16.56, 16.56, 19.68, 21.72, 23.52, 24.24, 24.8, 20.88, 20.0, 19.92, 19.92, 19.64, 19.64]
+14.209118843078613
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 141552, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.798607587814331, 'TIME_S_1KI': 0.07628721309352274, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 269.76709407806396, 'W': 18.985490730093364}
+[16.32, 16.4, 16.24, 16.76, 16.72, 17.12, 17.44, 17.24, 17.36, 17.12, 16.28, 16.24, 16.24, 16.36, 16.6, 16.4, 16.24, 16.4, 16.44, 16.28]
+299.2
+14.959999999999999
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 141552, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.798607587814331, 'TIME_S_1KI': 0.07628721309352274, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 269.76709407806396, 'W': 18.985490730093364, 'J_1KI': 1.9057808725985077, 'W_1KI': 0.13412379005661076, 'W_D': 4.025490730093365, 'J_D': 57.198676185607916, 'W_D_1KI': 0.028438246934648505, 'J_D_1KI': 0.0002009031799949736}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..ccc8254
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 52454, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.6396803855896, "TIME_S_1KI": 0.20283830376309908, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 254.19103302001952, "W": 17.900584259593533, "J_1KI": 4.845979963778158, "W_1KI": 0.3412625206770415, "W_D": 2.811584259593534, "J_D": 39.92492630434038, "W_D_1KI": 0.05360095053939707, "J_D_1KI": 0.0010218658355777839}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..6f0e024
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 10000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.02812814712524414}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 4998, 4999, 5000]),
+       col_indices=tensor([4541, 9541, 3383,  ..., 9920, 3344, 2731]),
+       values=tensor([0.3320, 0.1825, 0.5042,  ..., 0.6612, 0.1900, 0.5121]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.0144, 0.2553, 0.6494,  ..., 0.0787, 0.9201, 0.6475])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.02812814712524414 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 37329 -ss 10000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 7.472296953201294}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 4999, 5000]),
+       col_indices=tensor([6333, 2190, 7526,  ..., 2226, 3084, 9881]),
+       values=tensor([0.4590, 0.1089, 0.5094,  ..., 0.8341, 0.9457, 0.0387]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.3900, 0.6084, 0.4843,  ..., 0.7689, 0.5332, 0.9837])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 7.472296953201294 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 52454 -ss 10000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.6396803855896}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([2165, 7276,  636,  ..., 1970, 2680, 2527]),
+       values=tensor([0.0210, 0.6731, 0.7347,  ..., 0.2518, 0.3264, 0.9787]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.5715, 0.5141, 0.9224,  ..., 0.0062, 0.8236, 0.9187])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.6396803855896 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([2165, 7276,  636,  ..., 1970, 2680, 2527]),
+       values=tensor([0.0210, 0.6731, 0.7347,  ..., 0.2518, 0.3264, 0.9787]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.5715, 0.5141, 0.9224,  ..., 0.0062, 0.8236, 0.9187])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.6396803855896 seconds
+
+[16.64, 16.48, 16.64, 16.84, 17.0, 16.8, 16.72, 16.4, 16.4, 16.2]
+[16.28, 16.44, 16.8, 17.88, 19.0, 19.88, 20.6, 20.92, 20.48, 20.0, 20.12, 20.12, 19.92, 19.84]
+14.20015287399292
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 52454, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.6396803855896, 'TIME_S_1KI': 0.20283830376309908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 254.19103302001952, 'W': 17.900584259593533}
+[16.64, 16.48, 16.64, 16.84, 17.0, 16.8, 16.72, 16.4, 16.4, 16.2, 16.72, 16.92, 16.8, 16.88, 17.04, 17.16, 16.96, 16.8, 16.84, 16.64]
+301.78
+15.088999999999999
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 52454, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.6396803855896, 'TIME_S_1KI': 0.20283830376309908, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 254.19103302001952, 'W': 17.900584259593533, 'J_1KI': 4.845979963778158, 'W_1KI': 0.3412625206770415, 'W_D': 2.811584259593534, 'J_D': 39.92492630434038, 'W_D_1KI': 0.05360095053939707, 'J_D_1KI': 0.0010218658355777839}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..2f5c509
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.72194480895996, "TIME_S_1KI": 227.2194480895996, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2080.890781612396, "W": 61.453409935621025, "J_1KI": 20808.90781612396, "W_1KI": 614.5340993562103, "W_D": 45.112409935621024, "J_D": 1527.5636953213213, "W_D_1KI": 451.12409935621025, "J_D_1KI": 4511.240993562103}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..4a89b40
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 22.72194480895996}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       56,      116,  ..., 24999900,
+                            24999953, 25000000]),
+       col_indices=tensor([   647,   4700,  33413,  ..., 445020, 463377,
+                           482076]),
+       values=tensor([0.2494, 0.9199, 0.9974,  ..., 0.2647, 0.3316, 0.8056]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.1920, 0.0118, 0.3050,  ..., 0.0609, 0.1776, 0.7503])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 22.72194480895996 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       56,      116,  ..., 24999900,
+                            24999953, 25000000]),
+       col_indices=tensor([   647,   4700,  33413,  ..., 445020, 463377,
+                           482076]),
+       values=tensor([0.2494, 0.9199, 0.9974,  ..., 0.2647, 0.3316, 0.8056]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.1920, 0.0118, 0.3050,  ..., 0.0609, 0.1776, 0.7503])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 22.72194480895996 seconds
+
+[17.96, 18.12, 17.96, 17.96, 17.88, 17.96, 17.88, 17.92, 17.76, 17.8]
+[17.8, 17.6, 18.48, 20.36, 21.4, 22.72, 22.72, 24.08, 25.12, 34.04, 34.44, 33.88, 34.28, 32.56, 45.4, 59.36, 77.8, 92.4, 98.2, 98.2, 95.48, 95.96, 96.12, 95.0, 93.72, 96.32, 96.24, 93.28, 93.88, 93.4, 91.28, 91.4, 93.2]
+33.8612744808197
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.72194480895996, 'TIME_S_1KI': 227.2194480895996, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2080.890781612396, 'W': 61.453409935621025}
+[17.96, 18.12, 17.96, 17.96, 17.88, 17.96, 17.88, 17.92, 17.76, 17.8, 17.72, 18.0, 18.44, 18.6, 18.6, 18.64, 18.76, 18.48, 18.16, 17.92]
+326.82000000000005
+16.341
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 22.72194480895996, 'TIME_S_1KI': 227.2194480895996, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2080.890781612396, 'W': 61.453409935621025, 'J_1KI': 20808.90781612396, 'W_1KI': 614.5340993562103, 'W_D': 45.112409935621024, 'J_D': 1527.5636953213213, 'W_D_1KI': 451.12409935621025, 'J_D_1KI': 4511.240993562103}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..208d33f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 279, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 12.429174900054932, "TIME_S_1KI": 44.549013978691505, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 925.7997315406801, "W": 55.99377793769148, "J_1KI": 3318.2786076726884, "W_1KI": 200.69454457953935, "W_D": 40.002777937691484, "J_D": 661.4049353270533, "W_D_1KI": 143.37913239315944, "J_D_1KI": 513.9037003339048}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..2fab080
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,110 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 4.175914764404297}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,       9,  ..., 2499991,
+                            2499995, 2500000]),
+       col_indices=tensor([236491, 268930, 282894,  ..., 290854, 362096,
+                           428990]),
+       values=tensor([0.4942, 0.2006, 0.6461,  ..., 0.6339, 0.7923, 0.0061]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6272, 0.4782, 0.6613,  ..., 0.5722, 0.7323, 0.6099])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 4.175914764404297 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 251 -ss 500000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.986790895462036}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       3,       4,  ..., 2499995,
+                            2499998, 2500000]),
+       col_indices=tensor([168226, 184311, 332682,  ..., 175948,  40749,
+                           152556]),
+       values=tensor([0.8367, 0.0584, 0.1423,  ..., 0.1509, 0.1566, 0.6036]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8910, 0.6965, 0.0939,  ..., 0.1566, 0.5700, 0.8005])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 9.986790895462036 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 263 -ss 500000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.86619520187378}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       1,       4,  ..., 2499987,
+                            2499997, 2500000]),
+       col_indices=tensor([120869, 339930, 358219,  ...,  35981,  71933,
+                           400518]),
+       values=tensor([0.0243, 0.7300, 0.4495,  ..., 0.8433, 0.9453, 0.9296]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0560, 0.8217, 0.0541,  ..., 0.5269, 0.7792, 0.2112])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 9.86619520187378 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 279 -ss 500000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 12.429174900054932}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       4,  ..., 2499986,
+                            2499991, 2500000]),
+       col_indices=tensor([  4708,  62252, 239037,  ..., 346193, 443276,
+                           467019]),
+       values=tensor([0.7542, 0.0207, 0.5398,  ..., 0.0649, 0.4673, 0.8331]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8471, 0.4897, 0.4001,  ..., 0.3407, 0.6143, 0.4869])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 12.429174900054932 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       4,  ..., 2499986,
+                            2499991, 2500000]),
+       col_indices=tensor([  4708,  62252, 239037,  ..., 346193, 443276,
+                           467019]),
+       values=tensor([0.7542, 0.0207, 0.5398,  ..., 0.0649, 0.4673, 0.8331]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8471, 0.4897, 0.4001,  ..., 0.3407, 0.6143, 0.4869])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 12.429174900054932 seconds
+
+[17.72, 17.72, 17.84, 17.8, 17.8, 18.04, 17.92, 17.8, 17.72, 17.32]
+[17.36, 17.64, 18.04, 20.2, 21.44, 35.64, 48.92, 66.32, 77.96, 77.96, 91.04, 90.76, 89.6, 87.56, 86.4, 84.76]
+16.53397512435913
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 279, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 12.429174900054932, 'TIME_S_1KI': 44.549013978691505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 925.7997315406801, 'W': 55.99377793769148}
+[17.72, 17.72, 17.84, 17.8, 17.8, 18.04, 17.92, 17.8, 17.72, 17.32, 17.84, 17.72, 17.8, 17.92, 17.8, 17.72, 17.6, 17.72, 17.72, 17.48]
+319.82
+15.991
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 279, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 12.429174900054932, 'TIME_S_1KI': 44.549013978691505, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 925.7997315406801, 'W': 55.99377793769148, 'J_1KI': 3318.2786076726884, 'W_1KI': 200.69454457953935, 'W_D': 40.002777937691484, 'J_D': 661.4049353270533, 'W_D_1KI': 143.37913239315944, 'J_D_1KI': 513.9037003339048}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..fd07ea0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 16.55699610710144, "TIME_S_1KI": 165.5699610710144, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1413.1975269508362, "W": 57.485721407839506, "J_1KI": 14131.97526950836, "W_1KI": 574.857214078395, "W_D": 41.59072140783951, "J_D": 1022.44354246974, "W_D_1KI": 415.9072140783951, "J_D_1KI": 4159.072140783951}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..33e7f40
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,47 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 500000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 16.55699610710144}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       27,       44,  ..., 12499945,
+                            12499971, 12500000]),
+       col_indices=tensor([  7086,  20899,  31000,  ..., 441979, 480995,
+                           482795]),
+       values=tensor([0.3494, 0.1791, 0.5321,  ..., 0.0256, 0.8127, 0.6614]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9181, 0.2128, 0.6316,  ..., 0.6360, 0.7946, 0.3835])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 16.55699610710144 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       27,       44,  ..., 12499945,
+                            12499971, 12500000]),
+       col_indices=tensor([  7086,  20899,  31000,  ..., 441979, 480995,
+                           482795]),
+       values=tensor([0.3494, 0.1791, 0.5321,  ..., 0.0256, 0.8127, 0.6614]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9181, 0.2128, 0.6316,  ..., 0.6360, 0.7946, 0.3835])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 16.55699610710144 seconds
+
+[17.52, 17.52, 17.88, 17.84, 17.84, 17.64, 17.48, 17.72, 17.44, 17.28]
+[17.4, 17.16, 20.64, 21.36, 23.44, 25.32, 33.76, 33.76, 31.56, 35.12, 47.32, 60.6, 67.04, 80.92, 86.0, 85.72, 87.24, 88.0, 90.8, 92.64, 93.72, 92.0, 92.0, 90.8]
+24.583452939987183
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 16.55699610710144, 'TIME_S_1KI': 165.5699610710144, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1413.1975269508362, 'W': 57.485721407839506}
+[17.52, 17.52, 17.88, 17.84, 17.84, 17.64, 17.48, 17.72, 17.44, 17.28, 18.0, 17.76, 17.6, 17.64, 17.44, 17.4, 17.8, 17.88, 17.76, 17.72]
+317.9
+15.895
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 16.55699610710144, 'TIME_S_1KI': 165.5699610710144, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1413.1975269508362, 'W': 57.485721407839506, 'J_1KI': 14131.97526950836, 'W_1KI': 574.857214078395, 'W_D': 41.59072140783951, 'J_D': 1022.44354246974, 'W_D_1KI': 415.9072140783951, 'J_D_1KI': 4159.072140783951}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..9346872
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1658, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 14.466236591339111, "TIME_S_1KI": 8.725112540011526, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1691.1909151077273, "W": 65.36355506616682, "J_1KI": 1020.0186460239609, "W_1KI": 39.423133333031856, "W_D": 49.293555066166824, "J_D": 1275.40205573082, "W_D_1KI": 29.730732850522816, "J_D_1KI": 17.93168446955538}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..686a0dc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.8603906631469727}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     11,  ..., 249990, 249996,
+                            250000]),
+       col_indices=tensor([  116,  2220,  2597,  ..., 31423, 34504, 36695]),
+       values=tensor([0.0356, 0.7526, 0.0114,  ..., 0.2051, 0.2717, 0.4326]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.6822, 0.2973, 0.4245,  ..., 0.4266, 0.4462, 0.3842])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.8603906631469727 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1220 -ss 50000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.725424766540527}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,     12,  ..., 249990, 249995,
+                            250000]),
+       col_indices=tensor([ 1339, 17035, 23748,  ..., 19329, 30492, 33219]),
+       values=tensor([0.1487, 0.4152, 0.3651,  ..., 0.6580, 0.7478, 0.7026]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4336, 0.1383, 0.7901,  ..., 0.8031, 0.7630, 0.0295])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 7.725424766540527 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1658 -ss 50000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 14.466236591339111}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 249989, 249992,
+                            250000]),
+       col_indices=tensor([14878, 23641, 26434,  ..., 39221, 43609, 44125]),
+       values=tensor([0.9348, 0.1734, 0.7472,  ..., 0.0129, 0.0523, 0.4218]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4659, 0.1302, 0.1589,  ..., 0.1214, 0.1279, 0.7413])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 14.466236591339111 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,      7,  ..., 249989, 249992,
+                            250000]),
+       col_indices=tensor([14878, 23641, 26434,  ..., 39221, 43609, 44125]),
+       values=tensor([0.9348, 0.1734, 0.7472,  ..., 0.0129, 0.0523, 0.4218]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4659, 0.1302, 0.1589,  ..., 0.1214, 0.1279, 0.7413])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 14.466236591339111 seconds
+
+[18.12, 18.08, 18.12, 18.12, 18.08, 18.0, 17.76, 17.64, 17.48, 17.48]
+[17.28, 17.4, 17.28, 19.64, 20.44, 35.08, 49.0, 64.92, 77.28, 86.8, 86.8, 86.52, 85.52, 84.64, 84.32, 84.0, 84.16, 84.16, 84.24, 84.24, 84.04, 84.28, 82.92, 82.4, 81.88]
+25.87360668182373
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1658, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 14.466236591339111, 'TIME_S_1KI': 8.725112540011526, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1691.1909151077273, 'W': 65.36355506616682}
+[18.12, 18.08, 18.12, 18.12, 18.08, 18.0, 17.76, 17.64, 17.48, 17.48, 18.04, 17.84, 17.8, 17.64, 17.76, 17.92, 17.8, 17.8, 17.72, 18.04]
+321.4
+16.07
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1658, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 14.466236591339111, 'TIME_S_1KI': 8.725112540011526, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1691.1909151077273, 'W': 65.36355506616682, 'J_1KI': 1020.0186460239609, 'W_1KI': 39.423133333031856, 'W_D': 49.293555066166824, 'J_D': 1275.40205573082, 'W_D_1KI': 29.730732850522816, 'J_D_1KI': 17.93168446955538}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..c314337
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 135, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.963478088378906, "TIME_S_1KI": 81.21094880280671, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 582.8568821525573, "W": 43.83509814272925, "J_1KI": 4317.458386315239, "W_1KI": 324.7044306868833, "W_D": 28.06209814272925, "J_D": 373.12992837095254, "W_D_1KI": 207.8673936498463, "J_D_1KI": 1539.758471480343}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..514e8a4
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.26869797706604}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      44,     102,  ..., 2499900,
+                            2499955, 2500000]),
+       col_indices=tensor([  878,  3105,  3271,  ..., 44510, 45389, 45985]),
+       values=tensor([0.2421, 0.0051, 0.2486,  ..., 0.1294, 0.9249, 0.4412]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3218, 0.5110, 0.5510,  ..., 0.0407, 0.3623, 0.3415])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 8.26869797706604 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 126 -ss 50000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.729291677474976}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      44,     101,  ..., 2499906,
+                            2499957, 2500000]),
+       col_indices=tensor([  430,  2871,  2934,  ..., 46471, 47392, 47877]),
+       values=tensor([0.4189, 0.2667, 0.2640,  ..., 0.7329, 0.4126, 0.0437]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6895, 0.8779, 0.8508,  ..., 0.5330, 0.3990, 0.7739])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 9.729291677474976 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 135 -ss 50000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.963478088378906}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      51,     106,  ..., 2499884,
+                            2499945, 2500000]),
+       col_indices=tensor([ 1316,  2608,  2921,  ..., 47281, 49169, 49691]),
+       values=tensor([0.1237, 0.8262, 0.6046,  ..., 0.7531, 0.6389, 0.8086]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9516, 0.0943, 0.1293,  ..., 0.9488, 0.5626, 0.5458])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.963478088378906 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      51,     106,  ..., 2499884,
+                            2499945, 2500000]),
+       col_indices=tensor([ 1316,  2608,  2921,  ..., 47281, 49169, 49691]),
+       values=tensor([0.1237, 0.8262, 0.6046,  ..., 0.7531, 0.6389, 0.8086]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.9516, 0.0943, 0.1293,  ..., 0.9488, 0.5626, 0.5458])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.963478088378906 seconds
+
+[17.96, 17.88, 17.56, 17.44, 17.36, 17.24, 17.44, 17.68, 17.68, 17.68]
+[17.88, 17.88, 17.88, 22.04, 23.04, 29.76, 42.2, 54.16, 64.96, 76.44, 81.04, 80.44, 80.44]
+13.296579837799072
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 135, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.963478088378906, 'TIME_S_1KI': 81.21094880280671, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 582.8568821525573, 'W': 43.83509814272925}
+[17.96, 17.88, 17.56, 17.44, 17.36, 17.24, 17.44, 17.68, 17.68, 17.68, 17.84, 17.6, 17.48, 17.48, 17.52, 17.32, 17.4, 17.36, 17.44, 17.68]
+315.46
+15.773
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 135, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.963478088378906, 'TIME_S_1KI': 81.21094880280671, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 582.8568821525573, 'W': 43.83509814272925, 'J_1KI': 4317.458386315239, 'W_1KI': 324.7044306868833, 'W_D': 28.06209814272925, 'J_D': 373.12992837095254, 'W_D_1KI': 207.8673936498463, 'J_D_1KI': 1539.758471480343}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..de9236a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 30.498249053955078, "TIME_S_1KI": 304.9824905395508, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2007.0959778976435, "W": 47.944381683758614, "J_1KI": 20070.959778976434, "W_1KI": 479.44381683758616, "W_D": 32.291381683758615, "J_D": 1351.8143319842811, "W_D_1KI": 322.9138168375861, "J_D_1KI": 3229.138168375861}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..9e427ef
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 30.498249053955078}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      519,     1009,  ..., 24999042,
+                            24999488, 25000000]),
+       col_indices=tensor([  129,   342,   437,  ..., 49566, 49630, 49865]),
+       values=tensor([0.8700, 0.4704, 0.0527,  ..., 0.4978, 0.7115, 0.5319]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3352, 0.1102, 0.4216,  ..., 0.1668, 0.6074, 0.9924])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 30.498249053955078 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      519,     1009,  ..., 24999042,
+                            24999488, 25000000]),
+       col_indices=tensor([  129,   342,   437,  ..., 49566, 49630, 49865]),
+       values=tensor([0.8700, 0.4704, 0.0527,  ..., 0.4978, 0.7115, 0.5319]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3352, 0.1102, 0.4216,  ..., 0.1668, 0.6074, 0.9924])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 30.498249053955078 seconds
+
+[17.68, 17.6, 17.56, 17.16, 17.16, 17.0, 17.0, 17.08, 17.36, 17.4]
+[17.52, 17.72, 18.04, 18.88, 20.68, 21.64, 23.68, 24.04, 31.04, 33.36, 33.36, 32.8, 32.72, 32.52, 28.16, 34.64, 41.2, 49.36, 57.8, 58.4, 58.92, 62.72, 62.72, 61.04, 61.04, 59.8, 60.88, 61.56, 64.04, 67.12, 70.76, 68.32, 68.92, 65.8, 66.72, 65.88, 65.84, 68.88, 68.88, 67.08, 67.04]
+41.863006830215454
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 30.498249053955078, 'TIME_S_1KI': 304.9824905395508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2007.0959778976435, 'W': 47.944381683758614}
+[17.68, 17.6, 17.56, 17.16, 17.16, 17.0, 17.0, 17.08, 17.36, 17.4, 17.56, 17.4, 17.44, 17.44, 17.4, 17.6, 17.56, 17.52, 17.6, 17.72]
+313.05999999999995
+15.652999999999997
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 30.498249053955078, 'TIME_S_1KI': 304.9824905395508, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2007.0959778976435, 'W': 47.944381683758614, 'J_1KI': 20070.959778976434, 'W_1KI': 479.44381683758616, 'W_D': 32.291381683758615, 'J_D': 1351.8143319842811, 'W_D_1KI': 322.9138168375861, 'J_D_1KI': 3229.138168375861}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..5f4cac5
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 8811, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 14.461963891983032, "TIME_S_1KI": 1.6413532961052129, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1601.2496216583254, "W": 63.889106877708535, "J_1KI": 181.7330180068466, "W_1KI": 7.251061954115144, "W_D": 47.822106877708535, "J_D": 1198.5631712055208, "W_D_1KI": 5.4275458946440285, "J_D_1KI": 0.6159965832078116}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..6d049d2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,120 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.2421858310699463}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     2,     2,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([11801, 48673, 42443,  ..., 35599, 34008, 22453]),
+       values=tensor([0.3951, 0.6998, 0.6224,  ..., 0.4352, 0.5927, 0.1013]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6391, 0.6971, 0.7049,  ..., 0.7658, 0.2053, 0.9702])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.2421858310699463 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 4335 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.2435102462768555}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([48470, 32812, 24371,  ..., 21811,  5693, 27792]),
+       values=tensor([7.9337e-01, 8.6969e-01, 2.1228e-02,  ...,
+                      3.1628e-01, 5.2154e-01, 8.3659e-04]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5123, 0.3871, 0.5639,  ..., 0.2434, 0.7885, 0.9337])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 6.2435102462768555 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 7290 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.1812264919281}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([ 1561, 34915, 11685,  ...,  9985, 24943, 27218]),
+       values=tensor([0.3207, 0.5476, 0.7721,  ..., 0.5221, 0.2072, 0.7139]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7737, 0.6011, 0.2764,  ..., 0.4575, 0.9058, 0.1946])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 9.1812264919281 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 8337 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.935004234313965}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([ 4660, 37796, 36819,  ..., 14791,   855,   165]),
+       values=tensor([0.6560, 0.1119, 0.0106,  ..., 0.5425, 0.3178, 0.8843]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7672, 0.9593, 0.0238,  ..., 0.4054, 0.5730, 0.7422])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 9.935004234313965 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 8811 -ss 50000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 14.461963891983032}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24997, 24998, 25000]),
+       col_indices=tensor([ 8691, 13268, 31788,  ..., 20611,  3075,  9688]),
+       values=tensor([0.8778, 0.6640, 0.4350,  ..., 0.9614, 0.5782, 0.7592]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9817, 0.3893, 0.5308,  ..., 0.4402, 0.5461, 0.2337])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 14.461963891983032 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24997, 24998, 25000]),
+       col_indices=tensor([ 8691, 13268, 31788,  ..., 20611,  3075,  9688]),
+       values=tensor([0.8778, 0.6640, 0.4350,  ..., 0.9614, 0.5782, 0.7592]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9817, 0.3893, 0.5308,  ..., 0.4402, 0.5461, 0.2337])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 14.461963891983032 seconds
+
+[18.04, 17.96, 17.84, 17.68, 17.72, 17.8, 17.84, 17.92, 17.8, 17.8]
+[17.8, 17.64, 17.64, 18.72, 19.96, 31.88, 49.36, 63.92, 79.84, 91.32, 91.4, 91.0, 90.8, 90.72, 90.68, 90.12, 89.48, 88.76, 88.76, 87.32, 86.72, 85.68, 84.4, 82.12]
+25.062952041625977
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8811, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 14.461963891983032, 'TIME_S_1KI': 1.6413532961052129, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1601.2496216583254, 'W': 63.889106877708535}
+[18.04, 17.96, 17.84, 17.68, 17.72, 17.8, 17.84, 17.92, 17.8, 17.8, 18.0, 17.84, 17.72, 17.92, 17.96, 17.88, 18.12, 17.92, 17.68, 17.64]
+321.34000000000003
+16.067
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 8811, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 14.461963891983032, 'TIME_S_1KI': 1.6413532961052129, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1601.2496216583254, 'W': 63.889106877708535, 'J_1KI': 181.7330180068466, 'W_1KI': 7.251061954115144, 'W_D': 47.822106877708535, 'J_D': 1198.5631712055208, 'W_D_1KI': 5.4275458946440285, 'J_D_1KI': 0.6159965832078116}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..a856634
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 3016, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 12.238471984863281, "TIME_S_1KI": 4.057848801347242, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1021.0734484481814, "W": 58.16446532539802, "J_1KI": 338.5522043926331, "W_1KI": 19.285300174203588, "W_D": 42.31446532539802, "J_D": 742.827717702389, "W_D_1KI": 14.02999513441579, "J_D_1KI": 4.651855150668366}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..0ab6a55
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 50000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.37006235122680664}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      5,  ..., 124994, 124997,
+                            125000]),
+       col_indices=tensor([ 3192, 33329, 36206,  ..., 17521, 36763, 39198]),
+       values=tensor([0.7954, 0.1728, 0.6419,  ..., 0.6370, 0.0715, 0.4891]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.4965, 0.4773, 0.1313,  ..., 0.0503, 0.1495, 0.6552])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.37006235122680664 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 2837 -ss 50000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 9.87511658668518}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      4,  ..., 124994, 124997,
+                            125000]),
+       col_indices=tensor([18930, 21812, 35293,  ...,   949,  2935, 28377]),
+       values=tensor([0.1354, 0.7141, 0.1182,  ..., 0.8833, 0.5348, 0.3796]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.0699, 0.1291, 0.1295,  ..., 0.2384, 0.2084, 0.3934])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 9.87511658668518 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 3016 -ss 50000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 12.238471984863281}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      8,  ..., 124995, 124998,
+                            125000]),
+       col_indices=tensor([ 1947, 25944, 43942,  ..., 29833,   871, 28509]),
+       values=tensor([0.4807, 0.5814, 0.8403,  ..., 0.1650, 0.5150, 0.8001]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7842, 0.6693, 0.1792,  ..., 0.7791, 0.2472, 0.4723])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 12.238471984863281 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      3,      8,  ..., 124995, 124998,
+                            125000]),
+       col_indices=tensor([ 1947, 25944, 43942,  ..., 29833,   871, 28509]),
+       values=tensor([0.4807, 0.5814, 0.8403,  ..., 0.1650, 0.5150, 0.8001]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7842, 0.6693, 0.1792,  ..., 0.7791, 0.2472, 0.4723])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 12.238471984863281 seconds
+
+[17.56, 17.48, 17.4, 17.56, 17.72, 17.76, 17.48, 17.56, 17.56, 17.52]
+[17.52, 17.68, 17.76, 20.16, 20.96, 34.48, 49.64, 65.8, 77.8, 88.2, 87.72, 86.68, 86.76, 87.64, 87.64, 89.04, 89.44]
+17.554935693740845
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3016, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 12.238471984863281, 'TIME_S_1KI': 4.057848801347242, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1021.0734484481814, 'W': 58.16446532539802}
+[17.56, 17.48, 17.4, 17.56, 17.72, 17.76, 17.48, 17.56, 17.56, 17.52, 17.72, 17.84, 17.8, 17.68, 17.68, 17.52, 17.56, 17.6, 17.48, 17.84]
+317.0
+15.85
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 3016, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 12.238471984863281, 'TIME_S_1KI': 4.057848801347242, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1021.0734484481814, 'W': 58.16446532539802, 'J_1KI': 338.5522043926331, 'W_1KI': 19.285300174203588, 'W_D': 42.31446532539802, 'J_D': 742.827717702389, 'W_D_1KI': 14.02999513441579, 'J_D_1KI': 4.651855150668366}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..fa80299
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 93664, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.030004501342773, "TIME_S_1KI": 0.107084947272621, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 253.85587940216067, "W": 17.876929919933424, "J_1KI": 2.7102822792338643, "W_1KI": 0.19086233686297213, "W_D": 2.859929919933423, "J_D": 40.61156071567538, "W_D_1KI": 0.03053392893676784, "J_D_1KI": 0.00032599428741851555}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..c2bcc94
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.018899202346801758}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([1031, 4368, 4092,  ...,  190, 4399, 1962]),
+       values=tensor([0.5248, 0.7492, 0.4749,  ..., 0.9820, 0.4892, 0.6710]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7298, 0.5753, 0.5464,  ..., 0.2530, 0.5594, 0.0214])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.018899202346801758 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 55557 -ss 5000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 6.228086948394775}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([ 532, 4399, 2173,  ..., 2637, 3554, 2146]),
+       values=tensor([0.0459, 0.9731, 0.3457,  ..., 0.8215, 0.1549, 0.6550]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2796, 0.3928, 0.8109,  ..., 0.2089, 0.4148, 0.7694])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 6.228086948394775 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 93664 -ss 5000 -sd 0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.030004501342773}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2498, 2498, 2500]),
+       col_indices=tensor([1824, 1160, 4169,  ..., 4733, 1262, 4559]),
+       values=tensor([0.9986, 0.6087, 0.2000,  ..., 0.7208, 0.5140, 0.1151]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.6500, 0.1776, 0.6063,  ..., 0.2915, 0.3213, 0.4804])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.030004501342773 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2498, 2498, 2500]),
+       col_indices=tensor([1824, 1160, 4169,  ..., 4733, 1262, 4559]),
+       values=tensor([0.9986, 0.6087, 0.2000,  ..., 0.7208, 0.5140, 0.1151]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.6500, 0.1776, 0.6063,  ..., 0.2915, 0.3213, 0.4804])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.030004501342773 seconds
+
+[16.8, 16.8, 17.16, 17.24, 17.04, 16.96, 16.96, 17.0, 16.6, 17.0]
+[16.8, 16.84, 17.08, 18.24, 19.2, 19.72, 20.36, 20.2, 20.2, 20.04, 19.8, 19.8, 20.0, 19.96]
+14.200194358825684
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 93664, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.030004501342773, 'TIME_S_1KI': 0.107084947272621, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 253.85587940216067, 'W': 17.876929919933424}
+[16.8, 16.8, 17.16, 17.24, 17.04, 16.96, 16.96, 17.0, 16.6, 17.0, 16.72, 16.48, 16.44, 16.48, 16.32, 16.48, 16.4, 16.28, 16.28, 16.32]
+300.34000000000003
+15.017000000000001
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 93664, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.030004501342773, 'TIME_S_1KI': 0.107084947272621, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 253.85587940216067, 'W': 17.876929919933424, 'J_1KI': 2.7102822792338643, 'W_1KI': 0.19086233686297213, 'W_D': 2.859929919933423, 'J_D': 40.61156071567538, 'W_D_1KI': 0.03053392893676784, 'J_D_1KI': 0.00032599428741851555}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..37907ba
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 17878, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.553908824920654, "TIME_S_1KI": 0.5903293894686572, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 275.88499256134037, "W": 19.43945882245899, "J_1KI": 15.431535549912763, "W_1KI": 1.0873396813099334, "W_D": 4.574458822458993, "J_D": 64.92076501369485, "W_D_1KI": 0.2558708369201808, "J_D_1KI": 0.014312050392671485}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..e1fffa0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06726479530334473}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    11,    16,  ..., 24990, 24996, 25000]),
+       col_indices=tensor([ 219,  546,  972,  ..., 2610, 3216, 3318]),
+       values=tensor([0.2561, 0.1283, 0.3219,  ..., 0.1859, 0.9829, 0.7598]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7133, 0.2668, 0.2369,  ..., 0.2811, 0.0980, 0.8981])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.06726479530334473 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 15609 -ss 5000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.167168855667114}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     9,    17,  ..., 24994, 24998, 25000]),
+       col_indices=tensor([ 299, 1244, 1941,  ..., 4267,  280, 4025]),
+       values=tensor([0.2861, 0.6940, 0.5528,  ..., 0.3063, 0.0705, 0.3058]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.7288, 0.9023, 0.1050,  ..., 0.1276, 0.6415, 0.2460])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 9.167168855667114 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 17878 -ss 5000 -sd 0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.553908824920654}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     6,    13,  ..., 24995, 24998, 25000]),
+       col_indices=tensor([1009, 1198, 2341,  ..., 3808,  999, 4327]),
+       values=tensor([0.4127, 0.3151, 0.8058,  ..., 0.6562, 0.4614, 0.0831]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8967, 0.5216, 0.2765,  ..., 0.9189, 0.4761, 0.3303])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.553908824920654 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     6,    13,  ..., 24995, 24998, 25000]),
+       col_indices=tensor([1009, 1198, 2341,  ..., 3808,  999, 4327]),
+       values=tensor([0.4127, 0.3151, 0.8058,  ..., 0.6562, 0.4614, 0.0831]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8967, 0.5216, 0.2765,  ..., 0.9189, 0.4761, 0.3303])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.553908824920654 seconds
+
+[16.32, 16.36, 16.36, 16.56, 16.68, 16.68, 16.64, 16.56, 16.68, 16.52]
+[16.44, 16.88, 16.88, 17.92, 19.12, 25.32, 25.8, 25.92, 25.32, 22.16, 19.68, 19.6, 19.64, 19.4]
+14.192009925842285
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 17878, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.553908824920654, 'TIME_S_1KI': 0.5903293894686572, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 275.88499256134037, 'W': 19.43945882245899}
+[16.32, 16.36, 16.36, 16.56, 16.68, 16.68, 16.64, 16.56, 16.68, 16.52, 16.52, 16.4, 16.4, 16.2, 16.28, 16.36, 16.32, 16.64, 17.04, 16.92]
+297.29999999999995
+14.864999999999998
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 17878, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.553908824920654, 'TIME_S_1KI': 0.5903293894686572, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 275.88499256134037, 'W': 19.43945882245899, 'J_1KI': 15.431535549912763, 'W_1KI': 1.0873396813099334, 'W_D': 4.574458822458993, 'J_D': 64.92076501369485, 'W_D_1KI': 0.2558708369201808, 'J_D_1KI': 0.014312050392671485}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..b2ee0da
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 1934, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.419332265853882, "TIME_S_1KI": 5.38745205059663, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 296.42124423980715, "W": 20.84298720811279, "J_1KI": 153.26848202678755, "W_1KI": 10.777139197576416, "W_D": 5.978987208112789, "J_D": 85.03094157409672, "W_D_1KI": 3.0915135512475644, "J_D_1KI": 1.598507523912908}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..048fad0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.6361579895019531}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     45,     88,  ..., 249912, 249956,
+                            250000]),
+       col_indices=tensor([ 150,  155,  160,  ..., 4906, 4918, 4974]),
+       values=tensor([0.5565, 0.2611, 0.6011,  ..., 0.5545, 0.3341, 0.9118]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3996, 0.8652, 0.4997,  ..., 0.5638, 0.5133, 0.5074])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.6361579895019531 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1650 -ss 5000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 8.95723843574524}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     48,    105,  ..., 249902, 249944,
+                            250000]),
+       col_indices=tensor([ 142,  192,  269,  ..., 4444, 4647, 4854]),
+       values=tensor([0.2391, 0.6427, 0.3721,  ..., 0.7376, 0.6381, 0.7309]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0904, 0.1755, 0.9906,  ..., 0.2784, 0.3906, 0.1798])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 8.95723843574524 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 1934 -ss 5000 -sd 0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.419332265853882}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     67,    123,  ..., 249901, 249950,
+                            250000]),
+       col_indices=tensor([  77,  297,  304,  ..., 4744, 4962, 4980]),
+       values=tensor([0.1458, 0.7428, 0.5307,  ..., 0.5713, 0.0836, 0.9823]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8374, 0.5393, 0.4548,  ..., 0.1972, 0.5711, 0.3877])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.419332265853882 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     67,    123,  ..., 249901, 249950,
+                            250000]),
+       col_indices=tensor([  77,  297,  304,  ..., 4744, 4962, 4980]),
+       values=tensor([0.1458, 0.7428, 0.5307,  ..., 0.5713, 0.0836, 0.9823]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8374, 0.5393, 0.4548,  ..., 0.1972, 0.5711, 0.3877])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.419332265853882 seconds
+
+[16.44, 16.56, 16.48, 16.6, 16.72, 16.56, 16.72, 16.6, 16.72, 16.64]
+[16.6, 16.56, 16.56, 20.2, 20.72, 28.04, 28.92, 29.0, 26.44, 26.36, 20.12, 20.28, 20.2, 20.4]
+14.221629619598389
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1934, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.419332265853882, 'TIME_S_1KI': 5.38745205059663, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.42124423980715, 'W': 20.84298720811279}
+[16.44, 16.56, 16.48, 16.6, 16.72, 16.56, 16.72, 16.6, 16.72, 16.64, 16.68, 16.44, 16.44, 16.2, 16.24, 16.32, 16.56, 16.56, 16.52, 16.32]
+297.28
+14.863999999999999
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 1934, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.419332265853882, 'TIME_S_1KI': 5.38745205059663, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 296.42124423980715, 'W': 20.84298720811279, 'J_1KI': 153.26848202678755, 'W_1KI': 10.777139197576416, 'W_D': 5.978987208112789, 'J_D': 85.03094157409672, 'W_D_1KI': 3.0915135512475644, 'J_D_1KI': 1.598507523912908}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..e125259
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 394, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.532436847686768, "TIME_S_1KI": 26.732073217479105, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 305.06099658966065, "W": 21.37670924510136, "J_1KI": 774.2664888062453, "W_1KI": 54.25560722106944, "W_D": 6.54270924510136, "J_D": 93.36916079187395, "W_D_1KI": 16.60586102817604, "J_D_1KI": 42.146855401462034}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..49ea4fe
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 2.876847505569458}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     270,     507,  ..., 1249492,
+                            1249752, 1250000]),
+       col_indices=tensor([  22,   47,   49,  ..., 4884, 4921, 4983]),
+       values=tensor([0.5298, 0.6030, 0.6480,  ..., 0.1911, 0.5303, 0.8187]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.2222, 0.3805, 0.6044,  ..., 0.3111, 0.2543, 0.4407])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 2.876847505569458 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 364 -ss 5000 -sd 0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.686374425888062}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     236,     499,  ..., 1249504,
+                            1249745, 1250000]),
+       col_indices=tensor([  18,   50,   76,  ..., 4926, 4932, 4975]),
+       values=tensor([0.1676, 0.6835, 0.4526,  ..., 0.3904, 0.9402, 0.3969]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.5247, 0.4082, 0.5074,  ..., 0.5246, 0.7808, 0.6822])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 9.686374425888062 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 394 -ss 5000 -sd 0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.532436847686768}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     285,     546,  ..., 1249527,
+                            1249741, 1250000]),
+       col_indices=tensor([  14,   79,   87,  ..., 4936, 4956, 4998]),
+       values=tensor([0.7519, 0.7358, 0.0306,  ..., 0.8162, 0.7664, 0.2246]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.9515, 0.5215, 0.2042,  ..., 0.7715, 0.5250, 0.3133])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.532436847686768 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     285,     546,  ..., 1249527,
+                            1249741, 1250000]),
+       col_indices=tensor([  14,   79,   87,  ..., 4936, 4956, 4998]),
+       values=tensor([0.7519, 0.7358, 0.0306,  ..., 0.8162, 0.7664, 0.2246]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.9515, 0.5215, 0.2042,  ..., 0.7715, 0.5250, 0.3133])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.532436847686768 seconds
+
+[16.32, 16.28, 16.04, 16.2, 16.32, 16.36, 16.36, 16.36, 16.64, 16.6]
+[16.72, 16.84, 17.04, 21.76, 24.68, 30.6, 31.68, 29.24, 27.56, 20.44, 20.44, 20.32, 20.44, 20.32]
+14.270718336105347
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 394, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.532436847686768, 'TIME_S_1KI': 26.732073217479105, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 305.06099658966065, 'W': 21.37670924510136}
+[16.32, 16.28, 16.04, 16.2, 16.32, 16.36, 16.36, 16.36, 16.64, 16.6, 16.32, 16.36, 16.44, 16.6, 16.76, 16.8, 16.64, 16.76, 16.76, 16.76]
+296.68
+14.834
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 394, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.532436847686768, 'TIME_S_1KI': 26.732073217479105, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 305.06099658966065, 'W': 21.37670924510136, 'J_1KI': 774.2664888062453, 'W_1KI': 54.25560722106944, 'W_D': 6.54270924510136, 'J_D': 93.36916079187395, 'W_D_1KI': 16.60586102817604, 'J_D_1KI': 42.146855401462034}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..5879ae9
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 197, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.453055620193481, "TIME_S_1KI": 53.06119604159128, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 328.8498130035401, "W": 21.582740338257725, "J_1KI": 1669.2883908809142, "W_1KI": 109.55705755460775, "W_D": 6.5647403382577245, "J_D": 100.02500140476232, "W_D_1KI": 33.32355501653667, "J_D_1KI": 169.15510160678514}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..45da6ca
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 5.324424982070923}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     475,     961,  ..., 2499006,
+                            2499520, 2500000]),
+       col_indices=tensor([  15,   18,   19,  ..., 4987, 4990, 4996]),
+       values=tensor([0.8221, 0.3138, 0.3999,  ..., 0.4846, 0.5872, 0.2809]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6625, 0.6086, 0.7821,  ..., 0.8108, 0.2752, 0.8534])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 5.324424982070923 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 197 -ss 5000 -sd 0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.453055620193481}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     504,     990,  ..., 2498981,
+                            2499511, 2500000]),
+       col_indices=tensor([   3,   15,   19,  ..., 4959, 4975, 4978]),
+       values=tensor([0.1275, 0.4769, 0.3626,  ..., 0.0765, 0.7881, 0.0735]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.2182, 0.1034, 0.8832,  ..., 0.0679, 0.0105, 0.3546])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.453055620193481 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     504,     990,  ..., 2498981,
+                            2499511, 2500000]),
+       col_indices=tensor([   3,   15,   19,  ..., 4959, 4975, 4978]),
+       values=tensor([0.1275, 0.4769, 0.3626,  ..., 0.0765, 0.7881, 0.0735]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.2182, 0.1034, 0.8832,  ..., 0.0679, 0.0105, 0.3546])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.453055620193481 seconds
+
+[16.48, 16.56, 16.52, 16.6, 16.92, 17.2, 17.24, 17.32, 17.32, 17.2]
+[17.2, 17.08, 16.8, 20.08, 22.0, 28.44, 31.8, 32.0, 28.2, 27.28, 20.16, 20.32, 20.56, 20.56, 20.32]
+15.236703395843506
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.453055620193481, 'TIME_S_1KI': 53.06119604159128, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.8498130035401, 'W': 21.582740338257725}
+[16.48, 16.56, 16.52, 16.6, 16.92, 17.2, 17.24, 17.32, 17.32, 17.2, 16.8, 16.52, 16.44, 16.56, 16.44, 16.36, 16.4, 16.48, 16.24, 16.0]
+300.36
+15.018
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.453055620193481, 'TIME_S_1KI': 53.06119604159128, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 328.8498130035401, 'W': 21.582740338257725, 'J_1KI': 1669.2883908809142, 'W_1KI': 109.55705755460775, 'W_D': 6.5647403382577245, 'J_D': 100.02500140476232, 'W_D_1KI': 33.32355501653667, 'J_D_1KI': 169.15510160678514}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..22c3547
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.520986080169678, "TIME_S_1KI": 105.20986080169678, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 343.0604390716553, "W": 22.467293864380782, "J_1KI": 3430.604390716553, "W_1KI": 224.67293864380784, "W_D": 7.879293864380783, "J_D": 120.3115083198548, "W_D_1KI": 78.79293864380782, "J_D_1KI": 787.9293864380782}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..46cc854
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.520986080169678}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1010,    1992,  ..., 4998032,
+                            4999035, 5000000]),
+       col_indices=tensor([   7,   20,   27,  ..., 4987, 4995, 4999]),
+       values=tensor([0.6859, 0.1805, 0.1498,  ..., 0.8538, 0.5250, 0.8232]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6117, 0.2257, 0.9695,  ..., 0.2383, 0.1591, 0.7207])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.520986080169678 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1010,    1992,  ..., 4998032,
+                            4999035, 5000000]),
+       col_indices=tensor([   7,   20,   27,  ..., 4987, 4995, 4999]),
+       values=tensor([0.6859, 0.1805, 0.1498,  ..., 0.8538, 0.5250, 0.8232]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6117, 0.2257, 0.9695,  ..., 0.2383, 0.1591, 0.7207])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.520986080169678 seconds
+
+[16.36, 16.04, 16.16, 15.96, 15.88, 15.88, 16.04, 16.04, 16.28, 16.32]
+[16.4, 16.48, 16.8, 20.24, 21.88, 29.72, 32.92, 30.52, 30.52, 30.68, 27.2, 20.4, 20.72, 20.84, 20.72]
+15.269326210021973
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.520986080169678, 'TIME_S_1KI': 105.20986080169678, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 343.0604390716553, 'W': 22.467293864380782}
+[16.36, 16.04, 16.16, 15.96, 15.88, 15.88, 16.04, 16.04, 16.28, 16.32, 16.84, 17.08, 16.68, 16.36, 16.32, 16.0, 16.08, 16.08, 16.04, 16.16]
+291.76
+14.588
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.520986080169678, 'TIME_S_1KI': 105.20986080169678, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 343.0604390716553, 'W': 22.467293864380782, 'J_1KI': 3430.604390716553, 'W_1KI': 224.67293864380784, 'W_D': 7.879293864380783, 'J_D': 120.3115083198548, 'W_D_1KI': 78.79293864380782, 'J_D_1KI': 787.9293864380782}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..1e920c1
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 16.019237279891968, "TIME_S_1KI": 160.19237279891968, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 445.7399830436707, "W": 20.90802632853554, "J_1KI": 4457.399830436707, "W_1KI": 209.08026328535541, "W_D": 5.958026328535542, "J_D": 127.0196676111222, "W_D_1KI": 59.58026328535542, "J_D_1KI": 595.8026328535542}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..039e7ba
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 16.019237279891968}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1526,    3031,  ..., 7497025,
+                            7498559, 7500000]),
+       col_indices=tensor([   0,    3,    7,  ..., 4995, 4996, 4999]),
+       values=tensor([0.4727, 0.1556, 0.1081,  ..., 0.9285, 0.0937, 0.3872]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.9962, 0.8284, 0.0086,  ..., 0.9887, 0.5066, 0.4274])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 16.019237279891968 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1526,    3031,  ..., 7497025,
+                            7498559, 7500000]),
+       col_indices=tensor([   0,    3,    7,  ..., 4995, 4996, 4999]),
+       values=tensor([0.4727, 0.1556, 0.1081,  ..., 0.9285, 0.0937, 0.3872]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.9962, 0.8284, 0.0086,  ..., 0.9887, 0.5066, 0.4274])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 16.019237279891968 seconds
+
+[16.44, 16.68, 16.72, 16.56, 16.6, 16.76, 16.84, 16.84, 16.68, 16.52]
+[16.44, 16.48, 16.8, 17.84, 18.92, 25.84, 27.76, 29.92, 29.96, 29.48, 23.32, 23.32, 20.24, 20.12, 20.24, 20.04, 20.04, 20.24, 20.28, 20.2, 20.12]
+21.319084644317627
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 16.019237279891968, 'TIME_S_1KI': 160.19237279891968, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 445.7399830436707, 'W': 20.90802632853554}
+[16.44, 16.68, 16.72, 16.56, 16.6, 16.76, 16.84, 16.84, 16.68, 16.52, 16.44, 16.44, 16.56, 16.56, 16.72, 16.64, 16.56, 16.52, 16.4, 16.44]
+299.0
+14.95
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 16.019237279891968, 'TIME_S_1KI': 160.19237279891968, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 445.7399830436707, 'W': 20.90802632853554, 'J_1KI': 4457.399830436707, 'W_1KI': 209.08026328535541, 'W_D': 5.958026328535542, 'J_D': 127.0196676111222, 'W_D_1KI': 59.58026328535542, 'J_D_1KI': 595.8026328535542}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..d6dabaf
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.045433044433594, "TIME_S_1KI": 210.45433044433594, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 590.3603710937499, "W": 21.581829265499348, "J_1KI": 5903.603710937499, "W_1KI": 215.81829265499348, "W_D": 6.281829265499347, "J_D": 171.83636339187612, "W_D_1KI": 62.81829265499347, "J_D_1KI": 628.1829265499347}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..bcbbf0c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 21.045433044433594}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2076,     4151,  ...,  9996002,
+                             9998013, 10000000]),
+       col_indices=tensor([   1,   14,   15,  ..., 4994, 4998, 4999]),
+       values=tensor([0.5831, 0.7619, 0.5912,  ..., 0.7349, 0.2932, 0.8119]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6981, 0.1886, 0.6279,  ..., 0.1836, 0.5536, 0.9370])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 21.045433044433594 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2076,     4151,  ...,  9996002,
+                             9998013, 10000000]),
+       col_indices=tensor([   1,   14,   15,  ..., 4994, 4998, 4999]),
+       values=tensor([0.5831, 0.7619, 0.5912,  ..., 0.7349, 0.2932, 0.8119]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6981, 0.1886, 0.6279,  ..., 0.1836, 0.5536, 0.9370])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 21.045433044433594 seconds
+
+[16.72, 16.72, 16.28, 16.36, 16.28, 17.72, 18.32, 19.12, 19.12, 19.28]
+[18.6, 17.76, 17.4, 20.72, 22.2, 26.08, 32.2, 30.12, 32.12, 31.16, 23.52, 23.52, 22.96, 20.28, 20.12, 20.24, 20.28, 20.28, 20.32, 20.32, 20.2, 20.12, 20.28, 20.28, 20.48, 20.6, 20.48]
+27.35451030731201
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.045433044433594, 'TIME_S_1KI': 210.45433044433594, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 590.3603710937499, 'W': 21.581829265499348}
+[16.72, 16.72, 16.28, 16.36, 16.28, 17.72, 18.32, 19.12, 19.12, 19.28, 16.44, 16.48, 16.36, 16.32, 16.32, 16.52, 16.72, 16.56, 16.4, 16.36]
+306.0
+15.3
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 21.045433044433594, 'TIME_S_1KI': 210.45433044433594, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 590.3603710937499, 'W': 21.581829265499348, 'J_1KI': 5903.603710937499, 'W_1KI': 215.81829265499348, 'W_D': 6.281829265499347, 'J_D': 171.83636339187612, 'W_D_1KI': 62.81829265499347, 'J_D_1KI': 628.1829265499347}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..edd5f52
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 100, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.53720736503601, "TIME_S_1KI": 265.3720736503601, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 691.3309565734863, "W": 20.656596648884427, "J_1KI": 6913.309565734863, "W_1KI": 206.56596648884428, "W_D": 5.692596648884429, "J_D": 190.5187167835236, "W_D_1KI": 56.92596648884429, "J_D_1KI": 569.2596648884429}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..7bfb817
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,45 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 26.53720736503601}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2531,     5099,  ..., 12494952,
+                            12497469, 12500000]),
+       col_indices=tensor([   0,    7,    9,  ..., 4997, 4998, 4999]),
+       values=tensor([0.6564, 0.0127, 0.9586,  ..., 0.9277, 0.7224, 0.6295]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9909, 0.2884, 0.1156,  ..., 0.4898, 0.4767, 0.4308])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 26.53720736503601 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2531,     5099,  ..., 12494952,
+                            12497469, 12500000]),
+       col_indices=tensor([   0,    7,    9,  ..., 4997, 4998, 4999]),
+       values=tensor([0.6564, 0.0127, 0.9586,  ..., 0.9277, 0.7224, 0.6295]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9909, 0.2884, 0.1156,  ..., 0.4898, 0.4767, 0.4308])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 26.53720736503601 seconds
+
+[16.56, 16.48, 16.36, 16.32, 16.28, 16.28, 16.52, 16.48, 16.8, 16.84]
+[16.84, 16.88, 16.84, 18.36, 18.96, 19.92, 26.16, 27.2, 30.16, 30.2, 27.92, 24.32, 23.48, 20.0, 20.0, 20.32, 20.36, 20.32, 20.48, 20.28, 20.2, 20.28, 20.2, 20.12, 20.08, 19.92, 19.92, 19.92, 20.04, 20.04, 20.2, 20.32, 20.16]
+33.4678053855896
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.53720736503601, 'TIME_S_1KI': 265.3720736503601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 691.3309565734863, 'W': 20.656596648884427}
+[16.56, 16.48, 16.36, 16.32, 16.28, 16.28, 16.52, 16.48, 16.8, 16.84, 16.6, 16.96, 16.88, 17.08, 17.04, 16.88, 16.6, 16.6, 16.52, 16.4]
+299.28
+14.963999999999999
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 100, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 26.53720736503601, 'TIME_S_1KI': 265.3720736503601, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 691.3309565734863, 'W': 20.656596648884427, 'J_1KI': 6913.309565734863, 'W_1KI': 206.56596648884428, 'W_D': 5.692596648884429, 'J_D': 190.5187167835236, 'W_D_1KI': 56.92596648884429, 'J_D_1KI': 569.2596648884429}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..50a61b1
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 277466, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.306395292282104, "TIME_S_1KI": 0.037144714279522914, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 274.4401516246796, "W": 19.314621204373413, "J_1KI": 0.9890947057465764, "W_1KI": 0.06961076746114267, "W_D": 4.503621204373411, "J_D": 63.99165032076835, "W_D_1KI": 0.016231254295565625, "J_D_1KI": 5.849817381432545e-05}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..19817c6
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,329 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.012943029403686523}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 249, 250]),
+       col_indices=tensor([1969, 3660, 2508, 3210, 4469, 2580, 1634, 4852, 2880,
+                           4171,  145, 1923, 1759,  292,  976, 2609, 4097, 2004,
+                           2150,  921,  866,  680, 1106, 3111, 2786, 1102, 3351,
+                            842, 4401, 1712,  443, 1843, 4964, 3710, 2157, 2831,
+                           4456, 4953,  180, 4915, 1729, 1065, 1121, 4037, 4202,
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+                           1841, 2976, 2610, 4155, 4902, 3543, 1747, 1961, 3342,
+                           3548, 4564, 2128, 4095,  930, 3054, 2352, 4784, 3523,
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+                           4688, 3574, 4745, 4027,  705, 4556,  616, 3800, 1985,
+                            763, 4632, 1608, 1959, 3621,  896, 2004, 4509, 4069,
+                           2954, 1079,  553, 3501,  741,  537,  251]),
+       values=tensor([0.4598, 0.9685, 0.1022, 0.6570, 0.2626, 0.0213, 0.4956,
+                      0.5380, 0.8437, 0.5470, 0.0848, 0.9620, 0.0264, 0.2693,
+                      0.6692, 0.6401, 0.9283, 0.8706, 0.6275, 0.5943, 0.3989,
+                      0.0766, 0.9946, 0.3522, 0.5912, 0.5773, 0.4771, 0.7005,
+                      0.3650, 0.1154, 0.5630, 0.9184, 0.7700, 0.6786, 0.7164,
+                      0.8177, 0.4007, 0.8722, 0.7773, 0.2355, 0.0635, 0.3183,
+                      0.2674, 0.1684, 0.4826, 0.2298, 0.6975, 0.5790, 0.7160,
+                      0.7090, 0.3192, 0.5709, 0.6972, 0.7933, 0.5030, 0.7005,
+                      0.6711, 0.9473, 0.3130, 0.7328, 0.0053, 0.6263, 0.3400,
+                      0.8317, 0.2127, 0.6912, 0.5279, 0.3351, 0.9454, 0.0502,
+                      0.5418, 0.3942, 0.6647, 0.1585, 0.1151, 0.4637, 0.2766,
+                      0.6645, 0.2561, 0.3586, 0.9007, 0.5641, 0.9024, 0.0924,
+                      0.7442, 0.4202, 0.9456, 0.3672, 0.6816, 0.6385, 0.3813,
+                      0.9352, 0.7740, 0.6736, 0.3753, 0.4691, 0.5509, 0.9780,
+                      0.7039, 0.8897, 0.5298, 0.1267, 0.2404, 0.1878, 0.8542,
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+                      0.8032, 0.7493, 0.7469, 0.6805, 0.5330, 0.9512, 0.6693,
+                      0.2875, 0.6060, 0.0101, 0.6329, 0.2104, 0.2244, 0.8216,
+                      0.9850, 0.4320, 0.6288, 0.3139, 0.5255, 0.8128, 0.7760,
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+                      0.9188, 0.1363, 0.2426, 0.1136, 0.6949, 0.1461, 0.1346,
+                      0.9068, 0.3140, 0.4405, 0.5547, 0.7829, 0.5939, 0.7712,
+                      0.2027, 0.1672, 0.0139, 0.3950, 0.5428, 0.8675, 0.7414,
+                      0.0223, 0.4081, 0.0576, 0.0795, 0.5466, 0.8051, 0.7481,
+                      0.9772, 0.6723, 0.3420, 0.3703, 0.9258, 0.7375, 0.0725,
+                      0.3308, 0.3117, 0.7279, 0.7861, 0.0210, 0.0730, 0.7490,
+                      0.2970, 0.3351, 0.7560, 0.6932, 0.1496, 0.7733, 0.0425,
+                      0.6190, 0.6584, 0.3857, 0.2533, 0.8724, 0.8049, 0.1764,
+                      0.5947, 0.7425, 0.4336, 0.4669, 0.5320, 0.4576, 0.7077,
+                      0.9652, 0.4590, 0.0046, 0.8577, 0.7045, 0.2574, 0.6998,
+                      0.6151, 0.3438, 0.0111, 0.9822, 0.5390, 0.2832, 0.0368,
+                      0.2525, 0.0950, 0.9720, 0.8354, 0.5121, 0.4167, 0.7493,
+                      0.0233, 0.0628, 0.4681, 0.4028, 0.7881, 0.6370, 0.0526,
+                      0.9211, 0.1381, 0.0723, 0.8459, 0.6295, 0.9210, 0.7735,
+                      0.6887, 0.7409, 0.5606, 0.9654, 0.1055]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.8735, 0.3833, 0.2694,  ..., 0.9142, 0.8243, 0.1368])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.012943029403686523 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 81124 -ss 5000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.069929599761963}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 249, 250, 250]),
+       col_indices=tensor([4942, 4831, 3020, 4398,   15,  273, 1128,  112, 3723,
+                           3585,  170, 3865, 4631, 2789,  948, 1973, 2152, 1168,
+                           2755, 4895, 2445,  616,  942, 1070,   15, 1656, 1361,
+                            381, 4008,  517,  700, 1825, 1420, 3779, 3711, 3163,
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+                            705, 4226, 2826, 2546, 3410, 4394, 1120, 1279, 3317,
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+                           2242, 2044, 2755, 1543, 1597, 1787,  451, 1992, 2258,
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+                           1006, 2747, 1763, 4043, 3117, 1987, 1941,  903, 4871,
+                           2123, 2041, 2574,  168, 2922, 2931, 3435]),
+       values=tensor([0.2833, 0.5136, 0.0459, 0.1040, 0.7712, 0.7813, 0.1004,
+                      0.0062, 0.4357, 0.2247, 0.8578, 0.6295, 0.0947, 0.0842,
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+                      0.1493, 0.3104, 0.8119, 0.6904, 0.9596, 0.5459, 0.5380,
+                      0.4871, 0.4126, 0.3848, 0.8347, 0.6321]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.3638, 0.9432, 0.7166,  ..., 0.3961, 0.0448, 0.0792])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 3.069929599761963 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 277466 -ss 5000 -sd 1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.306395292282104}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([2530, 1671, 1710,  529, 4147, 2671,  774, 3521, 3142,
+                           4079, 2231, 3137,  198, 1214, 3263,  994, 2667, 2294,
+                            182, 3631, 1055, 2979,   71, 3078, 4821, 3439, 3949,
+                           2018, 1636, 1734, 4146, 1819,  670, 2737, 4839,  929,
+                            652, 4064, 1709, 1446,  637,  485, 3208, 2342, 4556,
+                           3470, 1760, 3837, 1164,  826, 2500,  338, 4213, 1539,
+                           2699,    7, 3593,  628, 2634, 1851, 2277, 2906, 1873,
+                           3675,  109,  933,   39, 1888, 3153, 1802, 2749, 4653,
+                             10, 1407, 3436, 4501, 1652, 4732, 4648, 3990, 3869,
+                           1528, 3105, 3115, 2926,  448, 1508, 3766,  414,    0,
+                             99, 1356,  732, 2391, 4307,  374, 3096, 3847, 1168,
+                           2149, 2270, 3071, 2538, 4038, 1887, 3751, 3671, 1345,
+                            271, 1144,  828, 1558, 3741,  642, 1130,   26, 2512,
+                           1351, 4437,   62, 3040, 3132, 4639, 4608, 3358, 1316,
+                           2346, 4344, 2385, 4204,  358, 3346, 4011,  297,  728,
+                           1635, 4143, 3858, 4661, 2365, 4156, 4923, 3921, 4212,
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+                           2128, 2335, 3936, 4843, 1018, 2088, 3052, 1843, 3652,
+                           3264, 2342,  212, 2423, 2603,  526, 4357, 2538, 2326,
+                           4304, 4490,   19, 2158, 4734, 2481, 4574, 1764, 4922,
+                           1924, 1668,  665, 3628, 4416,  779, 3359, 3281,  668,
+                           2259,  684, 1693,  262, 2335, 2116, 2444,  285,  472,
+                           1695, 1989, 2831, 2933, 4834, 3892, 2679,   43,  338,
+                           1143, 3133, 3290, 2874, 3505, 1654, 2420, 3323, 4487,
+                           4528, 2876, 3002, 3959,  635, 1503, 2493, 4974, 3994,
+                           3304, 3215, 2609, 4509, 2631, 2777,  683, 3623, 3596,
+                           2685,  115, 2166, 1456, 3440, 4502, 1541,  136, 4160,
+                           2313, 2928, 4917, 3863, 3827, 2109, 4794]),
+       values=tensor([0.1255, 0.3198, 0.8133, 0.3742, 0.0163, 0.1439, 0.7607,
+                      0.6784, 0.8830, 0.0545, 0.8528, 0.7242, 0.8352, 0.4737,
+                      0.9256, 0.7090, 0.7451, 0.5297, 0.6794, 0.7283, 0.9067,
+                      0.0313, 0.2449, 0.1565, 0.6919, 0.4035, 0.9905, 0.2192,
+                      0.0562, 0.4841, 0.8665, 0.8712, 0.9887, 0.8805, 0.4264,
+                      0.9291, 0.7188, 0.3153, 0.5767, 0.0112, 0.8354, 0.4919,
+                      0.1313, 0.2676, 0.8495, 0.9700, 0.8615, 0.6450, 0.0071,
+                      0.4545, 0.8713, 0.2228, 0.4878, 0.1926, 0.0886, 0.8092,
+                      0.4330, 0.1067, 0.1112, 0.2683, 0.4340, 0.7229, 0.1649,
+                      0.0932, 0.0193, 0.5783, 0.2193, 0.3091, 0.4364, 0.5673,
+                      0.8010, 0.5772, 0.0521, 0.5829, 0.4101, 0.3786, 0.0283,
+                      0.4786, 0.3304, 0.3446, 0.7315, 0.6206, 0.8294, 0.4404,
+                      0.4676, 0.0871, 0.3497, 0.0069, 0.9043, 0.8947, 0.1952,
+                      0.6809, 0.4255, 0.1696, 0.7442, 0.9124, 0.8603, 0.9907,
+                      0.1133, 0.2677, 0.6551, 0.8223, 0.8137, 0.2411, 0.5924,
+                      0.7002, 0.4248, 0.2041, 0.8601, 0.8179, 0.6180, 0.7986,
+                      0.0067, 0.6255, 0.0265, 0.4455, 0.0788, 0.2798, 0.3073,
+                      0.9253, 0.6087, 0.7948, 0.9058, 0.2527, 0.3922, 0.9638,
+                      0.1626, 0.4231, 0.5916, 0.0663, 0.3747, 0.8133, 0.1672,
+                      0.4958, 0.1234, 0.2670, 0.0752, 0.3763, 0.6411, 0.3294,
+                      0.4132, 0.2682, 0.3319, 0.1004, 0.6692, 0.2485, 0.0663,
+                      0.1318, 0.4180, 0.2011, 0.4748, 0.2487, 0.0200, 0.3002,
+                      0.6475, 0.2552, 0.7456, 0.9304, 0.8959, 0.8069, 0.8309,
+                      0.8055, 0.5114, 0.9547, 0.4277, 0.3391, 0.6653, 0.7441,
+                      0.9317, 0.2522, 0.9794, 0.9450, 0.7609, 0.7552, 0.3464,
+                      0.2683, 0.6131, 0.7507, 0.3858, 0.2947, 0.5291, 0.7914,
+                      0.4452, 0.6309, 0.6569, 0.3974, 0.5452, 0.9065, 0.8000,
+                      0.7314, 0.8661, 0.0826, 0.0659, 0.8406, 0.3397, 0.6235,
+                      0.6886, 0.4334, 0.9899, 0.6808, 0.0386, 0.9324, 0.6160,
+                      0.2724, 0.3632, 0.4386, 0.4733, 0.7494, 0.2806, 0.7238,
+                      0.0116, 0.8061, 0.3580, 0.8134, 0.7511, 0.4690, 0.9418,
+                      0.0495, 0.8282, 0.9024, 0.7411, 0.2424, 0.5263, 0.6983,
+                      0.9412, 0.6025, 0.1977, 0.9907, 0.4170, 0.2685, 0.9711,
+                      0.6755, 0.6817, 0.5130, 0.8481, 0.9901, 0.9980, 0.3527,
+                      0.5949, 0.5533, 0.2777, 0.4754, 0.0948, 0.6148, 0.7233,
+                      0.0545, 0.7637, 0.1155, 0.4005, 0.7155]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.1431, 0.4969, 0.0611,  ..., 0.8896, 0.3924, 0.7446])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.306395292282104 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([2530, 1671, 1710,  529, 4147, 2671,  774, 3521, 3142,
+                           4079, 2231, 3137,  198, 1214, 3263,  994, 2667, 2294,
+                            182, 3631, 1055, 2979,   71, 3078, 4821, 3439, 3949,
+                           2018, 1636, 1734, 4146, 1819,  670, 2737, 4839,  929,
+                            652, 4064, 1709, 1446,  637,  485, 3208, 2342, 4556,
+                           3470, 1760, 3837, 1164,  826, 2500,  338, 4213, 1539,
+                           2699,    7, 3593,  628, 2634, 1851, 2277, 2906, 1873,
+                           3675,  109,  933,   39, 1888, 3153, 1802, 2749, 4653,
+                             10, 1407, 3436, 4501, 1652, 4732, 4648, 3990, 3869,
+                           1528, 3105, 3115, 2926,  448, 1508, 3766,  414,    0,
+                             99, 1356,  732, 2391, 4307,  374, 3096, 3847, 1168,
+                           2149, 2270, 3071, 2538, 4038, 1887, 3751, 3671, 1345,
+                            271, 1144,  828, 1558, 3741,  642, 1130,   26, 2512,
+                           1351, 4437,   62, 3040, 3132, 4639, 4608, 3358, 1316,
+                           2346, 4344, 2385, 4204,  358, 3346, 4011,  297,  728,
+                           1635, 4143, 3858, 4661, 2365, 4156, 4923, 3921, 4212,
+                            419, 1025, 1912, 1997, 3589,  965, 4863,  581, 2400,
+                           2128, 2335, 3936, 4843, 1018, 2088, 3052, 1843, 3652,
+                           3264, 2342,  212, 2423, 2603,  526, 4357, 2538, 2326,
+                           4304, 4490,   19, 2158, 4734, 2481, 4574, 1764, 4922,
+                           1924, 1668,  665, 3628, 4416,  779, 3359, 3281,  668,
+                           2259,  684, 1693,  262, 2335, 2116, 2444,  285,  472,
+                           1695, 1989, 2831, 2933, 4834, 3892, 2679,   43,  338,
+                           1143, 3133, 3290, 2874, 3505, 1654, 2420, 3323, 4487,
+                           4528, 2876, 3002, 3959,  635, 1503, 2493, 4974, 3994,
+                           3304, 3215, 2609, 4509, 2631, 2777,  683, 3623, 3596,
+                           2685,  115, 2166, 1456, 3440, 4502, 1541,  136, 4160,
+                           2313, 2928, 4917, 3863, 3827, 2109, 4794]),
+       values=tensor([0.1255, 0.3198, 0.8133, 0.3742, 0.0163, 0.1439, 0.7607,
+                      0.6784, 0.8830, 0.0545, 0.8528, 0.7242, 0.8352, 0.4737,
+                      0.9256, 0.7090, 0.7451, 0.5297, 0.6794, 0.7283, 0.9067,
+                      0.0313, 0.2449, 0.1565, 0.6919, 0.4035, 0.9905, 0.2192,
+                      0.0562, 0.4841, 0.8665, 0.8712, 0.9887, 0.8805, 0.4264,
+                      0.9291, 0.7188, 0.3153, 0.5767, 0.0112, 0.8354, 0.4919,
+                      0.1313, 0.2676, 0.8495, 0.9700, 0.8615, 0.6450, 0.0071,
+                      0.4545, 0.8713, 0.2228, 0.4878, 0.1926, 0.0886, 0.8092,
+                      0.4330, 0.1067, 0.1112, 0.2683, 0.4340, 0.7229, 0.1649,
+                      0.0932, 0.0193, 0.5783, 0.2193, 0.3091, 0.4364, 0.5673,
+                      0.8010, 0.5772, 0.0521, 0.5829, 0.4101, 0.3786, 0.0283,
+                      0.4786, 0.3304, 0.3446, 0.7315, 0.6206, 0.8294, 0.4404,
+                      0.4676, 0.0871, 0.3497, 0.0069, 0.9043, 0.8947, 0.1952,
+                      0.6809, 0.4255, 0.1696, 0.7442, 0.9124, 0.8603, 0.9907,
+                      0.1133, 0.2677, 0.6551, 0.8223, 0.8137, 0.2411, 0.5924,
+                      0.7002, 0.4248, 0.2041, 0.8601, 0.8179, 0.6180, 0.7986,
+                      0.0067, 0.6255, 0.0265, 0.4455, 0.0788, 0.2798, 0.3073,
+                      0.9253, 0.6087, 0.7948, 0.9058, 0.2527, 0.3922, 0.9638,
+                      0.1626, 0.4231, 0.5916, 0.0663, 0.3747, 0.8133, 0.1672,
+                      0.4958, 0.1234, 0.2670, 0.0752, 0.3763, 0.6411, 0.3294,
+                      0.4132, 0.2682, 0.3319, 0.1004, 0.6692, 0.2485, 0.0663,
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+                      0.6475, 0.2552, 0.7456, 0.9304, 0.8959, 0.8069, 0.8309,
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+                      0.4452, 0.6309, 0.6569, 0.3974, 0.5452, 0.9065, 0.8000,
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+                      0.6886, 0.4334, 0.9899, 0.6808, 0.0386, 0.9324, 0.6160,
+                      0.2724, 0.3632, 0.4386, 0.4733, 0.7494, 0.2806, 0.7238,
+                      0.0116, 0.8061, 0.3580, 0.8134, 0.7511, 0.4690, 0.9418,
+                      0.0495, 0.8282, 0.9024, 0.7411, 0.2424, 0.5263, 0.6983,
+                      0.9412, 0.6025, 0.1977, 0.9907, 0.4170, 0.2685, 0.9711,
+                      0.6755, 0.6817, 0.5130, 0.8481, 0.9901, 0.9980, 0.3527,
+                      0.5949, 0.5533, 0.2777, 0.4754, 0.0948, 0.6148, 0.7233,
+                      0.0545, 0.7637, 0.1155, 0.4005, 0.7155]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.1431, 0.4969, 0.0611,  ..., 0.8896, 0.3924, 0.7446])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.306395292282104 seconds
+
+[16.56, 16.4, 16.72, 16.72, 16.84, 16.92, 17.04, 16.64, 16.64, 16.64]
+[16.68, 16.8, 19.52, 20.28, 22.4, 23.28, 23.28, 24.04, 21.56, 21.2, 20.08, 19.92, 19.76, 19.72]
+14.208932638168335
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 277466, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.306395292282104, 'TIME_S_1KI': 0.037144714279522914, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 274.4401516246796, 'W': 19.314621204373413}
+[16.56, 16.4, 16.72, 16.72, 16.84, 16.92, 17.04, 16.64, 16.64, 16.64, 16.24, 16.2, 16.2, 16.48, 16.12, 16.2, 16.2, 16.16, 16.0, 16.04]
+296.22
+14.811000000000002
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 277466, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.306395292282104, 'TIME_S_1KI': 0.037144714279522914, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 274.4401516246796, 'W': 19.314621204373413, 'J_1KI': 0.9890947057465764, 'W_1KI': 0.06961076746114267, 'W_D': 4.503621204373411, 'J_D': 63.99165032076835, 'W_D_1KI': 0.016231254295565625, 'J_D_1KI': 5.849817381432545e-05}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..ed92a63
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Altra", "CORES": 80, "ITERATIONS": 147819, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.450809717178345, "TIME_S_1KI": 0.07070004341240534, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 270.7087575721741, "W": 19.037186911173507, "J_1KI": 1.831352921966554, "W_1KI": 0.12878714448868891, "W_D": 4.033186911173505, "J_D": 57.35190933799741, "W_D_1KI": 0.027284631279967428, "J_D_1KI": 0.00018458135476472868}
diff --git a/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..3732e5f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/altra_max_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 100 -ss 5000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.015123844146728516}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([3446, 3211, 1459,  ..., 3404, 1400, 4328]),
+       values=tensor([0.9299, 0.4025, 0.7514,  ..., 0.4501, 0.7034, 0.4301]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.5652, 0.8868, 0.6802,  ..., 0.3723, 0.2839, 0.7363])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.015123844146728516 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 69426 -ss 5000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.931497573852539}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([2025, 3924,  898,  ..., 1281, 3893, 4108]),
+       values=tensor([0.0304, 0.7639, 0.3864,  ..., 0.2285, 0.7727, 0.3264]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.1740, 0.1105, 0.4664,  ..., 0.6286, 0.2183, 0.7582])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 4.931497573852539 seconds
+
+['apptainer', 'run', 'pytorch-altra.sif', '-c', 'numactl --cpunodebind=0 --membind=0 python3 spmv.py synthetic csr 147819 -ss 5000 -sd 5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.450809717178345}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4384, 3481, 3445,  ..., 2544, 3259, 4103]),
+       values=tensor([0.0351, 0.6486, 0.3996,  ..., 0.8336, 0.2691, 0.7388]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.9301, 0.0450, 0.3581,  ..., 0.7152, 0.3980, 0.2103])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.450809717178345 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at /space/jenkins/workspace/Releases/pytorch-dls/pytorch-dls/aten/src/ATen/SparseCsrTensorImpl.cpp:55.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4384, 3481, 3445,  ..., 2544, 3259, 4103]),
+       values=tensor([0.0351, 0.6486, 0.3996,  ..., 0.8336, 0.2691, 0.7388]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.9301, 0.0450, 0.3581,  ..., 0.7152, 0.3980, 0.2103])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.450809717178345 seconds
+
+[16.36, 16.36, 16.6, 16.8, 16.76, 16.88, 16.88, 16.64, 16.44, 16.48]
+[16.72, 16.76, 17.08, 20.92, 22.6, 23.2, 24.36, 21.56, 20.88, 20.88, 19.84, 19.92, 19.92, 20.04]
+14.219997882843018
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 147819, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.450809717178345, 'TIME_S_1KI': 0.07070004341240534, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 270.7087575721741, 'W': 19.037186911173507}
+[16.36, 16.36, 16.6, 16.8, 16.76, 16.88, 16.88, 16.64, 16.44, 16.48, 16.52, 16.72, 16.68, 16.76, 16.76, 16.76, 16.6, 16.68, 16.72, 16.72]
+300.08000000000004
+15.004000000000001
+{'CPU': 'Altra', 'CORES': 80, 'ITERATIONS': 147819, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.450809717178345, 'TIME_S_1KI': 0.07070004341240534, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 270.7087575721741, 'W': 19.037186911173507, 'J_1KI': 1.831352921966554, 'W_1KI': 0.12878714448868891, 'W_D': 4.033186911173505, 'J_D': 57.35190933799741, 'W_D_1KI': 0.027284631279967428, 'J_D_1KI': 0.00018458135476472868}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..4ca7cf3
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 67064, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.610118389129639, "TIME_S_1KI": 0.15820885108448107, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1755.3175721168518, "W": 134.25, "J_1KI": 26.173767924920252, "W_1KI": 2.0018191578193965, "W_D": 98.104, "J_D": 1282.7089392547607, "W_D_1KI": 1.4628414648693786, "J_D_1KI": 0.021812618765200086}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..6ec2345
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.05684256553649902}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      13,      23,  ...,  999979,
+                             999991, 1000000]),
+       col_indices=tensor([ 4525, 11074, 13753,  ..., 80507, 85385, 86427]),
+       values=tensor([0.4106, 0.9983, 0.2404,  ..., 0.2427, 0.2624, 0.7034]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4963, 0.1898, 0.5953,  ..., 0.4144, 0.1558, 0.0288])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.05684256553649902 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18472', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.8920857906341553}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       8,      17,  ...,  999981,
+                             999989, 1000000]),
+       col_indices=tensor([ 8653, 22699, 39303,  ..., 86578, 89246, 90775]),
+       values=tensor([0.9948, 0.4799, 0.6025,  ..., 0.7759, 0.3812, 0.6990]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4256, 0.6072, 0.2987,  ..., 0.6512, 0.1573, 0.7068])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 2.8920857906341553 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '67064', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.610118389129639}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      22,  ...,  999983,
+                             999993, 1000000]),
+       col_indices=tensor([27755, 29395, 33124,  ..., 74386, 97777, 99456]),
+       values=tensor([0.7148, 0.9361, 0.5875,  ..., 0.1256, 0.4168, 0.2712]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0632, 0.1399, 0.4829,  ..., 0.0512, 0.0510, 0.5050])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.610118389129639 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       9,      22,  ...,  999983,
+                             999993, 1000000]),
+       col_indices=tensor([27755, 29395, 33124,  ..., 74386, 97777, 99456]),
+       values=tensor([0.7148, 0.9361, 0.5875,  ..., 0.1256, 0.4168, 0.2712]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0632, 0.1399, 0.4829,  ..., 0.0512, 0.0510, 0.5050])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.610118389129639 seconds
+
+[42.15, 40.1, 39.72, 39.74, 39.84, 40.12, 39.64, 39.58, 39.89, 39.55]
+[134.25]
+13.074991226196289
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 67064, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.610118389129639, 'TIME_S_1KI': 0.15820885108448107, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1755.3175721168518, 'W': 134.25}
+[42.15, 40.1, 39.72, 39.74, 39.84, 40.12, 39.64, 39.58, 39.89, 39.55, 42.7, 40.22, 40.9, 40.07, 39.72, 39.89, 39.69, 39.67, 39.56, 44.74]
+722.9200000000001
+36.146
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 67064, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.610118389129639, 'TIME_S_1KI': 0.15820885108448107, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1755.3175721168518, 'W': 134.25, 'J_1KI': 26.173767924920252, 'W_1KI': 2.0018191578193965, 'W_D': 98.104, 'J_D': 1282.7089392547607, 'W_D_1KI': 1.4628414648693786, 'J_D_1KI': 0.021812618765200086}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..97a1aaa
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3865, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.6199471950531, "TIME_S_1KI": 2.7477224308028725, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1736.0991844940186, "W": 125.54, "J_1KI": 449.1847825340281, "W_1KI": 32.48124191461837, "W_D": 89.51725000000002, "J_D": 1237.938702589989, "W_D_1KI": 23.160996119016822, "J_D_1KI": 5.992495761711985}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..ab63507
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.29607152938842773}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      108,      205,  ...,  9999797,
+                             9999886, 10000000]),
+       col_indices=tensor([ 1353,  2200,  3779,  ..., 96854, 97028, 97339]),
+       values=tensor([0.4346, 0.2367, 0.4770,  ..., 0.1479, 0.4649, 0.9103]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.4412, 0.7177, 0.2059,  ..., 0.3280, 0.0589, 0.7180])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 0.29607152938842773 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3546', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 9.63291883468628}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       92,      201,  ...,  9999814,
+                             9999913, 10000000]),
+       col_indices=tensor([ 1097,  1389,  2328,  ..., 96293, 96542, 99036]),
+       values=tensor([0.4476, 0.1977, 0.6820,  ..., 0.8020, 0.1490, 0.2819]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6506, 0.9195, 0.4022,  ..., 0.6497, 0.8706, 0.8621])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 9.63291883468628 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3865', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.6199471950531}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       98,      197,  ...,  9999799,
+                             9999902, 10000000]),
+       col_indices=tensor([ 2919,  3313,  3728,  ..., 97238, 97697, 98577]),
+       values=tensor([0.4198, 0.7828, 0.7567,  ..., 0.6995, 0.0988, 0.1528]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.5695, 0.4144, 0.5638,  ..., 0.3431, 0.2067, 0.0841])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.6199471950531 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       98,      197,  ...,  9999799,
+                             9999902, 10000000]),
+       col_indices=tensor([ 2919,  3313,  3728,  ..., 97238, 97697, 98577]),
+       values=tensor([0.4198, 0.7828, 0.7567,  ..., 0.6995, 0.0988, 0.1528]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.5695, 0.4144, 0.5638,  ..., 0.3431, 0.2067, 0.0841])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.6199471950531 seconds
+
+[41.63, 40.06, 40.05, 39.72, 39.88, 39.71, 40.32, 40.32, 40.26, 39.8]
+[125.54]
+13.829051971435547
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3865, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.6199471950531, 'TIME_S_1KI': 2.7477224308028725, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1736.0991844940186, 'W': 125.54}
+[41.63, 40.06, 40.05, 39.72, 39.88, 39.71, 40.32, 40.32, 40.26, 39.8, 40.39, 39.7, 39.76, 39.75, 39.78, 40.5, 39.95, 39.84, 40.11, 39.67]
+720.4549999999999
+36.022749999999995
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3865, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.6199471950531, 'TIME_S_1KI': 2.7477224308028725, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1736.0991844940186, 'W': 125.54, 'J_1KI': 449.1847825340281, 'W_1KI': 32.48124191461837, 'W_D': 89.51725000000002, 'J_D': 1237.938702589989, 'W_D_1KI': 23.160996119016822, 'J_D_1KI': 5.992495761711985}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..9be5a17
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 102064, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.524274349212646, "TIME_S_1KI": 0.10311446101674092, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1601.1415306377412, "W": 113.64000000000001, "J_1KI": 15.687622772355983, "W_1KI": 1.1134190311961123, "W_D": 77.54950000000002, "J_D": 1092.6410166331532, "W_D_1KI": 0.7598124706066784, "J_D_1KI": 0.007444470828173288}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..a8ac003
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.051177263259887695}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      1,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([ 5338, 33433, 17911,  ..., 60039, 74427, 45774]),
+       values=tensor([0.9933, 0.3915, 0.2951,  ..., 0.3503, 0.7922, 0.2614]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6274, 0.9288, 0.1155,  ..., 0.7548, 0.5951, 0.2372])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.051177263259887695 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '20516', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 2.11061429977417}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      2,  ...,  99999, 100000,
+                            100000]),
+       col_indices=tensor([35938, 84023, 26382,  ..., 80961, 25218, 78065]),
+       values=tensor([0.1771, 0.7263, 0.1955,  ..., 0.1569, 0.5183, 0.0872]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.4261, 0.9316, 0.9486,  ..., 0.4583, 0.3074, 0.5243])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 2.11061429977417 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '102064', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.524274349212646}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      3,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([ 7923, 87583, 82060,  ..., 33729, 87076,   106]),
+       values=tensor([0.1731, 0.5965, 0.6757,  ..., 0.8844, 0.0621, 0.1000]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.3641, 0.2409, 0.5686,  ..., 0.5557, 0.7015, 0.9398])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.524274349212646 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      3,  ..., 100000, 100000,
+                            100000]),
+       col_indices=tensor([ 7923, 87583, 82060,  ..., 33729, 87076,   106]),
+       values=tensor([0.1731, 0.5965, 0.6757,  ..., 0.8844, 0.0621, 0.1000]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.3641, 0.2409, 0.5686,  ..., 0.5557, 0.7015, 0.9398])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.524274349212646 seconds
+
+[40.73, 39.59, 39.74, 39.73, 39.63, 39.83, 40.08, 39.98, 40.09, 39.48]
+[113.64]
+14.089594602584839
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102064, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.524274349212646, 'TIME_S_1KI': 0.10311446101674092, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1601.1415306377412, 'W': 113.64000000000001}
+[40.73, 39.59, 39.74, 39.73, 39.63, 39.83, 40.08, 39.98, 40.09, 39.48, 40.32, 39.82, 39.63, 39.81, 39.64, 39.48, 39.85, 39.99, 44.93, 39.45]
+721.81
+36.0905
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102064, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.524274349212646, 'TIME_S_1KI': 0.10311446101674092, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1601.1415306377412, 'W': 113.64000000000001, 'J_1KI': 15.687622772355983, 'W_1KI': 1.1134190311961123, 'W_D': 77.54950000000002, 'J_D': 1092.6410166331532, 'W_D_1KI': 0.7598124706066784, 'J_D_1KI': 0.007444470828173288}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..24bf6da
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 83443, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.331598043441772, "TIME_S_1KI": 0.12381623435688761, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1753.1380189323427, "W": 133.96, "J_1KI": 21.010007057899916, "W_1KI": 1.6054072840142373, "W_D": 97.99700000000001, "J_D": 1282.489298606396, "W_D_1KI": 1.1744184652996656, "J_D_1KI": 0.014074499542198454}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..566b51a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.04202604293823242}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,     10,  ..., 499989, 499993,
+                            500000]),
+       col_indices=tensor([44479,  4048, 15938,  ..., 81904, 89204, 96058]),
+       values=tensor([0.8024, 0.2371, 0.1804,  ..., 0.7304, 0.5867, 0.0881]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.7637, 0.3596, 0.9771,  ..., 0.6123, 0.8042, 0.6339])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 0.04202604293823242 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24984', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 3.1438426971435547}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     12,  ..., 499992, 499996,
+                            500000]),
+       col_indices=tensor([25849, 26475, 42516,  ..., 54532, 74351, 87242]),
+       values=tensor([0.9779, 0.7287, 0.9943,  ..., 0.8976, 0.9175, 0.6342]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.3426, 0.5652, 0.2473,  ..., 0.4439, 0.3784, 0.0403])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 3.1438426971435547 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '83443', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.331598043441772}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,      9,  ..., 499992, 499996,
+                            500000]),
+       col_indices=tensor([44464, 48782, 50602,  ..., 44812, 48851, 96308]),
+       values=tensor([0.0768, 0.4231, 0.3229,  ..., 0.7263, 0.8571, 0.9151]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.5899, 0.0490, 0.9717,  ..., 0.2037, 0.9811, 0.9760])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.331598043441772 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,      9,  ..., 499992, 499996,
+                            500000]),
+       col_indices=tensor([44464, 48782, 50602,  ..., 44812, 48851, 96308]),
+       values=tensor([0.0768, 0.4231, 0.3229,  ..., 0.7263, 0.8571, 0.9151]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.5899, 0.0490, 0.9717,  ..., 0.2037, 0.9811, 0.9760])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.331598043441772 seconds
+
+[41.17, 39.77, 40.36, 40.06, 41.66, 39.61, 39.74, 39.58, 39.55, 39.78]
+[133.96]
+13.087026119232178
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 83443, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.331598043441772, 'TIME_S_1KI': 0.12381623435688761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1753.1380189323427, 'W': 133.96}
+[41.17, 39.77, 40.36, 40.06, 41.66, 39.61, 39.74, 39.58, 39.55, 39.78, 41.11, 39.76, 39.73, 39.7, 39.67, 40.13, 39.73, 39.66, 39.62, 39.8]
+719.26
+35.963
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 83443, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.331598043441772, 'TIME_S_1KI': 0.12381623435688761, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1753.1380189323427, 'W': 133.96, 'J_1KI': 21.010007057899916, 'W_1KI': 1.6054072840142373, 'W_D': 97.99700000000001, 'J_D': 1282.489298606396, 'W_D_1KI': 1.1744184652996656, 'J_D_1KI': 0.014074499542198454}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..846867c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 288187, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.643323421478271, "TIME_S_1KI": 0.03693200394701451, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1243.6877346038818, "W": 98.76, "J_1KI": 4.315558073764194, "W_1KI": 0.34269415344897586, "W_D": 63.04600000000001, "J_D": 793.9402279853822, "W_D_1KI": 0.21876767515536788, "J_D_1KI": 0.0007591170842382477}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..c5c4dec
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.02071547508239746}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9999, 10000]),
+       col_indices=tensor([ 662, 2214, 9373,  ..., 9890, 1994, 4209]),
+       values=tensor([0.5116, 0.1051, 0.5373,  ..., 0.4151, 0.7725, 0.9175]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.1401, 0.0502, 0.3458,  ..., 0.2506, 0.9913, 0.9973])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.02071547508239746 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '50686', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.8467259407043457}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     5,  ...,  9998, 10000, 10000]),
+       col_indices=tensor([2329, 7525, 8810,  ..., 9177, 1519, 2359]),
+       values=tensor([0.1835, 0.9536, 0.7906,  ..., 0.4035, 0.0564, 0.3832]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.5918, 0.9817, 0.1058,  ..., 0.3816, 0.0120, 0.7112])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 1.8467259407043457 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '288187', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.643323421478271}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9998, 10000]),
+       col_indices=tensor([5118, 9103, 6912,  ..., 6081, 2494, 7728]),
+       values=tensor([0.1845, 0.4117, 0.0579,  ..., 0.8363, 0.9429, 0.5429]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.5505, 0.3516, 0.4265,  ..., 0.6375, 0.7561, 0.2541])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.643323421478271 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ...,  9998,  9998, 10000]),
+       col_indices=tensor([5118, 9103, 6912,  ..., 6081, 2494, 7728]),
+       values=tensor([0.1845, 0.4117, 0.0579,  ..., 0.8363, 0.9429, 0.5429]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.5505, 0.3516, 0.4265,  ..., 0.6375, 0.7561, 0.2541])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.643323421478271 seconds
+
+[40.51, 40.09, 39.52, 39.33, 39.47, 39.48, 39.43, 39.77, 39.53, 39.82]
+[98.76]
+12.59303092956543
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 288187, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.643323421478271, 'TIME_S_1KI': 0.03693200394701451, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1243.6877346038818, 'W': 98.76}
+[40.51, 40.09, 39.52, 39.33, 39.47, 39.48, 39.43, 39.77, 39.53, 39.82, 41.15, 40.14, 40.36, 39.64, 39.34, 39.28, 39.84, 39.18, 39.56, 39.16]
+714.28
+35.714
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 288187, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.643323421478271, 'TIME_S_1KI': 0.03693200394701451, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1243.6877346038818, 'W': 98.76, 'J_1KI': 4.315558073764194, 'W_1KI': 0.34269415344897586, 'W_D': 63.04600000000001, 'J_D': 793.9402279853822, 'W_D_1KI': 0.21876767515536788, 'J_D_1KI': 0.0007591170842382477}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..d5b0fd0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 192082, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.38179612159729, "TIME_S_1KI": 0.054048771470503694, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1536.0875409460068, "W": 107.47, "J_1KI": 7.997040539696623, "W_1KI": 0.5595006299392968, "W_D": 71.52775, "J_D": 1022.3586638773679, "W_D_1KI": 0.3723813267250445, "J_D_1KI": 0.0019386581081259277}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..41b527b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.02809286117553711}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     18,  ...,  99983,  99993,
+                            100000]),
+       col_indices=tensor([2653, 3722, 5304,  ..., 7707, 8674, 8869]),
+       values=tensor([0.5856, 0.9425, 0.9349,  ..., 0.4089, 0.4268, 0.7151]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5412, 0.5320, 0.4895,  ..., 0.9332, 0.4774, 0.7844])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.02809286117553711 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '37376', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.043123722076416}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     18,     27,  ...,  99987,  99996,
+                            100000]),
+       col_indices=tensor([ 496, 1705, 2513,  ..., 6230, 8377, 9882]),
+       values=tensor([0.7106, 0.5928, 0.5041,  ..., 0.9691, 0.8218, 0.7424]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6869, 0.6587, 0.3624,  ..., 0.7168, 0.6886, 0.1198])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 2.043123722076416 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '192082', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.38179612159729}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     14,     28,  ...,  99972,  99987,
+                            100000]),
+       col_indices=tensor([  27, 2567, 2642,  ..., 7209, 7267, 7735]),
+       values=tensor([0.8851, 0.6027, 0.9664,  ..., 0.7310, 0.7426, 0.3698]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8025, 0.3022, 0.3457,  ..., 0.3811, 0.1140, 0.9144])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.38179612159729 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     14,     28,  ...,  99972,  99987,
+                            100000]),
+       col_indices=tensor([  27, 2567, 2642,  ..., 7209, 7267, 7735]),
+       values=tensor([0.8851, 0.6027, 0.9664,  ..., 0.7310, 0.7426, 0.3698]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.8025, 0.3022, 0.3457,  ..., 0.3811, 0.1140, 0.9144])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.38179612159729 seconds
+
+[40.11, 39.55, 39.33, 44.82, 39.9, 39.64, 39.98, 39.29, 39.99, 39.21]
+[107.47]
+14.293175220489502
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 192082, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.38179612159729, 'TIME_S_1KI': 0.054048771470503694, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1536.0875409460068, 'W': 107.47}
+[40.11, 39.55, 39.33, 44.82, 39.9, 39.64, 39.98, 39.29, 39.99, 39.21, 40.63, 39.47, 39.85, 39.28, 39.66, 39.35, 39.46, 39.87, 39.78, 39.3]
+718.845
+35.94225
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 192082, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.38179612159729, 'TIME_S_1KI': 0.054048771470503694, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1536.0875409460068, 'W': 107.47, 'J_1KI': 7.997040539696623, 'W_1KI': 0.5595006299392968, 'W_D': 71.52775, 'J_D': 1022.3586638773679, 'W_D_1KI': 0.3723813267250445, 'J_D_1KI': 0.0019386581081259277}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..c0a8393
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 102052, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.32213807106018, "TIME_S_1KI": 0.10114586750931075, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1720.3125169992445, "W": 134.17, "J_1KI": 16.857215115815904, "W_1KI": 1.3147219064790499, "W_D": 98.29849999999999, "J_D": 1260.3722139990327, "W_D_1KI": 0.9632197311174694, "J_D_1KI": 0.0094385189032794}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..3b630d1
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.04350447654724121}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      98,     200,  ...,  999802,
+                             999895, 1000000]),
+       col_indices=tensor([  47,   93,  107,  ..., 9931, 9947, 9964]),
+       values=tensor([0.2387, 0.2735, 0.7135,  ..., 0.1692, 0.6802, 0.4186]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2660, 0.3479, 0.7430,  ..., 0.3350, 0.7379, 0.6869])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.04350447654724121 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '24135', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.483203411102295}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     121,     211,  ...,  999813,
+                             999902, 1000000]),
+       col_indices=tensor([ 152,  193,  233,  ..., 9824, 9889, 9990]),
+       values=tensor([0.9787, 0.2142, 0.0572,  ..., 0.5889, 0.8836, 0.8390]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.2484, 0.0072, 0.5266,  ..., 0.8378, 0.3257, 0.7895])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 2.483203411102295 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '102052', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.32213807106018}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     109,     197,  ...,  999808,
+                             999909, 1000000]),
+       col_indices=tensor([  12,  158,  312,  ..., 9915, 9965, 9970]),
+       values=tensor([0.1097, 0.7996, 0.8802,  ..., 0.2965, 0.2793, 0.1775]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4138, 0.6115, 0.5428,  ..., 0.5829, 0.0748, 0.9104])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.32213807106018 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     109,     197,  ...,  999808,
+                             999909, 1000000]),
+       col_indices=tensor([  12,  158,  312,  ..., 9915, 9965, 9970]),
+       values=tensor([0.1097, 0.7996, 0.8802,  ..., 0.2965, 0.2793, 0.1775]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.4138, 0.6115, 0.5428,  ..., 0.5829, 0.0748, 0.9104])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.32213807106018 seconds
+
+[40.44, 39.51, 39.85, 40.81, 39.86, 41.16, 39.43, 39.73, 39.64, 39.53]
+[134.17]
+12.821886539459229
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102052, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.32213807106018, 'TIME_S_1KI': 0.10114586750931075, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1720.3125169992445, 'W': 134.17}
+[40.44, 39.51, 39.85, 40.81, 39.86, 41.16, 39.43, 39.73, 39.64, 39.53, 40.32, 40.34, 39.64, 39.51, 39.49, 39.33, 39.71, 39.95, 39.67, 39.31]
+717.43
+35.8715
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 102052, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.32213807106018, 'TIME_S_1KI': 0.10114586750931075, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1720.3125169992445, 'W': 134.17, 'J_1KI': 16.857215115815904, 'W_1KI': 1.3147219064790499, 'W_D': 98.29849999999999, 'J_D': 1260.3722139990327, 'W_D_1KI': 0.9632197311174694, 'J_D_1KI': 0.0094385189032794}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..0c8793e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 27901, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.459260940551758, "TIME_S_1KI": 0.3748704684617669, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2101.511001520157, "W": 151.74, "J_1KI": 75.32027531343526, "W_1KI": 5.43851474857532, "W_D": 115.6735, "J_D": 1602.0108925421239, "W_D_1KI": 4.145854987276442, "J_D_1KI": 0.14859162708420637}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..d46dec8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.07326960563659668}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     503,     989,  ..., 4998955,
+                            4999450, 5000000]),
+       col_indices=tensor([  38,   72,   81,  ..., 9956, 9978, 9983]),
+       values=tensor([0.2927, 0.3163, 0.4567,  ..., 0.1935, 0.9639, 0.3715]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.4283, 0.0472, 0.5653,  ..., 0.2916, 0.5894, 0.9993])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 0.07326960563659668 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '14330', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 5.392660140991211}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     524,    1025,  ..., 4998972,
+                            4999489, 5000000]),
+       col_indices=tensor([   6,   15,   16,  ..., 9973, 9985, 9996]),
+       values=tensor([0.1466, 0.4320, 0.8734,  ..., 0.2839, 0.7163, 0.2149]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6711, 0.1737, 0.7087,  ..., 0.1819, 0.7746, 0.6924])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 5.392660140991211 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '27901', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.459260940551758}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     478,     963,  ..., 4998976,
+                            4999469, 5000000]),
+       col_indices=tensor([   4,    6,    8,  ..., 9977, 9981, 9998]),
+       values=tensor([0.4938, 0.7817, 0.2868,  ..., 0.2355, 0.4075, 0.9137]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7828, 0.6669, 0.8649,  ..., 0.0217, 0.0077, 0.7398])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.459260940551758 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     478,     963,  ..., 4998976,
+                            4999469, 5000000]),
+       col_indices=tensor([   4,    6,    8,  ..., 9977, 9981, 9998]),
+       values=tensor([0.4938, 0.7817, 0.2868,  ..., 0.2355, 0.4075, 0.9137]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7828, 0.6669, 0.8649,  ..., 0.0217, 0.0077, 0.7398])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.459260940551758 seconds
+
+[40.28, 39.74, 40.09, 39.52, 39.81, 39.49, 40.09, 39.86, 39.96, 39.9]
+[151.74]
+13.849420070648193
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27901, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.459260940551758, 'TIME_S_1KI': 0.3748704684617669, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2101.511001520157, 'W': 151.74}
+[40.28, 39.74, 40.09, 39.52, 39.81, 39.49, 40.09, 39.86, 39.96, 39.9, 41.6, 42.09, 41.06, 39.67, 39.75, 39.6, 39.57, 40.11, 40.26, 39.54]
+721.33
+36.066500000000005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 27901, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.459260940551758, 'TIME_S_1KI': 0.3748704684617669, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2101.511001520157, 'W': 151.74, 'J_1KI': 75.32027531343526, 'W_1KI': 5.43851474857532, 'W_D': 115.6735, 'J_D': 1602.0108925421239, 'W_D_1KI': 4.145854987276442, 'J_D_1KI': 0.14859162708420637}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..2bdc411
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4895, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.465762615203857, "TIME_S_1KI": 2.3423416987137604, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1961.564714949131, "W": 125.13, "J_1KI": 400.72823594466416, "W_1KI": 25.562819203268642, "W_D": 88.61599999999999, "J_D": 1389.1634202823636, "W_D_1KI": 18.10337078651685, "J_D_1KI": 3.698339282230205}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..c73c74f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.26529860496520996}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      948,     1895,  ...,  9998021,
+                             9999002, 10000000]),
+       col_indices=tensor([   3,    4,   24,  ..., 9958, 9984, 9986]),
+       values=tensor([0.2249, 0.5337, 0.8362,  ..., 0.6636, 0.7975, 0.6242]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1264, 0.7394, 0.5519,  ..., 0.0745, 0.0081, 0.2644])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 0.26529860496520996 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3957', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.487387418746948}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      978,     1933,  ...,  9998004,
+                             9999014, 10000000]),
+       col_indices=tensor([   3,    5,    6,  ..., 9972, 9982, 9998]),
+       values=tensor([0.7080, 0.9187, 0.9413,  ..., 0.1315, 0.2244, 0.9797]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.5306, 0.8726, 0.4027,  ..., 0.7037, 0.0033, 0.8016])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 8.487387418746948 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4895', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 11.465762615203857}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      986,     1965,  ...,  9997996,
+                             9999022, 10000000]),
+       col_indices=tensor([   5,   25,   37,  ..., 9984, 9993, 9998]),
+       values=tensor([0.8800, 0.4752, 0.0446,  ..., 0.6391, 0.5084, 0.8692]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6122, 0.5951, 0.3953,  ..., 0.4999, 0.2315, 0.6538])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 11.465762615203857 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      986,     1965,  ...,  9997996,
+                             9999022, 10000000]),
+       col_indices=tensor([   5,   25,   37,  ..., 9984, 9993, 9998]),
+       values=tensor([0.8800, 0.4752, 0.0446,  ..., 0.6391, 0.5084, 0.8692]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6122, 0.5951, 0.3953,  ..., 0.4999, 0.2315, 0.6538])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 11.465762615203857 seconds
+
+[40.6, 45.25, 40.43, 39.72, 40.94, 40.35, 40.49, 39.74, 42.24, 39.59]
+[125.13]
+15.676214456558228
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4895, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.465762615203857, 'TIME_S_1KI': 2.3423416987137604, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1961.564714949131, 'W': 125.13}
+[40.6, 45.25, 40.43, 39.72, 40.94, 40.35, 40.49, 39.74, 42.24, 39.59, 41.13, 40.62, 40.17, 40.53, 40.27, 40.01, 39.62, 39.57, 39.92, 39.5]
+730.2800000000001
+36.514
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4895, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 11.465762615203857, 'TIME_S_1KI': 2.3423416987137604, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1961.564714949131, 'W': 125.13, 'J_1KI': 400.72823594466416, 'W_1KI': 25.562819203268642, 'W_D': 88.61599999999999, 'J_D': 1389.1634202823636, 'W_D_1KI': 18.10337078651685, 'J_D_1KI': 3.698339282230205}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..0c05e29
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2082, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.557694435119629, "TIME_S_1KI": 5.070938729644394, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1859.5419220256806, "W": 120.04, "J_1KI": 893.1517396857256, "W_1KI": 57.656099903938525, "W_D": 83.7395, "J_D": 1297.2101864334345, "W_D_1KI": 40.22070124879924, "J_D_1KI": 19.31830031162307}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..68bbca5
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.5041141510009766}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2037,     4062,  ..., 19995956,
+                            19997954, 20000000]),
+       col_indices=tensor([   0,    3,    5,  ..., 9996, 9997, 9998]),
+       values=tensor([0.3088, 0.0777, 0.1762,  ..., 0.6057, 0.6562, 0.8467]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.5661, 0.3739, 0.3594,  ..., 0.8068, 0.7143, 0.9609])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 0.5041141510009766 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2082', '-ss', '10000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.557694435119629}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1900,     3860,  ..., 19996021,
+                            19998015, 20000000]),
+       col_indices=tensor([   0,    3,   11,  ..., 9989, 9992, 9996]),
+       values=tensor([0.1071, 0.4523, 0.1080,  ..., 0.2881, 0.4034, 0.8495]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.3748, 0.6654, 0.9133,  ..., 0.7126, 0.6760, 0.9288])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.557694435119629 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1900,     3860,  ..., 19996021,
+                            19998015, 20000000]),
+       col_indices=tensor([   0,    3,   11,  ..., 9989, 9992, 9996]),
+       values=tensor([0.1071, 0.4523, 0.1080,  ..., 0.2881, 0.4034, 0.8495]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.3748, 0.6654, 0.9133,  ..., 0.7126, 0.6760, 0.9288])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.557694435119629 seconds
+
+[40.83, 40.15, 39.72, 39.76, 39.84, 39.84, 40.49, 39.96, 40.13, 39.73]
+[120.04]
+15.491019010543823
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2082, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.557694435119629, 'TIME_S_1KI': 5.070938729644394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1859.5419220256806, 'W': 120.04}
+[40.83, 40.15, 39.72, 39.76, 39.84, 39.84, 40.49, 39.96, 40.13, 39.73, 41.31, 40.05, 40.38, 40.21, 39.71, 39.65, 45.13, 39.78, 40.38, 39.79]
+726.01
+36.3005
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2082, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.557694435119629, 'TIME_S_1KI': 5.070938729644394, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1859.5419220256806, 'W': 120.04, 'J_1KI': 893.1517396857256, 'W_1KI': 57.656099903938525, 'W_D': 83.7395, 'J_D': 1297.2101864334345, 'W_D_1KI': 40.22070124879924, 'J_D_1KI': 19.31830031162307}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..b94bf6c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1470, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.845632314682007, "TIME_S_1KI": 7.377981166450344, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2161.157882115841, "W": 117.03, "J_1KI": 1470.1754300107762, "W_1KI": 79.61224489795917, "W_D": 80.6345, "J_D": 1489.0531081386805, "W_D_1KI": 54.853401360544225, "J_D_1KI": 37.315239020778385}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..4e7bca2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.7518825531005859}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2974,     5950,  ..., 29994087,
+                            29997108, 30000000]),
+       col_indices=tensor([   6,    8,   10,  ..., 9985, 9992, 9996]),
+       values=tensor([0.7151, 0.6737, 0.4043,  ..., 0.5812, 0.5679, 0.6733]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.6135, 0.6008, 0.5882,  ..., 0.6628, 0.8539, 0.9204])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 0.7518825531005859 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1396', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.968559503555298}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3001,     6027,  ..., 29994039,
+                            29997052, 30000000]),
+       col_indices=tensor([   7,   11,   16,  ..., 9989, 9996, 9999]),
+       values=tensor([0.2908, 0.3192, 0.9662,  ..., 0.5726, 0.8523, 0.1200]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.6009, 0.9845, 0.3791,  ..., 0.1987, 0.1714, 0.4278])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 9.968559503555298 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1470', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.845632314682007}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2929,     5851,  ..., 29993945,
+                            29997029, 30000000]),
+       col_indices=tensor([   6,    7,   11,  ..., 9986, 9997, 9998]),
+       values=tensor([0.6210, 0.5427, 0.8130,  ..., 0.4194, 0.0441, 0.7442]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.0141, 0.0033, 0.5199,  ..., 0.4699, 0.7276, 0.5761])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.845632314682007 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2929,     5851,  ..., 29993945,
+                            29997029, 30000000]),
+       col_indices=tensor([   6,    7,   11,  ..., 9986, 9997, 9998]),
+       values=tensor([0.6210, 0.5427, 0.8130,  ..., 0.4194, 0.0441, 0.7442]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.0141, 0.0033, 0.5199,  ..., 0.4699, 0.7276, 0.5761])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.845632314682007 seconds
+
+[46.28, 39.97, 40.2, 40.69, 40.27, 40.47, 40.33, 41.03, 39.84, 40.37]
+[117.03]
+18.466699838638306
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1470, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.845632314682007, 'TIME_S_1KI': 7.377981166450344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2161.157882115841, 'W': 117.03}
+[46.28, 39.97, 40.2, 40.69, 40.27, 40.47, 40.33, 41.03, 39.84, 40.37, 41.81, 40.41, 40.59, 39.82, 40.04, 39.71, 39.78, 40.7, 39.78, 40.1]
+727.9100000000001
+36.395500000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1470, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.845632314682007, 'TIME_S_1KI': 7.377981166450344, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2161.157882115841, 'W': 117.03, 'J_1KI': 1470.1754300107762, 'W_1KI': 79.61224489795917, 'W_D': 80.6345, 'J_D': 1489.0531081386805, 'W_D_1KI': 54.853401360544225, 'J_D_1KI': 37.315239020778385}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..b4516b2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 366482, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.093939065933228, "TIME_S_1KI": 0.0302714432521467, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1264.2006668257713, "W": 98.79, "J_1KI": 3.4495573229402026, "W_1KI": 0.26956303447372587, "W_D": 62.66125, "J_D": 801.866525297463, "W_D_1KI": 0.17098043014390885, "J_D_1KI": 0.0004665452331735497}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..b43f8ba
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1793 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.019162416458129883}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([2508, 9046,  543, 4312, 1967, 3077, 2314, 3402, 8716,
+                           3244, 7898, 5856, 5314, 5976, 2342, 2492, 8104, 2147,
+                           1979, 1751, 5860, 9783, 5705, 5222, 9824,   95, 1929,
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+                      2.2592e-02, 4.9539e-01, 7.1062e-02, 1.8299e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4331, 0.6450, 0.9910,  ..., 0.2595, 0.5081, 0.5389])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.019162416458129883 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '54794', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.6940038204193115}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      2.9480e-01, 2.6967e-01, 8.8380e-03, 9.0770e-02]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8612, 0.4581, 0.7630,  ..., 0.0492, 0.2811, 0.3451])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 1.6940038204193115 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '339631', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.730679035186768}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([4972, 1405, 2738,  821, 7457, 7836, 7120, 5534,  695,
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4250, 0.1408, 0.2235,  ..., 0.5768, 0.6841, 0.8384])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 9.730679035186768 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '366482', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 11.093939065933228}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      3.1057e-01, 3.6455e-01, 8.0890e-01, 2.7987e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7740, 0.9703, 0.1840,  ..., 0.7477, 0.1526, 0.5369])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 11.093939065933228 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      6.9638e-01, 7.6941e-01, 2.3646e-01, 7.2314e-01,
+                      1.4276e-01, 4.5362e-01, 5.5788e-01, 2.2118e-01,
+                      3.1804e-01, 6.9545e-01, 4.2483e-01, 3.3560e-01,
+                      3.3597e-01, 4.0398e-02, 1.9135e-01, 5.4384e-01,
+                      5.9982e-01, 9.7372e-01, 1.5970e-01, 6.0079e-01,
+                      5.9714e-01, 9.5044e-01, 5.0232e-01, 3.6790e-01,
+                      7.3455e-01, 8.0673e-01, 4.0026e-01, 6.3795e-02,
+                      8.2192e-01, 1.8676e-01, 1.9070e-01, 6.2954e-02,
+                      1.8591e-01, 1.6582e-01, 8.1810e-01, 1.1599e-02,
+                      4.7558e-01, 8.4737e-01, 7.5306e-01, 1.2992e-01,
+                      1.5127e-01, 2.0163e-01, 4.6257e-01, 3.4956e-01,
+                      9.0495e-01, 2.1175e-03, 9.4719e-01, 3.1681e-01,
+                      2.0418e-01, 5.3479e-02, 3.5683e-01, 5.8247e-02,
+                      9.3423e-01, 1.1090e-01, 4.3007e-01, 6.1117e-01,
+                      4.0593e-01, 1.1448e-02, 8.5302e-01, 2.5408e-01,
+                      9.1190e-01, 6.4738e-01, 2.7596e-01, 2.8235e-01,
+                      4.6522e-01, 8.7885e-01, 7.0320e-01, 4.2790e-01,
+                      2.9479e-02, 3.8500e-01, 3.9245e-01, 1.0004e-01,
+                      4.2397e-01, 7.1833e-01, 6.6614e-01, 4.6682e-02,
+                      5.8017e-01, 6.0782e-01, 4.8419e-01, 5.5802e-01,
+                      2.3916e-01, 1.4114e-01, 8.3739e-01, 1.0626e-01,
+                      5.1946e-01, 9.4847e-01, 4.1767e-01, 3.7856e-01,
+                      1.1090e-01, 1.5010e-01, 4.3945e-01, 7.5067e-02,
+                      9.8959e-01, 2.8002e-01, 5.0613e-01, 8.0707e-01,
+                      6.1595e-01, 8.2005e-01, 9.9749e-01, 1.1749e-01,
+                      6.5959e-01, 2.3371e-01, 8.3971e-01, 4.3270e-03,
+                      6.2581e-01, 8.0238e-01, 2.8393e-01, 7.0314e-01,
+                      2.0960e-01, 3.2954e-02, 6.5011e-01, 8.0206e-01,
+                      9.2215e-01, 8.1873e-01, 3.4350e-01, 2.8733e-01,
+                      1.9274e-01, 3.4014e-01, 3.0741e-01, 3.4144e-01,
+                      2.7448e-02, 7.6554e-01, 6.2323e-01, 3.0307e-01,
+                      4.5175e-01, 3.9421e-01, 8.5280e-01, 6.5476e-01,
+                      3.1057e-01, 3.6455e-01, 8.0890e-01, 2.7987e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.7740, 0.9703, 0.1840,  ..., 0.7477, 0.1526, 0.5369])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 11.093939065933228 seconds
+
+[40.33, 40.0, 39.88, 40.14, 39.62, 39.53, 39.86, 39.53, 39.55, 39.82]
+[98.79]
+12.79684853553772
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 366482, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.093939065933228, 'TIME_S_1KI': 0.0302714432521467, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1264.2006668257713, 'W': 98.79}
+[40.33, 40.0, 39.88, 40.14, 39.62, 39.53, 39.86, 39.53, 39.55, 39.82, 40.52, 39.54, 40.01, 39.91, 40.14, 39.87, 40.07, 39.9, 44.97, 39.44]
+722.575
+36.128750000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 366482, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 11.093939065933228, 'TIME_S_1KI': 0.0302714432521467, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1264.2006668257713, 'W': 98.79, 'J_1KI': 3.4495573229402026, 'W_1KI': 0.26956303447372587, 'W_D': 62.66125, 'J_D': 801.866525297463, 'W_D_1KI': 0.17098043014390885, 'J_D_1KI': 0.0004665452331735497}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..c87bd7a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 305580, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.080953121185303, "TIME_S_1KI": 0.032989571049104334, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1230.7398206591606, "W": 98.25, "J_1KI": 4.027553572416914, "W_1KI": 0.32151973296681724, "W_D": 62.38325, "J_D": 781.450889741838, "W_D_1KI": 0.20414703187381372, "J_D_1KI": 0.0006680641137306555}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..9e7563d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.018548250198364258}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    2,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([1572, 8127, 3303,  ..., 3635, 8012, 8701]),
+       values=tensor([0.7029, 0.2681, 0.5472,  ..., 0.1372, 0.6564, 0.9870]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.6228, 0.5154, 0.0077,  ..., 0.6369, 0.2601, 0.0192])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.018548250198364258 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '56609', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.9451327323913574}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4999, 4999, 5000]),
+       col_indices=tensor([3842,  387, 3686,  ..., 4115, 6419, 2917]),
+       values=tensor([0.9231, 0.0215, 0.0697,  ..., 0.2708, 0.2879, 0.7516]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.2403, 0.0214, 0.1380,  ..., 0.6094, 0.2095, 0.9923])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 1.9451327323913574 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '305580', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.080953121185303}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 4999, 4999, 5000]),
+       col_indices=tensor([1481, 9557, 9045,  ...,  186, 1024,  519]),
+       values=tensor([0.0681, 0.8562, 0.9064,  ..., 0.7770, 0.2010, 0.9088]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.5005, 0.6529, 0.0782,  ..., 0.6202, 0.1736, 0.9901])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.080953121185303 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    2,  ..., 4999, 4999, 5000]),
+       col_indices=tensor([1481, 9557, 9045,  ...,  186, 1024,  519]),
+       values=tensor([0.0681, 0.8562, 0.9064,  ..., 0.7770, 0.2010, 0.9088]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.5005, 0.6529, 0.0782,  ..., 0.6202, 0.1736, 0.9901])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.080953121185303 seconds
+
+[40.69, 39.86, 40.65, 39.74, 39.56, 39.52, 39.78, 39.47, 39.45, 39.45]
+[98.25]
+12.52661395072937
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 305580, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.080953121185303, 'TIME_S_1KI': 0.032989571049104334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1230.7398206591606, 'W': 98.25}
+[40.69, 39.86, 40.65, 39.74, 39.56, 39.52, 39.78, 39.47, 39.45, 39.45, 40.13, 40.88, 39.46, 41.14, 39.99, 39.46, 39.46, 39.44, 39.59, 39.5]
+717.335
+35.86675
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 305580, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.080953121185303, 'TIME_S_1KI': 0.032989571049104334, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1230.7398206591606, 'W': 98.25, 'J_1KI': 4.027553572416914, 'W_1KI': 0.32151973296681724, 'W_D': 62.38325, 'J_D': 781.450889741838, 'W_D_1KI': 0.20414703187381372, 'J_D_1KI': 0.0006680641137306555}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..0a007ba
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1273, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.290857076644897, "TIME_S_1KI": 8.083941144261507, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2248.191835269928, "W": 120.44000000000001, "J_1KI": 1766.0580009975868, "W_1KI": 94.61115475255303, "W_D": 84.17750000000001, "J_D": 1571.2983079826834, "W_D_1KI": 66.12529457973291, "J_D_1KI": 51.944457643152326}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..1b9241c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.824350118637085}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       55,      100,  ..., 24999899,
+                            24999953, 25000000]),
+       col_indices=tensor([  1283,  31647,  40047,  ..., 487577, 491974,
+                           492635]),
+       values=tensor([0.2687, 0.0076, 0.0743,  ..., 0.5051, 0.4444, 0.1527]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.2422, 0.7782, 0.2817,  ..., 0.4809, 0.1219, 0.8722])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 0.824350118637085 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1273', '-ss', '500000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.290857076644897}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       45,      101,  ..., 24999883,
+                            24999939, 25000000]),
+       col_indices=tensor([  9313,  23523,  43031,  ..., 488537, 498363,
+                           498593]),
+       values=tensor([0.9134, 0.4019, 0.3601,  ..., 0.2723, 0.3306, 0.0527]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.9993, 0.9766, 0.6194,  ..., 0.0672, 0.4807, 0.1643])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.290857076644897 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       45,      101,  ..., 24999883,
+                            24999939, 25000000]),
+       col_indices=tensor([  9313,  23523,  43031,  ..., 488537, 498363,
+                           498593]),
+       values=tensor([0.9134, 0.4019, 0.3601,  ..., 0.2723, 0.3306, 0.0527]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.9993, 0.9766, 0.6194,  ..., 0.0672, 0.4807, 0.1643])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.290857076644897 seconds
+
+[40.99, 40.04, 40.78, 39.98, 40.28, 39.98, 39.98, 39.94, 40.19, 39.9]
+[120.44]
+18.66648817062378
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1273, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.290857076644897, 'TIME_S_1KI': 8.083941144261507, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2248.191835269928, 'W': 120.44000000000001}
+[40.99, 40.04, 40.78, 39.98, 40.28, 39.98, 39.98, 39.94, 40.19, 39.9, 41.12, 40.02, 40.17, 39.62, 39.57, 39.49, 39.52, 45.4, 39.55, 39.47]
+725.25
+36.2625
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1273, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.290857076644897, 'TIME_S_1KI': 8.083941144261507, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2248.191835269928, 'W': 120.44000000000001, 'J_1KI': 1766.0580009975868, 'W_1KI': 94.61115475255303, 'W_D': 84.17750000000001, 'J_D': 1571.2983079826834, 'W_D_1KI': 66.12529457973291, 'J_D_1KI': 51.944457643152326}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..22e8b8e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 21531, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.381713628768921, "TIME_S_1KI": 0.48217517202029264, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2048.753864378929, "W": 153.79, "J_1KI": 95.15367908499043, "W_1KI": 7.142724443825182, "W_D": 117.79275, "J_D": 1569.2070470012427, "W_D_1KI": 5.4708443639403646, "J_D_1KI": 0.25409151288562376}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..da8bd5c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.08279204368591309}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,      10,  ..., 2499988,
+                            2499995, 2500000]),
+       col_indices=tensor([111354, 133493, 148601,  ..., 214459, 291734,
+                           295580]),
+       values=tensor([0.7692, 0.5972, 0.6345,  ..., 0.2595, 0.9828, 0.2512]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1544, 0.9362, 0.6152,  ..., 0.8648, 0.4518, 0.0330])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 0.08279204368591309 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '12682', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 6.184589385986328}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      12,  ..., 2499985,
+                            2499992, 2500000]),
+       col_indices=tensor([135614, 168986, 215859,  ..., 402290, 443216,
+                           486549]),
+       values=tensor([0.4455, 0.2288, 0.5445,  ..., 0.6029, 0.8332, 0.9959]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.5969, 0.6605, 0.1157,  ..., 0.5750, 0.9019, 0.4949])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 6.184589385986328 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '21531', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.381713628768921}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       6,  ..., 2499988,
+                            2499995, 2500000]),
+       col_indices=tensor([192037, 290494, 298239,  ..., 203209, 269872,
+                           299833]),
+       values=tensor([0.2087, 0.5501, 0.3490,  ..., 0.1907, 0.4204, 0.3032]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3717, 0.6256, 0.6803,  ..., 0.1727, 0.9290, 0.7130])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.381713628768921 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       4,       6,  ..., 2499988,
+                            2499995, 2500000]),
+       col_indices=tensor([192037, 290494, 298239,  ..., 203209, 269872,
+                           299833]),
+       values=tensor([0.2087, 0.5501, 0.3490,  ..., 0.1907, 0.4204, 0.3032]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3717, 0.6256, 0.6803,  ..., 0.1727, 0.9290, 0.7130])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.381713628768921 seconds
+
+[40.84, 39.87, 40.71, 40.09, 39.75, 39.79, 39.67, 39.6, 39.76, 40.21]
+[153.79]
+13.321762561798096
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21531, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.381713628768921, 'TIME_S_1KI': 0.48217517202029264, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2048.753864378929, 'W': 153.79}
+[40.84, 39.87, 40.71, 40.09, 39.75, 39.79, 39.67, 39.6, 39.76, 40.21, 41.58, 40.07, 40.12, 40.08, 39.68, 39.76, 39.59, 39.74, 40.65, 39.4]
+719.9449999999999
+35.997249999999994
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 21531, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.381713628768921, 'TIME_S_1KI': 0.48217517202029264, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2048.753864378929, 'W': 153.79, 'J_1KI': 95.15367908499043, 'W_1KI': 7.142724443825182, 'W_D': 117.79275, 'J_D': 1569.2070470012427, 'W_D_1KI': 5.4708443639403646, 'J_D_1KI': 0.25409151288562376}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..2972d33
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 2288, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.664037466049194, "TIME_S_1KI": 4.660855535860661, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2026.948439025879, "W": 124.48, "J_1KI": 885.9040380357864, "W_1KI": 54.40559440559441, "W_D": 87.53675000000001, "J_D": 1425.389450272322, "W_D_1KI": 38.25906905594406, "J_D_1KI": 16.721621090884643}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..16454d4
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.45888853073120117}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       24,       46,  ..., 12499954,
+                            12499981, 12500000]),
+       col_indices=tensor([ 49072, 112972, 116125,  ..., 361100, 370525,
+                           412609]),
+       values=tensor([0.2354, 0.3643, 0.0075,  ..., 0.8603, 0.9033, 0.4787]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.8534, 0.6179, 0.0838,  ..., 0.4832, 0.1451, 0.6650])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 0.45888853073120117 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '2288', '-ss', '500000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.664037466049194}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       27,       48,  ..., 12499952,
+                            12499972, 12500000]),
+       col_indices=tensor([  3842,   7633,   8971,  ..., 455163, 462741,
+                           476944]),
+       values=tensor([0.8075, 0.4724, 0.8976,  ..., 0.5541, 0.2969, 0.9431]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0930, 0.1654, 0.5776,  ..., 0.1397, 0.2168, 0.6873])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.664037466049194 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       27,       48,  ..., 12499952,
+                            12499972, 12500000]),
+       col_indices=tensor([  3842,   7633,   8971,  ..., 455163, 462741,
+                           476944]),
+       values=tensor([0.8075, 0.4724, 0.8976,  ..., 0.5541, 0.2969, 0.9431]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0930, 0.1654, 0.5776,  ..., 0.1397, 0.2168, 0.6873])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.664037466049194 seconds
+
+[40.58, 39.64, 39.65, 39.68, 39.54, 40.08, 40.01, 55.98, 39.52, 39.5]
+[124.48]
+16.283326148986816
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2288, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.664037466049194, 'TIME_S_1KI': 4.660855535860661, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2026.948439025879, 'W': 124.48}
+[40.58, 39.64, 39.65, 39.68, 39.54, 40.08, 40.01, 55.98, 39.52, 39.5, 40.22, 40.13, 39.62, 39.51, 44.89, 40.37, 40.29, 40.46, 39.51, 39.67]
+738.865
+36.94325
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 2288, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.664037466049194, 'TIME_S_1KI': 4.660855535860661, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2026.948439025879, 'W': 124.48, 'J_1KI': 885.9040380357864, 'W_1KI': 54.40559440559441, 'W_D': 87.53675000000001, 'J_D': 1425.389450272322, 'W_D_1KI': 38.25906905594406, 'J_D_1KI': 16.721621090884643}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..f1d4670
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 94004, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.384105920791626, "TIME_S_1KI": 0.12110235650388947, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1783.8070621061327, "W": 114.34, "J_1KI": 18.975863389920992, "W_1KI": 1.2163312199480873, "W_D": 78.24925, "J_D": 1220.758831157148, "W_D_1KI": 0.8324034083656016, "J_D_1KI": 0.008854978600544674}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..0b0a270
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.04173541069030762}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,      9,  ..., 249994, 249996,
+                            250000]),
+       col_indices=tensor([ 2875, 11250, 41033,  ..., 32140, 46339, 48534]),
+       values=tensor([0.9791, 0.8918, 0.3698,  ..., 0.3708, 0.0646, 0.7857]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.2100, 0.1946, 0.2511,  ..., 0.6374, 0.0985, 0.4430])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.04173541069030762 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '25158', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.8100624084472656}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      2,      7,  ..., 249990, 249993,
+                            250000]),
+       col_indices=tensor([41615, 42906, 15488,  ..., 31340, 31947, 35417]),
+       values=tensor([0.0772, 0.0729, 0.2688,  ..., 0.4463, 0.5032, 0.2162]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.5388, 0.5198, 0.1392,  ..., 0.7254, 0.7688, 0.9922])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 2.8100624084472656 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '94004', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 11.384105920791626}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     13,  ..., 249985, 249990,
+                            250000]),
+       col_indices=tensor([ 2939, 14473, 20084,  ..., 45023, 47616, 49448]),
+       values=tensor([0.6894, 0.8051, 0.8240,  ..., 0.7425, 0.1769, 0.4023]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4931, 0.5539, 0.3107,  ..., 0.8523, 0.9706, 0.6879])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 11.384105920791626 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     13,  ..., 249985, 249990,
+                            250000]),
+       col_indices=tensor([ 2939, 14473, 20084,  ..., 45023, 47616, 49448]),
+       values=tensor([0.6894, 0.8051, 0.8240,  ..., 0.7425, 0.1769, 0.4023]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4931, 0.5539, 0.3107,  ..., 0.8523, 0.9706, 0.6879])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 11.384105920791626 seconds
+
+[40.36, 39.91, 39.67, 39.83, 39.58, 39.72, 40.16, 39.72, 39.91, 39.98]
+[114.34]
+15.600901365280151
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 94004, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.384105920791626, 'TIME_S_1KI': 0.12110235650388947, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1783.8070621061327, 'W': 114.34}
+[40.36, 39.91, 39.67, 39.83, 39.58, 39.72, 40.16, 39.72, 39.91, 39.98, 40.89, 39.87, 39.99, 39.51, 39.5, 39.56, 39.75, 45.01, 39.69, 39.64]
+721.815
+36.09075
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 94004, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 11.384105920791626, 'TIME_S_1KI': 0.12110235650388947, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1783.8070621061327, 'W': 114.34, 'J_1KI': 18.975863389920992, 'W_1KI': 1.2163312199480873, 'W_D': 78.24925, 'J_D': 1220.758831157148, 'W_D_1KI': 0.8324034083656016, 'J_D_1KI': 0.008854978600544674}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..ca129d7
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 46418, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.356630086898804, "TIME_S_1KI": 0.22311668074666732, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1954.5975055408476, "W": 147.82, "J_1KI": 42.10861100307742, "W_1KI": 3.1845404799862123, "W_D": 111.69725, "J_D": 1476.952822525859, "W_D_1KI": 2.4063348270067646, "J_D_1KI": 0.051840553815476}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..0dda590
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.06997370719909668}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      52,     102,  ..., 2499883,
+                            2499945, 2500000]),
+       col_indices=tensor([  266,   347,  3014,  ..., 46062, 47055, 47354]),
+       values=tensor([0.8937, 0.7241, 0.1967,  ..., 0.6923, 0.3348, 0.4624]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3743, 0.1265, 0.3703,  ..., 0.6234, 0.9781, 0.6963])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.06997370719909668 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '15005', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 3.3941891193389893}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      54,      97,  ..., 2499911,
+                            2499955, 2500000]),
+       col_indices=tensor([ 2370,  4930,  5051,  ..., 41423, 44524, 44646]),
+       values=tensor([0.0412, 0.5807, 0.8088,  ..., 0.8046, 0.7553, 0.5801]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6465, 0.4915, 0.1151,  ..., 0.6682, 0.4745, 0.9594])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 3.3941891193389893 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '46418', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.356630086898804}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      46,      86,  ..., 2499896,
+                            2499949, 2500000]),
+       col_indices=tensor([ 1254,  3268,  3363,  ..., 48004, 48805, 49373]),
+       values=tensor([0.4618, 0.8696, 0.7740,  ..., 0.9354, 0.3130, 0.0156]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3463, 0.0749, 0.0037,  ..., 0.8223, 0.0446, 0.2738])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.356630086898804 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      46,      86,  ..., 2499896,
+                            2499949, 2500000]),
+       col_indices=tensor([ 1254,  3268,  3363,  ..., 48004, 48805, 49373]),
+       values=tensor([0.4618, 0.8696, 0.7740,  ..., 0.9354, 0.3130, 0.0156]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.3463, 0.0749, 0.0037,  ..., 0.8223, 0.0446, 0.2738])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.356630086898804 seconds
+
+[40.8, 41.43, 39.77, 40.05, 40.2, 40.01, 40.25, 39.8, 39.91, 39.83]
+[147.82]
+13.222821712493896
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46418, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.356630086898804, 'TIME_S_1KI': 0.22311668074666732, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1954.5975055408476, 'W': 147.82}
+[40.8, 41.43, 39.77, 40.05, 40.2, 40.01, 40.25, 39.8, 39.91, 39.83, 40.83, 39.7, 40.25, 40.38, 40.19, 39.67, 41.03, 39.72, 39.6, 39.53]
+722.4549999999999
+36.122749999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 46418, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.356630086898804, 'TIME_S_1KI': 0.22311668074666732, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1954.5975055408476, 'W': 147.82, 'J_1KI': 42.10861100307742, 'W_1KI': 3.1845404799862123, 'W_D': 111.69725, 'J_D': 1476.952822525859, 'W_D_1KI': 2.4063348270067646, 'J_D_1KI': 0.051840553815476}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..ce3aa00
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 1681, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.72816252708435, "TIME_S_1KI": 6.382012211233998, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2210.374014186859, "W": 116.29999999999998, "J_1KI": 1314.9161297958708, "W_1KI": 69.18500892325996, "W_D": 80.01049999999998, "J_D": 1520.6631991581912, "W_D_1KI": 47.596966091612124, "J_D_1KI": 28.314673463183894}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..fde54b7
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.6244547367095947}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      524,     1064,  ..., 24999000,
+                            24999502, 25000000]),
+       col_indices=tensor([   60,    76,   165,  ..., 49872, 49944, 49977]),
+       values=tensor([0.3464, 0.5127, 0.2524,  ..., 0.4585, 0.6152, 0.8409]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3169, 0.2467, 0.7317,  ..., 0.4966, 0.9013, 0.2021])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 0.6244547367095947 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '1681', '-ss', '50000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.72816252708435}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      503,      984,  ..., 24998995,
+                            24999487, 25000000]),
+       col_indices=tensor([   80,   111,   167,  ..., 49695, 49904, 49943]),
+       values=tensor([0.9741, 0.4832, 0.1000,  ..., 0.9253, 0.4991, 0.7681]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.5734, 0.0323, 0.0030,  ..., 0.5787, 0.7337, 0.7260])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.72816252708435 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      503,      984,  ..., 24998995,
+                            24999487, 25000000]),
+       col_indices=tensor([   80,   111,   167,  ..., 49695, 49904, 49943]),
+       values=tensor([0.9741, 0.4832, 0.1000,  ..., 0.9253, 0.4991, 0.7681]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.5734, 0.0323, 0.0030,  ..., 0.5787, 0.7337, 0.7260])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.72816252708435 seconds
+
+[40.52, 40.33, 46.14, 39.67, 40.04, 40.05, 39.67, 39.99, 39.82, 39.93]
+[116.3]
+19.0057954788208
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1681, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.72816252708435, 'TIME_S_1KI': 6.382012211233998, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2210.374014186859, 'W': 116.29999999999998}
+[40.52, 40.33, 46.14, 39.67, 40.04, 40.05, 39.67, 39.99, 39.82, 39.93, 42.61, 39.87, 39.87, 39.75, 39.67, 39.77, 39.75, 39.54, 40.39, 39.88]
+725.7900000000001
+36.289500000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 1681, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.72816252708435, 'TIME_S_1KI': 6.382012211233998, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2210.374014186859, 'W': 116.29999999999998, 'J_1KI': 1314.9161297958708, 'W_1KI': 69.18500892325996, 'W_D': 80.01049999999998, 'J_D': 1520.6631991581912, 'W_D_1KI': 47.596966091612124, 'J_D_1KI': 28.314673463183894}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..73ac2d2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 128261, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.553161382675171, "TIME_S_1KI": 0.08227880168309283, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1360.8756747579575, "W": 104.26, "J_1KI": 10.610206335191194, "W_1KI": 0.8128737496199158, "W_D": 68.19500000000001, "J_D": 890.1296435844899, "W_D_1KI": 0.5316892898075019, "J_D_1KI": 0.004145369908292481}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..b70442e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.0555570125579834}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([42051, 34515, 37611,  ..., 41473, 46289, 26191]),
+       values=tensor([0.0144, 0.4378, 0.1715,  ..., 0.0832, 0.5030, 0.6687]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1124, 0.4102, 0.7912,  ..., 0.3553, 0.2259, 0.3847])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.0555570125579834 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '18899', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.547147274017334}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 24999, 25000, 25000]),
+       col_indices=tensor([ 9684, 40954, 42907,  ..., 26506, 37971, 35337]),
+       values=tensor([0.0766, 0.5354, 0.2778,  ..., 0.4912, 0.6494, 0.7856]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.2325, 0.6288, 0.8060,  ..., 0.4059, 0.0257, 0.5351])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 1.547147274017334 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '128261', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.553161382675171}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([43490, 45422, 41208,  ..., 48729, 34812, 29106]),
+       values=tensor([0.6729, 0.4582, 0.1719,  ..., 0.9792, 0.1938, 0.4197]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3715, 0.8721, 0.7070,  ..., 0.0207, 0.1985, 0.3006])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.553161382675171 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     2,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([43490, 45422, 41208,  ..., 48729, 34812, 29106]),
+       values=tensor([0.6729, 0.4582, 0.1719,  ..., 0.9792, 0.1938, 0.4197]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3715, 0.8721, 0.7070,  ..., 0.0207, 0.1985, 0.3006])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.553161382675171 seconds
+
+[42.38, 39.84, 39.75, 39.86, 39.86, 40.05, 40.23, 39.88, 39.93, 39.73]
+[104.26]
+13.052711248397827
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 128261, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.553161382675171, 'TIME_S_1KI': 0.08227880168309283, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1360.8756747579575, 'W': 104.26}
+[42.38, 39.84, 39.75, 39.86, 39.86, 40.05, 40.23, 39.88, 39.93, 39.73, 40.22, 39.71, 39.72, 39.44, 39.86, 40.03, 39.57, 39.83, 40.02, 45.11]
+721.3
+36.065
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 128261, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.553161382675171, 'TIME_S_1KI': 0.08227880168309283, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1360.8756747579575, 'W': 104.26, 'J_1KI': 10.610206335191194, 'W_1KI': 0.8128737496199158, 'W_D': 68.19500000000001, 'J_D': 890.1296435844899, 'W_D_1KI': 0.5316892898075019, 'J_D_1KI': 0.004145369908292481}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..b465c9c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 110115, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.965436697006226, "TIME_S_1KI": 0.1086630949190049, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1446.9478559374809, "W": 111.69, "J_1KI": 13.14033379591773, "W_1KI": 1.0143032284429914, "W_D": 75.71975, "J_D": 980.9520092633368, "W_D_1KI": 0.6876424646959997, "J_D_1KI": 0.006244766514062568}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..b1f3c1f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.05198168754577637}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ..., 124998, 124999,
+                            125000]),
+       col_indices=tensor([  927,  8914,  5646,  ..., 41839,  2622, 37662]),
+       values=tensor([0.2093, 0.3505, 0.4434,  ..., 0.7585, 0.2953, 0.8139]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.7158, 0.3261, 0.8838,  ..., 0.1644, 0.9864, 0.1779])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.05198168754577637 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '20199', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.9260566234588623}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,      9,  ..., 124993, 124995,
+                            125000]),
+       col_indices=tensor([ 5280, 12669, 18309,  ..., 32915, 33761, 44585]),
+       values=tensor([0.1104, 0.6442, 0.1166,  ..., 0.0611, 0.5204, 0.6774]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.3981, 0.9235, 0.5295,  ..., 0.9056, 0.3690, 0.2596])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 1.9260566234588623 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '110115', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 11.965436697006226}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,      7,  ..., 124990, 124994,
+                            125000]),
+       col_indices=tensor([  332, 15911, 38702,  ..., 27905, 36936, 47310]),
+       values=tensor([0.9967, 0.4995, 0.1475,  ..., 0.0565, 0.7404, 0.0608]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.3103, 0.3240, 0.6987,  ..., 0.1758, 0.7445, 0.7079])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 11.965436697006226 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,      7,  ..., 124990, 124994,
+                            125000]),
+       col_indices=tensor([  332, 15911, 38702,  ..., 27905, 36936, 47310]),
+       values=tensor([0.9967, 0.4995, 0.1475,  ..., 0.0565, 0.7404, 0.0608]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.3103, 0.3240, 0.6987,  ..., 0.1758, 0.7445, 0.7079])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 11.965436697006226 seconds
+
+[40.59, 40.33, 40.2, 40.19, 39.75, 39.57, 39.56, 39.52, 39.58, 39.6]
+[111.69]
+12.955034971237183
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 110115, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.965436697006226, 'TIME_S_1KI': 0.1086630949190049, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1446.9478559374809, 'W': 111.69}
+[40.59, 40.33, 40.2, 40.19, 39.75, 39.57, 39.56, 39.52, 39.58, 39.6, 40.22, 41.59, 39.67, 40.02, 39.96, 39.85, 39.52, 40.16, 39.94, 39.58]
+719.405
+35.97025
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 110115, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 11.965436697006226, 'TIME_S_1KI': 0.1086630949190049, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1446.9478559374809, 'W': 111.69, 'J_1KI': 13.14033379591773, 'W_1KI': 1.0143032284429914, 'W_D': 75.71975, 'J_D': 980.9520092633368, 'W_D_1KI': 0.6876424646959997, 'J_D_1KI': 0.006244766514062568}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..2c10823
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 447788, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.585598468780518, "TIME_S_1KI": 0.023639754680296294, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1210.6447257900238, "W": 96.76, "J_1KI": 2.7036113647306848, "W_1KI": 0.21608439708076144, "W_D": 60.779250000000005, "J_D": 760.4596780691744, "W_D_1KI": 0.13573219916567664, "J_D_1KI": 0.00030311709819306603}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..38edffa
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.01688361167907715}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 2499, 2499, 2500]),
+       col_indices=tensor([2619, 4724, 4043,  ...,  721, 4005, 3452]),
+       values=tensor([0.3560, 0.4737, 0.9490,  ..., 0.6650, 0.5511, 0.5102]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.9035, 0.6347, 0.7264,  ..., 0.8885, 0.4271, 0.9746])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.01688361167907715 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '62190', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.4582650661468506}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([ 785, 2078,  964,  ..., 3093, 2409, 4914]),
+       values=tensor([0.2674, 0.7127, 0.0446,  ..., 0.5887, 0.3242, 0.2984]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.2053, 0.2435, 0.6452,  ..., 0.2463, 0.9693, 0.2980])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 1.4582650661468506 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '447788', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.585598468780518}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([3170,   56,  953,  ..., 2101, 4088, 4138]),
+       values=tensor([0.7441, 0.4324, 0.6982,  ..., 0.2565, 0.3946, 0.1156]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7808, 0.5836, 0.6876,  ..., 0.2450, 0.1275, 0.2911])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.585598468780518 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([3170,   56,  953,  ..., 2101, 4088, 4138]),
+       values=tensor([0.7441, 0.4324, 0.6982,  ..., 0.2565, 0.3946, 0.1156]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.7808, 0.5836, 0.6876,  ..., 0.2450, 0.1275, 0.2911])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.585598468780518 seconds
+
+[40.68, 39.36, 44.27, 39.07, 39.18, 39.33, 39.3, 39.56, 39.47, 39.52]
+[96.76]
+12.511830568313599
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 447788, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.585598468780518, 'TIME_S_1KI': 0.023639754680296294, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1210.6447257900238, 'W': 96.76}
+[40.68, 39.36, 44.27, 39.07, 39.18, 39.33, 39.3, 39.56, 39.47, 39.52, 45.98, 39.49, 39.82, 39.41, 39.22, 39.42, 41.09, 39.62, 39.26, 39.31]
+719.615
+35.98075
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 447788, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.585598468780518, 'TIME_S_1KI': 0.023639754680296294, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1210.6447257900238, 'W': 96.76, 'J_1KI': 2.7036113647306848, 'W_1KI': 0.21608439708076144, 'W_D': 60.779250000000005, 'J_D': 760.4596780691744, 'W_D_1KI': 0.13573219916567664, 'J_D_1KI': 0.00030311709819306603}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..f972143
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 251242, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.495816707611084, "TIME_S_1KI": 0.041775725028502735, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1274.650104198456, "W": 98.84, "J_1KI": 5.073395786526361, "W_1KI": 0.393405561172097, "W_D": 63.200750000000006, "J_D": 815.0429236434699, "W_D_1KI": 0.25155328328862214, "J_D_1KI": 0.0010012389779122206}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..489fd07
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.020148515701293945}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     7,    10,  ..., 24991, 24995, 25000]),
+       col_indices=tensor([ 119,  931, 3406,  ..., 3461, 3840, 3846]),
+       values=tensor([0.6773, 0.1678, 0.7190,  ..., 0.1084, 0.6735, 0.7339]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0928, 0.4745, 0.9490,  ..., 0.8279, 0.2614, 0.8062])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.020148515701293945 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52113', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.177922248840332}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     6,    11,  ..., 24987, 24992, 25000]),
+       col_indices=tensor([1164, 1818, 2007,  ..., 3806, 4515, 4674]),
+       values=tensor([0.7293, 0.4677, 0.0557,  ..., 0.9608, 0.8022, 0.8772]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.5594, 0.0674, 0.3236,  ..., 0.9684, 0.1982, 0.3579])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 2.177922248840332 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '251242', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.495816707611084}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     8,  ..., 24992, 24997, 25000]),
+       col_indices=tensor([1486, 3242, 3522,  ..., 1754, 2627, 4146]),
+       values=tensor([0.1836, 0.4006, 0.2197,  ..., 0.0536, 0.9699, 0.8761]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8727, 0.2072, 0.9866,  ..., 0.7187, 0.7974, 0.6886])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.495816707611084 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     8,  ..., 24992, 24997, 25000]),
+       col_indices=tensor([1486, 3242, 3522,  ..., 1754, 2627, 4146]),
+       values=tensor([0.1836, 0.4006, 0.2197,  ..., 0.0536, 0.9699, 0.8761]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8727, 0.2072, 0.9866,  ..., 0.7187, 0.7974, 0.6886])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.495816707611084 seconds
+
+[40.75, 39.26, 39.42, 39.22, 39.47, 40.61, 39.23, 39.53, 39.63, 39.38]
+[98.84]
+12.896095752716064
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 251242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.495816707611084, 'TIME_S_1KI': 0.041775725028502735, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.650104198456, 'W': 98.84}
+[40.75, 39.26, 39.42, 39.22, 39.47, 40.61, 39.23, 39.53, 39.63, 39.38, 40.47, 39.12, 40.14, 40.63, 39.59, 39.14, 39.11, 39.22, 39.39, 39.55]
+712.785
+35.63925
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 251242, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.495816707611084, 'TIME_S_1KI': 0.041775725028502735, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1274.650104198456, 'W': 98.84, 'J_1KI': 5.073395786526361, 'W_1KI': 0.393405561172097, 'W_D': 63.200750000000006, 'J_D': 815.0429236434699, 'W_D_1KI': 0.25155328328862214, 'J_D_1KI': 0.0010012389779122206}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..7edf7f8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 151851, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.677409887313843, "TIME_S_1KI": 0.07031504492768466, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1443.7484180927277, "W": 117.47, "J_1KI": 9.507664869462351, "W_1KI": 0.7735872664651534, "W_D": 81.37549999999999, "J_D": 1000.1340716481208, "W_D_1KI": 0.5358904452390829, "J_D_1KI": 0.0035290544365139706}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..f6f6dab
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.027439117431640625}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     55,    107,  ..., 249902, 249955,
+                            250000]),
+       col_indices=tensor([  26,  155,  397,  ..., 4652, 4756, 4760]),
+       values=tensor([0.9134, 0.8993, 0.8423,  ..., 0.2444, 0.0288, 0.7023]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3636, 0.7288, 0.4529,  ..., 0.6147, 0.2907, 0.3015])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.027439117431640625 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '38266', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 2.645953893661499}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     49,     92,  ..., 249897, 249949,
+                            250000]),
+       col_indices=tensor([ 507,  568,  655,  ..., 4839, 4844, 4959]),
+       values=tensor([0.4543, 0.3787, 0.6932,  ..., 0.4487, 0.3087, 0.1431]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.4167, 0.3931, 0.0326,  ..., 0.8288, 0.4472, 0.1506])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 2.645953893661499 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '151851', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.677409887313843}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     62,    123,  ..., 249890, 249948,
+                            250000]),
+       col_indices=tensor([  53,   60,  101,  ..., 4781, 4787, 4941]),
+       values=tensor([0.8546, 0.9316, 0.6470,  ..., 0.1212, 0.6179, 0.4318]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7208, 0.6556, 0.2590,  ..., 0.8294, 0.6979, 0.2347])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.677409887313843 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     62,    123,  ..., 249890, 249948,
+                            250000]),
+       col_indices=tensor([  53,   60,  101,  ..., 4781, 4787, 4941]),
+       values=tensor([0.8546, 0.9316, 0.6470,  ..., 0.1212, 0.6179, 0.4318]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7208, 0.6556, 0.2590,  ..., 0.8294, 0.6979, 0.2347])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.677409887313843 seconds
+
+[39.83, 39.62, 39.22, 39.2, 39.58, 39.98, 44.8, 39.52, 39.58, 39.44]
+[117.47]
+12.290358543395996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 151851, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.677409887313843, 'TIME_S_1KI': 0.07031504492768466, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.7484180927277, 'W': 117.47}
+[39.83, 39.62, 39.22, 39.2, 39.58, 39.98, 44.8, 39.52, 39.58, 39.44, 40.11, 39.32, 39.47, 44.63, 39.38, 39.24, 39.42, 39.88, 39.66, 39.4]
+721.8900000000001
+36.094500000000004
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 151851, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.677409887313843, 'TIME_S_1KI': 0.07031504492768466, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1443.7484180927277, 'W': 117.47, 'J_1KI': 9.507664869462351, 'W_1KI': 0.7735872664651534, 'W_D': 81.37549999999999, 'J_D': 1000.1340716481208, 'W_D_1KI': 0.5358904452390829, 'J_D_1KI': 0.0035290544365139706}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..0dcc054
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 91742, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.460400104522705, "TIME_S_1KI": 0.11401975218027409, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1797.7089305996894, "W": 131.45, "J_1KI": 19.595266405786766, "W_1KI": 1.4328224804342613, "W_D": 95.54724999999999, "J_D": 1306.7032683091759, "W_D_1KI": 1.0414777310283185, "J_D_1KI": 0.011352245765607012}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..ee62fae
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03591585159301758}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     282,     523,  ..., 1249534,
+                            1249768, 1250000]),
+       col_indices=tensor([  14,   20,   65,  ..., 4981, 4988, 4994]),
+       values=tensor([0.5427, 0.7626, 0.3688,  ..., 0.1462, 0.4395, 0.5084]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.3604, 0.6234, 0.1526,  ..., 0.4035, 0.4868, 0.5530])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 0.03591585159301758 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '29235', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 3.345984697341919}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     247,     522,  ..., 1249509,
+                            1249766, 1250000]),
+       col_indices=tensor([  11,   41,   47,  ..., 4983, 4993, 4996]),
+       values=tensor([0.4860, 0.1371, 0.2214,  ..., 0.6634, 0.1469, 0.9637]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.5776, 0.4725, 0.0368,  ..., 0.6036, 0.3775, 0.0011])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 3.345984697341919 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '91742', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.460400104522705}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     245,     499,  ..., 1249503,
+                            1249746, 1250000]),
+       col_indices=tensor([  11,   13,   56,  ..., 4961, 4967, 4994]),
+       values=tensor([0.1643, 0.9353, 0.3976,  ..., 0.1683, 0.6963, 0.8462]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6649, 0.4073, 0.4211,  ..., 0.1078, 0.2188, 0.0388])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.460400104522705 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     245,     499,  ..., 1249503,
+                            1249746, 1250000]),
+       col_indices=tensor([  11,   13,   56,  ..., 4961, 4967, 4994]),
+       values=tensor([0.1643, 0.9353, 0.3976,  ..., 0.1683, 0.6963, 0.8462]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.6649, 0.4073, 0.4211,  ..., 0.1078, 0.2188, 0.0388])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.460400104522705 seconds
+
+[40.58, 39.92, 39.93, 39.37, 39.59, 39.9, 39.71, 40.07, 40.76, 39.67]
+[131.45]
+13.675990343093872
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91742, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.460400104522705, 'TIME_S_1KI': 0.11401975218027409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1797.7089305996894, 'W': 131.45}
+[40.58, 39.92, 39.93, 39.37, 39.59, 39.9, 39.71, 40.07, 40.76, 39.67, 40.34, 39.45, 39.48, 40.31, 39.86, 39.84, 39.9, 39.88, 39.9, 39.78]
+718.055
+35.90275
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 91742, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.460400104522705, 'TIME_S_1KI': 0.11401975218027409, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1797.7089305996894, 'W': 131.45, 'J_1KI': 19.595266405786766, 'W_1KI': 1.4328224804342613, 'W_D': 95.54724999999999, 'J_D': 1306.7032683091759, 'W_D_1KI': 1.0414777310283185, 'J_D_1KI': 0.011352245765607012}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..44fa72d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 52297, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.236442565917969, "TIME_S_1KI": 0.195736706998833, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1815.1719595336915, "W": 138.62, "J_1KI": 34.70891178334688, "W_1KI": 2.650630055261296, "W_D": 102.202, "J_D": 1338.293208831787, "W_D_1KI": 1.9542612386943803, "J_D_1KI": 0.0373685151862321}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..4fe4648
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.053244590759277344}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     487,     976,  ..., 2498986,
+                            2499481, 2500000]),
+       col_indices=tensor([  13,   19,   40,  ..., 4975, 4977, 4981]),
+       values=tensor([0.0276, 0.4992, 0.8339,  ..., 0.1235, 0.3053, 0.8819]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6982, 0.6943, 0.5654,  ..., 0.0343, 0.1924, 0.4615])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 0.053244590759277344 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19720', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 3.959235429763794}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     500,     997,  ..., 2499001,
+                            2499525, 2500000]),
+       col_indices=tensor([  22,   31,   36,  ..., 4976, 4979, 4990]),
+       values=tensor([0.9139, 0.4529, 0.5623,  ..., 0.7413, 0.5022, 0.1210]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.1909, 0.5057, 0.7269,  ..., 0.6307, 0.9165, 0.6325])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 3.959235429763794 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '52297', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.236442565917969}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     495,     967,  ..., 2499009,
+                            2499501, 2500000]),
+       col_indices=tensor([   2,    3,   29,  ..., 4974, 4984, 4998]),
+       values=tensor([0.7947, 0.6825, 0.2906,  ..., 0.1208, 0.9049, 0.2265]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7067, 0.8426, 0.8818,  ..., 0.1022, 0.5608, 0.1343])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.236442565917969 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     495,     967,  ..., 2499009,
+                            2499501, 2500000]),
+       col_indices=tensor([   2,    3,   29,  ..., 4974, 4984, 4998]),
+       values=tensor([0.7947, 0.6825, 0.2906,  ..., 0.1208, 0.9049, 0.2265]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7067, 0.8426, 0.8818,  ..., 0.1022, 0.5608, 0.1343])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.236442565917969 seconds
+
+[40.97, 39.52, 39.62, 39.4, 39.87, 45.09, 39.68, 39.56, 39.77, 39.67]
+[138.62]
+13.094589233398438
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52297, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.236442565917969, 'TIME_S_1KI': 0.195736706998833, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1815.1719595336915, 'W': 138.62}
+[40.97, 39.52, 39.62, 39.4, 39.87, 45.09, 39.68, 39.56, 39.77, 39.67, 41.69, 43.46, 41.5, 39.76, 39.97, 39.88, 39.62, 39.85, 40.84, 39.61]
+728.3600000000001
+36.418000000000006
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 52297, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.236442565917969, 'TIME_S_1KI': 0.195736706998833, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1815.1719595336915, 'W': 138.62, 'J_1KI': 34.70891178334688, 'W_1KI': 2.650630055261296, 'W_D': 102.202, 'J_D': 1338.293208831787, 'W_D_1KI': 1.9542612386943803, 'J_D_1KI': 0.0373685151862321}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..c26577c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 28289, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.465468645095825, "TIME_S_1KI": 0.36994834193841514, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1925.348158209324, "W": 138.77, "J_1KI": 68.05995822437428, "W_1KI": 4.905440277139524, "W_D": 102.84225, "J_D": 1426.8727867954374, "W_D_1KI": 3.6354148255505674, "J_D_1KI": 0.1285098386493184}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..4cf3fef
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.07305312156677246}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1021,    1969,  ..., 4997949,
+                            4999001, 5000000]),
+       col_indices=tensor([   1,    7,   18,  ..., 4990, 4995, 4998]),
+       values=tensor([0.6231, 0.9951, 0.8252,  ..., 0.9756, 0.2344, 0.2983]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.0355, 0.1933, 0.9788,  ..., 0.6197, 0.2365, 0.0393])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 0.07305312156677246 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '14373', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 5.334789037704468}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1024,    2040,  ..., 4998012,
+                            4999011, 5000000]),
+       col_indices=tensor([   1,    7,   11,  ..., 4993, 4994, 4998]),
+       values=tensor([0.0452, 0.7491, 0.1728,  ..., 0.4616, 0.1426, 0.7347]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.8794, 0.6343, 0.4463,  ..., 0.6355, 0.8597, 0.5087])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 5.334789037704468 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '28289', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.465468645095825}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     999,    2016,  ..., 4997966,
+                            4998976, 5000000]),
+       col_indices=tensor([   2,    9,   14,  ..., 4993, 4994, 4995]),
+       values=tensor([0.1135, 0.5000, 0.4923,  ..., 0.1880, 0.5290, 0.9229]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.0610, 0.8156, 0.2755,  ..., 0.7165, 0.9008, 0.9624])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.465468645095825 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     999,    2016,  ..., 4997966,
+                            4998976, 5000000]),
+       col_indices=tensor([   2,    9,   14,  ..., 4993, 4994, 4995]),
+       values=tensor([0.1135, 0.5000, 0.4923,  ..., 0.1880, 0.5290, 0.9229]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.0610, 0.8156, 0.2755,  ..., 0.7165, 0.9008, 0.9624])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.465468645095825 seconds
+
+[40.8, 40.7, 39.71, 40.23, 39.76, 39.64, 39.79, 39.68, 39.73, 39.72]
+[138.77]
+13.874383211135864
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28289, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.465468645095825, 'TIME_S_1KI': 0.36994834193841514, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1925.348158209324, 'W': 138.77}
+[40.8, 40.7, 39.71, 40.23, 39.76, 39.64, 39.79, 39.68, 39.73, 39.72, 40.37, 39.65, 40.3, 39.61, 40.3, 40.15, 39.64, 39.78, 39.65, 39.58]
+718.555
+35.927749999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 28289, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.465468645095825, 'TIME_S_1KI': 0.36994834193841514, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1925.348158209324, 'W': 138.77, 'J_1KI': 68.05995822437428, 'W_1KI': 4.905440277139524, 'W_D': 102.84225, 'J_D': 1426.8727867954374, 'W_D_1KI': 3.6354148255505674, 'J_D_1KI': 0.1285098386493184}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..edb21c0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 19365, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.708929538726807, "TIME_S_1KI": 0.5530043655423086, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1994.3257465744018, "W": 137.56, "J_1KI": 102.98609587267761, "W_1KI": 7.103537309579138, "W_D": 101.59625, "J_D": 1472.9283013260365, "W_D_1KI": 5.246385231087013, "J_D_1KI": 0.270921003412704}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..4cf90e5
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.0932457447052002}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1472,    2953,  ..., 7496947,
+                            7498464, 7500000]),
+       col_indices=tensor([   7,    9,   10,  ..., 4989, 4991, 4994]),
+       values=tensor([0.9868, 0.9731, 0.3711,  ..., 0.9277, 0.6596, 0.4560]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.4150, 0.1407, 0.7534,  ..., 0.5098, 0.0887, 0.6433])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 0.0932457447052002 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '11260', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 6.105090618133545}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1440,    2968,  ..., 7497040,
+                            7498552, 7500000]),
+       col_indices=tensor([   4,   15,   16,  ..., 4993, 4997, 4998]),
+       values=tensor([0.0079, 0.6033, 0.5837,  ..., 0.4070, 0.1537, 0.2862]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.9942, 0.8351, 0.2634,  ..., 0.1203, 0.3761, 0.2393])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 6.105090618133545 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '19365', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.708929538726807}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1483,    3029,  ..., 7497007,
+                            7498484, 7500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4991, 4992, 4999]),
+       values=tensor([0.2037, 0.3378, 0.5245,  ..., 0.5597, 0.6700, 0.6684]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.7739, 0.8705, 0.9400,  ..., 0.4166, 0.9328, 0.4141])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.708929538726807 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1483,    3029,  ..., 7497007,
+                            7498484, 7500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4991, 4992, 4999]),
+       values=tensor([0.2037, 0.3378, 0.5245,  ..., 0.5597, 0.6700, 0.6684]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.7739, 0.8705, 0.9400,  ..., 0.4166, 0.9328, 0.4141])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.708929538726807 seconds
+
+[42.29, 39.85, 39.72, 39.53, 39.58, 40.0, 40.36, 39.5, 39.8, 40.06]
+[137.56]
+14.4978609085083
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19365, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.708929538726807, 'TIME_S_1KI': 0.5530043655423086, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1994.3257465744018, 'W': 137.56}
+[42.29, 39.85, 39.72, 39.53, 39.58, 40.0, 40.36, 39.5, 39.8, 40.06, 40.39, 40.11, 40.04, 39.88, 41.07, 39.92, 39.57, 39.46, 39.73, 39.57]
+719.275
+35.96375
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 19365, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.708929538726807, 'TIME_S_1KI': 0.5530043655423086, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1994.3257465744018, 'W': 137.56, 'J_1KI': 102.98609587267761, 'W_1KI': 7.103537309579138, 'W_D': 101.59625, 'J_D': 1472.9283013260365, 'W_D_1KI': 5.246385231087013, 'J_D_1KI': 0.270921003412704}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..d0ddde8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 4054, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.937983989715576, "TIME_S_1KI": 2.6980720250901764, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1733.5753089761736, "W": 118.18, "J_1KI": 427.62094449338275, "W_1KI": 29.15145535273804, "W_D": 74.48075000000001, "J_D": 1092.5536401593092, "W_D_1KI": 18.37216329551061, "J_D_1KI": 4.531860704368675}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..fbc9c8c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.25896668434143066}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1967,     3942,  ...,  9996007,
+                             9997987, 10000000]),
+       col_indices=tensor([   0,    1,    3,  ..., 4989, 4995, 4996]),
+       values=tensor([0.9037, 0.0824, 0.8127,  ..., 0.2074, 0.6033, 0.5497]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6227, 0.6614, 0.9902,  ..., 0.2660, 0.9614, 0.3260])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 0.25896668434143066 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '4054', '-ss', '5000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.937983989715576}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2032,     3978,  ...,  9995983,
+                             9998000, 10000000]),
+       col_indices=tensor([   1,    2,    3,  ..., 4988, 4993, 4994]),
+       values=tensor([0.2457, 0.5907, 0.9941,  ..., 0.3357, 0.2301, 0.2269]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2230, 0.0807, 0.5998,  ..., 0.1430, 0.1498, 0.1360])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.937983989715576 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2032,     3978,  ...,  9995983,
+                             9998000, 10000000]),
+       col_indices=tensor([   1,    2,    3,  ..., 4988, 4993, 4994]),
+       values=tensor([0.2457, 0.5907, 0.9941,  ..., 0.3357, 0.2301, 0.2269]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2230, 0.0807, 0.5998,  ..., 0.1430, 0.1498, 0.1360])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.937983989715576 seconds
+
+[40.89, 40.0, 39.72, 39.6, 39.91, 39.64, 39.68, 39.81, 39.88, 45.41]
+[118.18]
+14.66893982887268
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4054, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.937983989715576, 'TIME_S_1KI': 2.6980720250901764, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1733.5753089761736, 'W': 118.18}
+[40.89, 40.0, 39.72, 39.6, 39.91, 39.64, 39.68, 39.81, 39.88, 45.41, 40.78, 40.13, 44.65, 73.6, 81.22, 73.52, 67.52, 42.65, 48.49, 40.85]
+873.9849999999999
+43.69924999999999
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 4054, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.937983989715576, 'TIME_S_1KI': 2.6980720250901764, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1733.5753089761736, 'W': 118.18, 'J_1KI': 427.62094449338275, 'W_1KI': 29.15145535273804, 'W_D': 74.48075000000001, 'J_D': 1092.5536401593092, 'W_D_1KI': 18.37216329551061, 'J_D_1KI': 4.531860704368675}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..a25c6eb
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 3758, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.1781005859375, "TIME_S_1KI": 2.708382274065327, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1956.303444838524, "W": 122.9, "J_1KI": 520.570368504131, "W_1KI": 32.70356572645024, "W_D": 86.40525000000001, "J_D": 1375.3855836219193, "W_D_1KI": 22.992349654071315, "J_D_1KI": 6.118240993632601}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..fd072be
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.29501914978027344}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2490,     5019,  ..., 12495032,
+                            12497514, 12500000]),
+       col_indices=tensor([   0,    1,    2,  ..., 4992, 4993, 4994]),
+       values=tensor([0.4521, 0.6419, 0.1807,  ..., 0.6429, 0.2936, 0.1963]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.6822, 0.2314, 0.5095,  ..., 0.2635, 0.2792, 0.8048])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 0.29501914978027344 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3559', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.942713260650635}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2478,     4936,  ..., 12495093,
+                            12497610, 12500000]),
+       col_indices=tensor([   1,    2,    3,  ..., 4992, 4995, 4998]),
+       values=tensor([0.6608, 0.6509, 0.8650,  ..., 0.2551, 0.6130, 0.3679]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.9258, 0.3878, 0.0027,  ..., 0.4707, 0.4169, 0.1792])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 9.942713260650635 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '3758', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.1781005859375}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2527,     4930,  ..., 12495017,
+                            12497540, 12500000]),
+       col_indices=tensor([   1,    2,    3,  ..., 4995, 4997, 4999]),
+       values=tensor([0.2576, 0.5438, 0.7818,  ..., 0.1593, 0.4265, 0.7530]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0335, 0.1835, 0.7330,  ..., 0.5684, 0.8047, 0.4810])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.1781005859375 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2527,     4930,  ..., 12495017,
+                            12497540, 12500000]),
+       col_indices=tensor([   1,    2,    3,  ..., 4995, 4997, 4999]),
+       values=tensor([0.2576, 0.5438, 0.7818,  ..., 0.1593, 0.4265, 0.7530]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0335, 0.1835, 0.7330,  ..., 0.5684, 0.8047, 0.4810])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.1781005859375 seconds
+
+[41.47, 40.36, 39.99, 40.33, 39.95, 40.28, 39.98, 39.88, 39.78, 45.28]
+[122.9]
+15.917847394943237
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3758, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.1781005859375, 'TIME_S_1KI': 2.708382274065327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1956.303444838524, 'W': 122.9}
+[41.47, 40.36, 39.99, 40.33, 39.95, 40.28, 39.98, 39.88, 39.78, 45.28, 40.38, 39.63, 39.74, 44.87, 40.1, 40.15, 40.75, 40.1, 40.4, 40.08]
+729.895
+36.494749999999996
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 3758, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.1781005859375, 'TIME_S_1KI': 2.708382274065327, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1956.303444838524, 'W': 122.9, 'J_1KI': 520.570368504131, 'W_1KI': 32.70356572645024, 'W_D': 86.40525000000001, 'J_D': 1375.3855836219193, 'W_D_1KI': 22.992349654071315, 'J_D_1KI': 6.118240993632601}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..c2948bd
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 505155, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.3298921585083, "TIME_S_1KI": 0.020448955584935914, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1240.863141503334, "W": 95.58, "J_1KI": 2.4564007908529737, "W_1KI": 0.1892092526056359, "W_D": 59.942499999999995, "J_D": 778.2008669131993, "W_D_1KI": 0.11866159891518444, "J_D_1KI": 0.0002349013647596964}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..107ac32
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,329 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.013948440551757812}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([3548, 3508, 4386, 3528, 2702, 3004, 3629, 4756, 1243,
+                            213, 2804, 1698,  689, 4639, 4580, 1578, 3327,  694,
+                           1408, 2610, 4665, 1701, 4464,  632, 2037, 2500, 1517,
+                           2177, 1389, 4628,  306, 1568, 3761, 3194, 3074, 2522,
+                           3705, 2681, 4246,  249, 1916, 3633, 4678, 1217,  107,
+                           2703, 1648, 2700, 2961, 4336, 1084, 4254,  396, 3740,
+                           3046, 2671, 2061, 1766, 3209, 4565, 1985, 2700, 4834,
+                           2805,  875, 2910, 2400, 2621, 4389,  955, 1399,  578,
+                           2242, 4964, 3239,  222, 1256, 3099, 3567, 2886, 3721,
+                           1671, 1246, 4445, 3748, 4434, 1765,  983, 1353, 3314,
+                           2249, 2525, 4314, 2896, 2171, 3775, 3320,  730, 2027,
+                           2731, 3976, 3825, 4171, 1978, 4468, 2371,  386, 1118,
+                           3263,  840, 3509, 4865, 3412, 2573, 1668, 4140, 1828,
+                           1203,  819, 4214, 2533, 3446,  643, 4924, 2902, 1393,
+                           4975,  841, 1924, 1159, 1396, 1327, 3531, 2008, 2330,
+                           3344,    0, 1785, 2268, 4522, 1792, 2828,  305, 4487,
+                           4986, 3210, 3476, 4418, 3986, 3188, 1206, 4837, 2877,
+                           2143, 1316, 3014, 3807,  339, 1928, 4332, 1721, 1955,
+                           1430, 1820, 1733,  132, 1124, 4910,  399, 4998, 3203,
+                           1066, 4770, 3787, 2390, 4240,  862, 2987, 1396, 4199,
+                           2140, 4278, 4725, 3767, 4419, 1019, 3708,   90, 2851,
+                           2610, 3655, 3402, 2040, 1712, 1375, 4589, 2905, 1572,
+                            545, 3985, 3399,  582, 4328, 3912, 2552,   83, 2255,
+                           1709,  772, 4299, 2146, 3329, 2442, 3295,   60,  173,
+                            543, 4997, 2966, 3912, 1602,  135, 2282, 3935, 2764,
+                           2342, 3756, 4573, 3705, 1470, 3025, 1498, 4276,  668,
+                           3561, 4033,  260, 3652,  775, 4020, 1031, 2617, 2294,
+                           2109, 2487, 3590, 1199, 2797, 1290, 3990]),
+       values=tensor([0.6994, 0.2438, 0.4802, 0.0829, 0.0677, 0.0178, 0.7638,
+                      0.1665, 0.8626, 0.8633, 0.8809, 0.3889, 0.5842, 0.4728,
+                      0.4918, 0.0860, 0.7324, 0.8491, 0.3798, 0.3500, 0.4975,
+                      0.0872, 0.8650, 0.3555, 0.4399, 0.2630, 0.0729, 0.3054,
+                      0.9674, 0.7941, 0.9749, 0.5236, 0.8844, 0.2916, 0.4218,
+                      0.0889, 0.1637, 0.0411, 0.1963, 0.8167, 0.6130, 0.2282,
+                      0.0754, 0.2471, 0.0778, 0.4752, 0.2737, 0.1262, 0.2451,
+                      0.2934, 0.3944, 0.0397, 0.3394, 0.7909, 0.5453, 0.0895,
+                      0.2329, 0.3870, 0.5830, 0.0888, 0.8460, 0.7742, 0.7374,
+                      0.8528, 0.2281, 0.9068, 0.0092, 0.0150, 0.9568, 0.4508,
+                      0.2063, 0.9542, 0.6049, 0.5147, 0.9346, 0.5104, 0.1196,
+                      0.8281, 0.2227, 0.7282, 0.2980, 0.7830, 0.6065, 0.2936,
+                      0.6589, 0.1956, 0.8884, 0.6244, 0.8765, 0.9279, 0.5777,
+                      0.8162, 0.0894, 0.3744, 0.1591, 0.3051, 0.9299, 0.1618,
+                      0.7383, 0.9907, 0.5121, 0.6397, 0.8338, 0.9391, 0.2607,
+                      0.4098, 0.6073, 0.2048, 0.8476, 0.1799, 0.1533, 0.5127,
+                      0.3612, 0.8614, 0.5878, 0.7167, 0.1917, 0.2581, 0.3381,
+                      0.5246, 0.2437, 0.9851, 0.9032, 0.6527, 0.5590, 0.5454,
+                      0.0253, 0.0710, 0.1587, 0.3574, 0.7354, 0.6182, 0.5365,
+                      0.0479, 0.8974, 0.6075, 0.5864, 0.7635, 0.4139, 0.6734,
+                      0.0016, 0.0763, 0.3633, 0.3792, 0.6630, 0.0919, 0.1222,
+                      0.5443, 0.8587, 0.0627, 0.1060, 0.4814, 0.8481, 0.2733,
+                      0.7553, 0.9339, 0.1865, 0.2260, 0.9547, 0.8541, 0.1158,
+                      0.0258, 0.5314, 0.6595, 0.5573, 0.7953, 0.3786, 0.1641,
+                      0.8997, 0.2507, 0.1855, 0.6951, 0.2863, 0.1627, 0.3079,
+                      0.5000, 0.3625, 0.8186, 0.3705, 0.2957, 0.1551, 0.0216,
+                      0.3714, 0.8284, 0.9522, 0.8937, 0.5141, 0.0703, 0.2182,
+                      0.9274, 0.7097, 0.4349, 0.6001, 0.7581, 0.1855, 0.1138,
+                      0.0069, 0.0143, 0.6779, 0.4223, 0.2934, 0.1234, 0.5974,
+                      0.7303, 0.9182, 0.4432, 0.6166, 0.0534, 0.9601, 0.1664,
+                      0.7453, 0.2693, 0.7496, 0.1561, 0.1695, 0.4247, 0.5083,
+                      0.7464, 0.9108, 0.9708, 0.4346, 0.1849, 0.3357, 0.6306,
+                      0.3234, 0.0643, 0.0684, 0.2529, 0.3070, 0.5381, 0.4691,
+                      0.3912, 0.0111, 0.6019, 0.4700, 0.8282, 0.9967, 0.0138,
+                      0.0331, 0.4050, 0.1544, 0.2207, 0.6016, 0.9303, 0.9139,
+                      0.9840, 0.7431, 0.3482, 0.1124, 0.6413]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.3949, 0.9428, 0.0102,  ..., 0.0310, 0.9492, 0.7070])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.013948440551757812 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '75277', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.5646839141845703}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([3746, 1654, 2453, 1836, 1687, 4126,  577,  989, 1161,
+                           2149, 3910, 3295, 4472,  133, 3358, 1352, 3096, 3601,
+                           3758, 2512, 1092, 4489, 1464, 1660, 3070, 3361, 4966,
+                            822, 3500,  236, 2632, 1344, 3148, 1004, 2075, 4538,
+                           1923, 4311, 3791, 3093, 1373,  470,  112, 1162, 2705,
+                           3514, 4485, 3748, 3597, 4486, 4629,   78,   32, 3433,
+                           1822, 3440, 1230,   93, 1755, 4162, 1309, 3789, 3501,
+                           2710, 1926, 2165,  381, 2357, 4887, 3442, 1756, 2858,
+                           2903, 4359, 3016, 2687, 1689, 4625, 1621, 3805, 3094,
+                           1702, 3528, 1035, 4698, 4982, 1451, 1771, 2089, 3195,
+                           4919, 4133, 1397, 4984, 2564, 4549, 4619, 2832, 4040,
+                           4237, 2079, 1796, 1577, 4625, 3108, 1608,   19, 3574,
+                           3985, 1287, 3355, 4562, 3138, 4018, 4235,  751, 3240,
+                           1452,   49, 2916, 1280, 2827, 2493, 4891, 2490, 4843,
+                           2541, 1858,  112, 4172, 3878, 2893,  375, 3701, 1061,
+                           2843, 3468,   53, 4322, 1606, 2648, 4201, 4904, 4969,
+                           3035, 4661, 1890, 3624, 2603,  426, 3014, 1375,  437,
+                           1036, 1237, 4055, 3154, 3403, 3642, 4899, 4262,  474,
+                           2778,  534, 2901, 1174, 2089, 4005, 2324,  163,  146,
+                            519, 4127, 1342,  708, 4532,  125, 4427,  212, 2077,
+                           4307, 3955, 3476,  203, 3487, 4805, 2720, 1985,  727,
+                             17,  534, 1652,  733, 3208, 1391, 4340, 3700, 1893,
+                            162, 3604, 4298, 2419, 2136,  802,  564,  129, 2585,
+                           1324, 2167, 2710, 4323,  116, 1401,  831, 2891, 2913,
+                           1398,  372, 4148, 3575, 2563, 4021,   40, 3400, 4783,
+                            824, 3068, 2795, 4664, 2245, 2717, 3120, 1588, 2273,
+                           2302, 3501, 1853, 4736, 2336, 2390, 3070, 2778, 2782,
+                            746, 4888,  427,  933, 3852, 4383, 1889]),
+       values=tensor([0.9927, 0.0340, 0.4488, 0.4635, 0.2230, 0.1466, 0.4603,
+                      0.2785, 0.5078, 0.0027, 0.7560, 0.4421, 0.3531, 0.7573,
+                      0.7663, 0.5287, 0.4866, 0.0160, 0.0811, 0.2667, 0.0905,
+                      0.2771, 0.9262, 0.8504, 0.9933, 0.1455, 0.8531, 0.9069,
+                      0.5790, 0.0929, 0.4671, 0.6608, 0.9664, 0.1896, 0.7953,
+                      0.9833, 0.4291, 0.7412, 0.1386, 0.5823, 0.9668, 0.6184,
+                      0.6067, 0.5096, 0.9072, 0.2000, 0.6029, 0.7709, 0.3337,
+                      0.1225, 0.3581, 0.4923, 0.5670, 0.5531, 0.2219, 0.3460,
+                      0.1648, 0.9635, 0.0634, 0.0066, 0.2847, 0.4656, 0.8836,
+                      0.9572, 0.7046, 0.5077, 0.6573, 0.4027, 0.0704, 0.6249,
+                      0.7137, 0.0161, 0.2786, 0.6964, 0.9732, 0.0101, 0.9546,
+                      0.7324, 0.8008, 0.6620, 0.3443, 0.9296, 0.6939, 0.5327,
+                      0.2792, 0.5590, 0.4343, 0.4000, 0.5836, 0.6246, 0.3293,
+                      0.4244, 0.5279, 0.7005, 0.2154, 0.0350, 0.9262, 0.9148,
+                      0.3048, 0.0077, 0.5649, 0.4772, 0.8216, 0.6110, 0.2610,
+                      0.1616, 0.7469, 0.5265, 0.3053, 0.8103, 0.5940, 0.9742,
+                      0.5862, 0.5529, 0.3872, 0.3034, 0.6804, 0.6806, 0.6660,
+                      0.5771, 0.6250, 0.9114, 0.6649, 0.3603, 0.1633, 0.2146,
+                      0.1054, 0.1040, 0.0319, 0.4499, 0.9767, 0.8617, 0.7495,
+                      0.1652, 0.5616, 0.5919, 0.4704, 0.2766, 0.9966, 0.7157,
+                      0.1989, 0.6419, 0.8518, 0.4191, 0.4983, 0.6045, 0.7545,
+                      0.3583, 0.1657, 0.2221, 0.8782, 0.5595, 0.9120, 0.4869,
+                      0.2789, 0.3396, 0.9955, 0.5682, 0.9596, 0.9327, 0.6012,
+                      0.2415, 0.5228, 0.5492, 0.9986, 0.8668, 0.0412, 0.8168,
+                      0.4480, 0.6255, 0.6348, 0.6366, 0.4660, 0.8412, 0.9469,
+                      0.3182, 0.0180, 0.7704, 0.3602, 0.4075, 0.8083, 0.5267,
+                      0.5330, 0.4008, 0.8286, 0.6612, 0.5353, 0.6215, 0.4553,
+                      0.1920, 0.3166, 0.3250, 0.3744, 0.5410, 0.8495, 0.8267,
+                      0.2666, 0.2654, 0.6447, 0.8392, 0.9176, 0.4756, 0.9542,
+                      0.8318, 0.5561, 0.5761, 0.1449, 0.0902, 0.9651, 0.4745,
+                      0.1336, 0.4136, 0.1136, 0.8153, 0.3693, 0.4404, 0.2291,
+                      0.9951, 0.7922, 0.8470, 0.5195, 0.9072, 0.0501, 0.5628,
+                      0.6200, 0.3160, 0.6988, 0.7319, 0.9009, 0.3185, 0.5934,
+                      0.7917, 0.9332, 0.5038, 0.7465, 0.1646, 0.8555, 0.0988,
+                      0.4002, 0.8098, 0.8642, 0.7419, 0.3377, 0.6378, 0.4276,
+                      0.2050, 0.6970, 0.0429, 0.1896, 0.1443]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.0748, 0.8455, 0.5581,  ..., 0.9449, 0.9600, 0.8816])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 1.5646839141845703 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '505155', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.3298921585083}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1247, 4227, 2384, 1324, 2688,  777, 3611, 2096, 3777,
+                           4017, 3060, 3853,  275, 2455, 1734, 3565,   37, 2244,
+                           1733, 4350, 1234, 2896, 3213, 1561, 1980, 4472, 4663,
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+                           2485, 4784, 4550,  726, 4920, 1331,  357, 3709, 4153,
+                           3528, 2484, 3864, 1866, 4322,  727, 4703]),
+       values=tensor([0.4663, 0.4340, 0.1210, 0.0765, 0.6336, 0.1902, 0.3091,
+                      0.4203, 0.8220, 0.0850, 0.1025, 0.1361, 0.5908, 0.4685,
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+                      0.3274, 0.7923, 0.1515, 0.5293, 0.8418, 0.1450, 0.8268,
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+                      0.5451, 0.8590, 0.5389, 0.0080, 0.8363, 0.8570, 0.5734,
+                      0.7613, 0.9018, 0.0697, 0.9293, 0.2543, 0.2531, 0.2854,
+                      0.3722, 0.6889, 0.4487, 0.3475, 0.2897]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.8985, 0.0813, 0.9894,  ..., 0.1805, 0.5543, 0.3501])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.3298921585083 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1247, 4227, 2384, 1324, 2688,  777, 3611, 2096, 3777,
+                           4017, 3060, 3853,  275, 2455, 1734, 3565,   37, 2244,
+                           1733, 4350, 1234, 2896, 3213, 1561, 1980, 4472, 4663,
+                            506, 4951, 3982, 1179,  751, 4549, 4447, 2215,  404,
+                            243, 3834, 4527, 3698, 4091, 3198, 2812, 3614, 4523,
+                           1643, 1713, 1084, 2707,    4, 4057, 1938, 3151, 2591,
+                            736, 3345,  437, 2659,  175, 3499, 4582, 1472,  654,
+                           3080, 4209,  728, 2500, 4977, 4359, 4315, 4550, 1180,
+                             65, 1612, 4551, 4050, 2941, 4296, 4434, 1223, 1238,
+                           1086, 3880, 4530, 1316, 2102, 4761, 3908, 1050, 3705,
+                           3194, 4362, 3882,  122,  751, 3713, 3799, 1795, 2520,
+                           1510, 2238, 4244, 1885, 2083, 3076, 2704, 4833, 3679,
+                           4672, 3718, 2996, 4020, 4241, 4395,  853, 4475, 4071,
+                           1868, 2791,   21, 4190, 1530, 3067, 1932, 3665, 3853,
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+                           2916, 2447, 2605, 2672, 2276, 4850, 4965, 1297,  694,
+                            259, 1199, 4640, 4880, 3240, 1438,   11, 2797, 2545,
+                           3917, 4254,  214, 1182, 3713, 4287, 4785, 2360, 1203,
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+                           1772, 4045, 3022, 1213, 4864, 3162, 1697, 2645,  550,
+                           2485, 4784, 4550,  726, 4920, 1331,  357, 3709, 4153,
+                           3528, 2484, 3864, 1866, 4322,  727, 4703]),
+       values=tensor([0.4663, 0.4340, 0.1210, 0.0765, 0.6336, 0.1902, 0.3091,
+                      0.4203, 0.8220, 0.0850, 0.1025, 0.1361, 0.5908, 0.4685,
+                      0.3710, 0.6463, 0.1664, 0.6973, 0.9489, 0.6122, 0.1354,
+                      0.4027, 0.3554, 0.9045, 0.3464, 0.4691, 0.3320, 0.2792,
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+                      0.0575, 0.3527, 0.3951, 0.3470, 0.3333, 0.5355, 0.7562,
+                      0.1077, 0.3035, 0.4048, 0.6937, 0.2967, 0.6561, 0.1737,
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+                      0.5455, 0.5723, 0.4523, 0.5057, 0.6324, 0.9028, 0.0577,
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+                      0.7613, 0.9018, 0.0697, 0.9293, 0.2543, 0.2531, 0.2854,
+                      0.3722, 0.6889, 0.4487, 0.3475, 0.2897]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.8985, 0.0813, 0.9894,  ..., 0.1805, 0.5543, 0.3501])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.3298921585083 seconds
+
+[40.32, 39.94, 39.79, 39.67, 40.95, 39.71, 39.34, 39.43, 39.56, 39.61]
+[95.58]
+12.982455968856812
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 505155, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.3298921585083, 'TIME_S_1KI': 0.020448955584935914, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1240.863141503334, 'W': 95.58}
+[40.32, 39.94, 39.79, 39.67, 40.95, 39.71, 39.34, 39.43, 39.56, 39.61, 39.84, 39.42, 39.39, 39.45, 39.34, 39.21, 39.4, 39.18, 39.13, 39.91]
+712.75
+35.6375
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 505155, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.3298921585083, 'TIME_S_1KI': 0.020448955584935914, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1240.863141503334, 'W': 95.58, 'J_1KI': 2.4564007908529737, 'W_1KI': 0.1892092526056359, 'W_D': 59.942499999999995, 'J_D': 778.2008669131993, 'W_D_1KI': 0.11866159891518444, 'J_D_1KI': 0.0002349013647596964}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..1cf76cc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Epyc 7313P", "CORES": 16, "ITERATIONS": 461197, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.877047538757324, "TIME_S_1KI": 0.023584384848030937, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1221.180625166893, "W": 95.74, "J_1KI": 2.6478503224585004, "W_1KI": 0.20759024885244265, "W_D": 60.15774999999999, "J_D": 767.3227360939383, "W_D_1KI": 0.13043829426470682, "J_D_1KI": 0.0002828255480081328}
diff --git a/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..09b3a13
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/epyc_7313p_max_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.0320582389831543}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 1249, 1249, 1250]),
+       col_indices=tensor([2956,  558, 3504,  ..., 1528, 4784, 1878]),
+       values=tensor([0.5224, 0.1438, 0.5941,  ..., 0.0368, 0.6760, 0.3012]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.8976, 0.5094, 0.6995,  ..., 0.0327, 0.1649, 0.7937])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.0320582389831543 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '32752', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.745659589767456}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([1743, 4461,  346,  ..., 1137, 3893, 4349]),
+       values=tensor([0.7861, 0.9854, 0.5411,  ..., 0.5282, 0.2898, 0.9587]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.5708, 0.3567, 0.0850,  ..., 0.6472, 0.1624, 0.7150])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.745659589767456 seconds
+
+['apptainer', 'run', 'pytorch-epyc_7313p.sif', 'python3', 'spmv.py', 'synthetic', 'csr', '461197', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.877047538757324}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4244, 4483, 4692,  ..., 3607, 4429,  290]),
+       values=tensor([0.6080, 0.0136, 0.3918,  ..., 0.5066, 0.3391, 0.6977]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.1101, 0.0872, 0.5048,  ..., 0.5059, 0.1642, 0.4124])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.877047538757324 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1250, 1250, 1250]),
+       col_indices=tensor([4244, 4483, 4692,  ..., 3607, 4429,  290]),
+       values=tensor([0.6080, 0.0136, 0.3918,  ..., 0.5066, 0.3391, 0.6977]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.1101, 0.0872, 0.5048,  ..., 0.5059, 0.1642, 0.4124])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.877047538757324 seconds
+
+[42.19, 40.35, 39.27, 40.36, 39.17, 39.06, 39.17, 39.21, 39.04, 39.45]
+[95.74]
+12.755176782608032
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 461197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.877047538757324, 'TIME_S_1KI': 0.023584384848030937, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.180625166893, 'W': 95.74}
+[42.19, 40.35, 39.27, 40.36, 39.17, 39.06, 39.17, 39.21, 39.04, 39.45, 39.84, 39.7, 39.64, 39.58, 39.75, 39.24, 39.17, 39.36, 39.12, 39.43]
+711.645
+35.58225
+{'CPU': 'Epyc 7313P', 'CORES': 16, 'ITERATIONS': 461197, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.877047538757324, 'TIME_S_1KI': 0.023584384848030937, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.180625166893, 'W': 95.74, 'J_1KI': 2.6478503224585004, 'W_1KI': 0.20759024885244265, 'W_D': 60.15774999999999, 'J_D': 767.3227360939383, 'W_D_1KI': 0.13043829426470682, 'J_D_1KI': 0.0002828255480081328}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json
new file mode 100644
index 0000000..b0c2a19
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 33525, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.643972158432007, "TIME_S_1KI": 0.3174935766870099, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1221.4049115991593, "W": 88.39, "J_1KI": 36.43265955553048, "W_1KI": 2.6365398956002983, "W_D": 72.1155, "J_D": 996.5179986698627, "W_D_1KI": 2.151096196868009, "J_D_1KI": 0.06416394323245365}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output
new file mode 100644
index 0000000..dc5d053
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.04636812210083008}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,      13,  ...,  999983,
+                             999994, 1000000]),
+       col_indices=tensor([ 8176, 34026, 54478,  ..., 84998, 92494, 98961]),
+       values=tensor([0.4351, 0.9999, 0.3437,  ..., 0.3684, 0.7357, 0.5729]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8699, 0.2767, 0.3378,  ..., 0.5349, 0.7243, 0.7857])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 0.04636812210083008 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '22644', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 7.091935634613037}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       6,      23,  ...,  999978,
+                             999991, 1000000]),
+       col_indices=tensor([26374, 42582, 44652,  ..., 65952, 74293, 78884]),
+       values=tensor([0.4256, 0.1611, 0.6127,  ..., 0.5242, 0.3400, 0.0348]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.0369, 0.6183, 0.8933,  ..., 0.8293, 0.4628, 0.9829])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 7.091935634613037 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33525', '-ss', '100000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.643972158432007}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       7,      17,  ...,  999978,
+                             999994, 1000000]),
+       col_indices=tensor([ 9594, 11946, 25379,  ..., 52892, 57506, 73818]),
+       values=tensor([0.4978, 0.8076, 0.6002,  ..., 0.2925, 0.4675, 0.5122]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8765, 0.8954, 0.8874,  ..., 0.8137, 0.3245, 0.8007])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.643972158432007 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       7,      17,  ...,  999978,
+                             999994, 1000000]),
+       col_indices=tensor([ 9594, 11946, 25379,  ..., 52892, 57506, 73818]),
+       values=tensor([0.4978, 0.8076, 0.6002,  ..., 0.2925, 0.4675, 0.5122]),
+       size=(100000, 100000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8765, 0.8954, 0.8874,  ..., 0.8137, 0.3245, 0.8007])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 1000000
+Density: 0.0001
+Time: 10.643972158432007 seconds
+
+[18.37, 17.76, 17.76, 17.79, 18.13, 17.85, 18.03, 17.72, 18.33, 18.48]
+[88.39]
+13.818360805511475
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33525, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.643972158432007, 'TIME_S_1KI': 0.3174935766870099, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.4049115991593, 'W': 88.39}
+[18.37, 17.76, 17.76, 17.79, 18.13, 17.85, 18.03, 17.72, 18.33, 18.48, 18.41, 18.08, 18.64, 18.55, 18.21, 18.02, 18.07, 18.01, 17.96, 17.9]
+325.49
+16.2745
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 33525, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.643972158432007, 'TIME_S_1KI': 0.3174935766870099, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1221.4049115991593, 'W': 88.39, 'J_1KI': 36.43265955553048, 'W_1KI': 2.6365398956002983, 'W_D': 72.1155, 'J_D': 996.5179986698627, 'W_D_1KI': 2.151096196868009, 'J_D_1KI': 0.06416394323245365}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json
new file mode 100644
index 0000000..7fe967e
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2660, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.18576693534851, "TIME_S_1KI": 3.829235689980643, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1328.5220910072328, "W": 81.2, "J_1KI": 499.44439511550104, "W_1KI": 30.526315789473685, "W_D": 64.453, "J_D": 1054.5225902917387, "W_D_1KI": 24.23045112781955, "J_D_1KI": 9.10919215331562}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.output
new file mode 100644
index 0000000..8cde86c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_0.001.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.39462947845458984}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      104,      208,  ...,  9999814,
+                             9999906, 10000000]),
+       col_indices=tensor([ 1924,  2222,  4663,  ..., 98435, 98556, 99127]),
+       values=tensor([0.9193, 0.2961, 0.8826,  ..., 0.2999, 0.4100, 0.0457]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8186, 0.3714, 0.9798,  ..., 0.9009, 0.9275, 0.2252])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 0.39462947845458984 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2660', '-ss', '100000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.18576693534851}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      111,      209,  ...,  9999785,
+                             9999908, 10000000]),
+       col_indices=tensor([ 4849,  5332,  5597,  ..., 99100, 99293, 99777]),
+       values=tensor([0.3984, 0.3126, 0.3684,  ..., 0.2469, 0.5703, 0.8605]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6012, 0.7247, 0.1820,  ..., 0.8515, 0.6518, 0.6577])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.18576693534851 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      111,      209,  ...,  9999785,
+                             9999908, 10000000]),
+       col_indices=tensor([ 4849,  5332,  5597,  ..., 99100, 99293, 99777]),
+       values=tensor([0.3984, 0.3126, 0.3684,  ..., 0.2469, 0.5703, 0.8605]),
+       size=(100000, 100000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.6012, 0.7247, 0.1820,  ..., 0.8515, 0.6518, 0.6577])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 10000000
+Density: 0.001
+Time: 10.18576693534851 seconds
+
+[18.33, 18.01, 18.45, 18.12, 17.88, 17.91, 18.33, 22.42, 18.21, 17.9]
+[81.2]
+16.361109495162964
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2660, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.18576693534851, 'TIME_S_1KI': 3.829235689980643, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1328.5220910072328, 'W': 81.2}
+[18.33, 18.01, 18.45, 18.12, 17.88, 17.91, 18.33, 22.42, 18.21, 17.9, 22.63, 20.65, 18.09, 18.03, 18.02, 18.26, 18.0, 18.17, 17.93, 18.06]
+334.94000000000005
+16.747000000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2660, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.18576693534851, 'TIME_S_1KI': 3.829235689980643, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1328.5220910072328, 'W': 81.2, 'J_1KI': 499.44439511550104, 'W_1KI': 30.526315789473685, 'W_D': 64.453, 'J_D': 1054.5225902917387, 'W_D_1KI': 24.23045112781955, 'J_D_1KI': 9.10919215331562}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json
new file mode 100644
index 0000000..fdd737d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 64522, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.3600492477417, "TIME_S_1KI": 0.1605661518201807, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1143.5515343093873, "W": 83.36000000000001, "J_1KI": 17.723435949124134, "W_1KI": 1.291962431418741, "W_D": 66.67275000000001, "J_D": 914.6320244616867, "W_D_1KI": 1.0333335916431607, "J_D_1KI": 0.01601521328605996}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output
new file mode 100644
index 0000000..8218685
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_1e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.03250718116760254}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      1,  ...,  99998,  99998,
+                            100000]),
+       col_indices=tensor([12882, 21465, 63858,  ..., 96153,  4715, 69382]),
+       values=tensor([0.1495, 0.9028, 0.7353,  ..., 0.9651, 0.0553, 0.0388]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6296, 0.2324, 0.7696,  ..., 0.0819, 0.5051, 0.6795])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 0.03250718116760254 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '32300', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 5.256327390670776}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      3,  ...,  99998,  99999,
+                            100000]),
+       col_indices=tensor([24100, 22524, 41698,  ..., 71518, 54296, 46275]),
+       values=tensor([0.6729, 0.0195, 0.1396,  ..., 0.6516, 0.4177, 0.7883]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.6330, 0.0132, 0.4522,  ..., 0.1249, 0.8426, 0.7168])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 5.256327390670776 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '64522', '-ss', '100000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.3600492477417}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99996,  99999,
+                            100000]),
+       col_indices=tensor([63372, 90175, 43637,  ..., 48404, 84175, 41742]),
+       values=tensor([0.0143, 0.7083, 0.4138,  ..., 0.7171, 0.1589, 0.0907]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2672, 0.1313, 0.6231,  ..., 0.4829, 0.5251, 0.7815])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.3600492477417 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      1,      1,  ...,  99996,  99999,
+                            100000]),
+       col_indices=tensor([63372, 90175, 43637,  ..., 48404, 84175, 41742]),
+       values=tensor([0.0143, 0.7083, 0.4138,  ..., 0.7171, 0.1589, 0.0907]),
+       size=(100000, 100000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.2672, 0.1313, 0.6231,  ..., 0.4829, 0.5251, 0.7815])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 100000
+Density: 1e-05
+Time: 10.3600492477417 seconds
+
+[18.53, 17.8, 18.06, 18.37, 18.25, 17.96, 18.14, 21.36, 18.43, 17.88]
+[83.36]
+13.718228578567505
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64522, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.3600492477417, 'TIME_S_1KI': 0.1605661518201807, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1143.5515343093873, 'W': 83.36000000000001}
+[18.53, 17.8, 18.06, 18.37, 18.25, 17.96, 18.14, 21.36, 18.43, 17.88, 18.55, 17.91, 18.58, 19.12, 20.8, 18.14, 18.33, 17.85, 18.25, 17.83]
+333.745
+16.68725
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 64522, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.3600492477417, 'TIME_S_1KI': 0.1605661518201807, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1143.5515343093873, 'W': 83.36000000000001, 'J_1KI': 17.723435949124134, 'W_1KI': 1.291962431418741, 'W_D': 66.67275000000001, 'J_D': 914.6320244616867, 'W_D_1KI': 1.0333335916431607, 'J_D_1KI': 0.01601521328605996}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.json
new file mode 100644
index 0000000..07c28cc
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 46682, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.460205316543579, "TIME_S_1KI": 0.22407363258951157, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1228.6304648017883, "W": 87.08, "J_1KI": 26.319147954281917, "W_1KI": 1.865387087099953, "W_D": 70.695, "J_D": 997.4509727740286, "W_D_1KI": 1.51439527012553, "J_D_1KI": 0.03244066814030097}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.output
new file mode 100644
index 0000000..0398e28
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_100000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.03882646560668945}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      4,     12,  ..., 499993, 499995,
+                            500000]),
+       col_indices=tensor([20130, 29829, 49027,  ..., 32515, 51857, 99803]),
+       values=tensor([0.4194, 0.2208, 0.8236,  ..., 0.3620, 0.7637, 0.5129]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.7254, 0.4636, 0.7256,  ..., 0.4819, 0.1264, 0.5273])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 0.03882646560668945 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '27043', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 6.0826029777526855}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     14,  ..., 499987, 499992,
+                            500000]),
+       col_indices=tensor([19007, 19428, 24486,  ..., 87536, 92504, 96559]),
+       values=tensor([0.8398, 0.0370, 0.5128,  ..., 0.3625, 0.4907, 0.6853]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.6134, 0.6519, 0.5356,  ..., 0.0589, 0.6530, 0.3358])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 6.0826029777526855 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '46682', '-ss', '100000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [100000, 100000], "MATRIX_ROWS": 100000, "MATRIX_SIZE": 10000000000, "MATRIX_NNZ": 500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.460205316543579}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     19,  ..., 499992, 499995,
+                            500000]),
+       col_indices=tensor([ 9218, 31473, 33160,  ..., 57052, 72094, 94375]),
+       values=tensor([0.1819, 0.5310, 0.0116,  ..., 0.3541, 0.3048, 0.3110]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.6007, 0.7506, 0.6846,  ..., 0.1657, 0.4869, 0.7821])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.460205316543579 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     19,  ..., 499992, 499995,
+                            500000]),
+       col_indices=tensor([ 9218, 31473, 33160,  ..., 57052, 72094, 94375]),
+       values=tensor([0.1819, 0.5310, 0.0116,  ..., 0.3541, 0.3048, 0.3110]),
+       size=(100000, 100000), nnz=500000, layout=torch.sparse_csr)
+tensor([0.6007, 0.7506, 0.6846,  ..., 0.1657, 0.4869, 0.7821])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([100000, 100000])
+Rows: 100000
+Size: 10000000000
+NNZ: 500000
+Density: 5e-05
+Time: 10.460205316543579 seconds
+
+[18.32, 17.95, 18.46, 18.2, 18.15, 17.94, 18.28, 18.06, 18.31, 18.04]
+[87.08]
+14.109215259552002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.460205316543579, 'TIME_S_1KI': 0.22407363258951157, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1228.6304648017883, 'W': 87.08}
+[18.32, 17.95, 18.46, 18.2, 18.15, 17.94, 18.28, 18.06, 18.31, 18.04, 18.26, 18.28, 17.89, 18.48, 18.48, 18.03, 17.99, 18.35, 18.44, 18.2]
+327.69999999999993
+16.384999999999998
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46682, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [100000, 100000], 'MATRIX_ROWS': 100000, 'MATRIX_SIZE': 10000000000, 'MATRIX_NNZ': 500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.460205316543579, 'TIME_S_1KI': 0.22407363258951157, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1228.6304648017883, 'W': 87.08, 'J_1KI': 26.319147954281917, 'W_1KI': 1.865387087099953, 'W_D': 70.695, 'J_D': 997.4509727740286, 'W_D_1KI': 1.51439527012553, 'J_D_1KI': 0.03244066814030097}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json
new file mode 100644
index 0000000..1b64231
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 253108, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.677898168563843, "TIME_S_1KI": 0.0421871223689644, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1070.6826658773423, "W": 74.61, "J_1KI": 4.230141543836395, "W_1KI": 0.2947753528138186, "W_D": 58.27775, "J_D": 836.3084939194918, "W_D_1KI": 0.23024855002607583, "J_D_1KI": 0.0009096849962311576}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output
new file mode 100644
index 0000000..1ec4855
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.02065730094909668}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     3,     4,  ...,  9998,  9998, 10000]),
+       col_indices=tensor([6728, 7614, 8179,  ..., 1004, 2058, 8025]),
+       values=tensor([0.0279, 0.0803, 0.4096,  ..., 0.0871, 0.5549, 0.2943]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.5482, 0.4991, 0.5547,  ..., 0.6547, 0.2547, 0.5094])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 0.02065730094909668 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '50829', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 2.10860276222229}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     1,  ..., 10000, 10000, 10000]),
+       col_indices=tensor([5153, 2587, 4463,  ..., 5061, 9520, 1424]),
+       values=tensor([0.2204, 0.6183, 0.5613,  ..., 0.3086, 0.3306, 0.5938]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.9842, 0.7142, 0.2121,  ..., 0.3434, 0.1561, 0.6145])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 2.10860276222229 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '253108', '-ss', '10000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.677898168563843}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     4,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([7291,  527, 4481,  ..., 6785, 7922, 1484]),
+       values=tensor([0.3434, 0.3822, 0.2401,  ..., 0.8298, 0.4309, 0.4668]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.2882, 0.0253, 0.9805,  ..., 0.1323, 0.6315, 0.0794])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.677898168563843 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     4,  ...,  9999,  9999, 10000]),
+       col_indices=tensor([7291,  527, 4481,  ..., 6785, 7922, 1484]),
+       values=tensor([0.3434, 0.3822, 0.2401,  ..., 0.8298, 0.4309, 0.4668]),
+       size=(10000, 10000), nnz=10000, layout=torch.sparse_csr)
+tensor([0.2882, 0.0253, 0.9805,  ..., 0.1323, 0.6315, 0.0794])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000
+Density: 0.0001
+Time: 10.677898168563843 seconds
+
+[18.38, 17.89, 18.34, 17.77, 18.06, 17.77, 18.13, 17.86, 17.98, 18.23]
+[74.61]
+14.350390911102295
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253108, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.677898168563843, 'TIME_S_1KI': 0.0421871223689644, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1070.6826658773423, 'W': 74.61}
+[18.38, 17.89, 18.34, 17.77, 18.06, 17.77, 18.13, 17.86, 17.98, 18.23, 18.53, 18.0, 18.04, 18.11, 18.16, 18.08, 18.2, 18.27, 19.29, 18.25]
+326.645
+16.33225
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 253108, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.677898168563843, 'TIME_S_1KI': 0.0421871223689644, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1070.6826658773423, 'W': 74.61, 'J_1KI': 4.230141543836395, 'W_1KI': 0.2947753528138186, 'W_D': 58.27775, 'J_D': 836.3084939194918, 'W_D_1KI': 0.23024855002607583, 'J_D_1KI': 0.0009096849962311576}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json
new file mode 100644
index 0000000..090450b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 194593, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.473386764526367, "TIME_S_1KI": 0.053822011914747024, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1083.1964892935753, "W": 79.61, "J_1KI": 5.566472017459905, "W_1KI": 0.40911029687604383, "W_D": 62.8515, "J_D": 855.175532556653, "W_D_1KI": 0.3229895217196919, "J_D_1KI": 0.001659820865702733}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output
new file mode 100644
index 0000000..8858416
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.020806312561035156}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      7,     14,  ...,  99985,  99993,
+                            100000]),
+       col_indices=tensor([5438, 7119, 8479,  ..., 6797, 6979, 8109]),
+       values=tensor([0.2056, 0.5255, 0.6332,  ..., 0.1682, 0.9365, 0.4633]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.0316, 0.8438, 0.1562,  ..., 0.6730, 0.8332, 0.9126])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 0.020806312561035156 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '50465', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.723015785217285}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     18,     34,  ...,  99978,  99989,
+                            100000]),
+       col_indices=tensor([ 818, 1321, 1616,  ..., 5603, 7366, 9704]),
+       values=tensor([0.8713, 0.7316, 0.2331,  ..., 0.6687, 0.3725, 0.7818]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.5742, 0.6233, 0.0987,  ..., 0.1452, 0.2067, 0.1195])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 2.723015785217285 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '194593', '-ss', '10000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 100000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.473386764526367}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     16,  ...,  99982,  99989,
+                            100000]),
+       col_indices=tensor([1034, 3380, 4243,  ..., 7428, 9116, 9600]),
+       values=tensor([0.4227, 0.1092, 0.7794,  ..., 0.2113, 0.3090, 0.9237]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7442, 0.3504, 0.6358,  ..., 0.6138, 0.7536, 0.9226])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.473386764526367 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     11,     16,  ...,  99982,  99989,
+                            100000]),
+       col_indices=tensor([1034, 3380, 4243,  ..., 7428, 9116, 9600]),
+       values=tensor([0.4227, 0.1092, 0.7794,  ..., 0.2113, 0.3090, 0.9237]),
+       size=(10000, 10000), nnz=100000, layout=torch.sparse_csr)
+tensor([0.7442, 0.3504, 0.6358,  ..., 0.6138, 0.7536, 0.9226])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 100000
+Density: 0.001
+Time: 10.473386764526367 seconds
+
+[19.08, 18.28, 17.95, 17.9, 19.17, 18.22, 19.03, 18.14, 18.71, 20.45]
+[79.61]
+13.606286764144897
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 194593, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.473386764526367, 'TIME_S_1KI': 0.053822011914747024, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1083.1964892935753, 'W': 79.61}
+[19.08, 18.28, 17.95, 17.9, 19.17, 18.22, 19.03, 18.14, 18.71, 20.45, 18.56, 18.36, 18.2, 17.96, 18.09, 18.17, 22.32, 18.37, 18.26, 17.99]
+335.17
+16.7585
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 194593, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 100000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.473386764526367, 'TIME_S_1KI': 0.053822011914747024, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1083.1964892935753, 'W': 79.61, 'J_1KI': 5.566472017459905, 'W_1KI': 0.40911029687604383, 'W_D': 62.8515, 'J_D': 855.175532556653, 'W_D_1KI': 0.3229895217196919, 'J_D_1KI': 0.001659820865702733}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json
new file mode 100644
index 0000000..8c6811d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 57740, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.343472957611084, "TIME_S_1KI": 0.17913877654331634, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1219.6624715328217, "W": 87.4, "J_1KI": 21.123354200429883, "W_1KI": 1.5136820228611017, "W_D": 71.01700000000001, "J_D": 991.0385553872587, "W_D_1KI": 1.2299445791479044, "J_D_1KI": 0.021301430189606934}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output
new file mode 100644
index 0000000..ff97c3a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.03377032279968262}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     110,     219,  ...,  999820,
+                             999911, 1000000]),
+       col_indices=tensor([   0,   38,  265,  ..., 9703, 9904, 9960]),
+       values=tensor([0.3683, 0.9828, 0.4174,  ..., 0.4331, 0.2376, 0.7467]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8519, 0.1445, 0.2230,  ..., 0.9278, 0.7204, 0.3614])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 0.03377032279968262 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '31092', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 5.654046535491943}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     102,     199,  ...,  999795,
+                             999900, 1000000]),
+       col_indices=tensor([ 113,  165,  189,  ..., 9912, 9940, 9996]),
+       values=tensor([0.2048, 0.9236, 0.8269,  ..., 0.2195, 0.4387, 0.6731]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.8364, 0.2950, 0.2365,  ..., 0.2102, 0.7661, 0.3156])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 5.654046535491943 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '57740', '-ss', '10000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.343472957611084}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     106,     216,  ...,  999804,
+                             999899, 1000000]),
+       col_indices=tensor([  61,   88,  117,  ..., 9666, 9676, 9799]),
+       values=tensor([0.7050, 0.8533, 0.9508,  ..., 0.3667, 0.6991, 0.8071]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9939, 0.1893, 0.9694,  ..., 0.0779, 0.3428, 0.9229])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.343472957611084 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     106,     216,  ...,  999804,
+                             999899, 1000000]),
+       col_indices=tensor([  61,   88,  117,  ..., 9666, 9676, 9799]),
+       values=tensor([0.7050, 0.8533, 0.9508,  ..., 0.3667, 0.6991, 0.8071]),
+       size=(10000, 10000), nnz=1000000, layout=torch.sparse_csr)
+tensor([0.9939, 0.1893, 0.9694,  ..., 0.0779, 0.3428, 0.9229])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000000
+Density: 0.01
+Time: 10.343472957611084 seconds
+
+[18.73, 18.07, 18.16, 18.31, 18.98, 17.81, 18.18, 18.17, 18.06, 18.83]
+[87.4]
+13.95494818687439
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 57740, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.343472957611084, 'TIME_S_1KI': 0.17913877654331634, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1219.6624715328217, 'W': 87.4}
+[18.73, 18.07, 18.16, 18.31, 18.98, 17.81, 18.18, 18.17, 18.06, 18.83, 18.31, 18.07, 18.19, 17.94, 18.17, 18.13, 18.04, 18.09, 18.22, 18.27]
+327.65999999999997
+16.383
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 57740, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.343472957611084, 'TIME_S_1KI': 0.17913877654331634, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1219.6624715328217, 'W': 87.4, 'J_1KI': 21.123354200429883, 'W_1KI': 1.5136820228611017, 'W_D': 71.01700000000001, 'J_D': 991.0385553872587, 'W_D_1KI': 1.2299445791479044, 'J_D_1KI': 0.021301430189606934}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json
new file mode 100644
index 0000000..9882866
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8902, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.860300064086914, "TIME_S_1KI": 1.2199842803961933, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1340.0520877552033, "W": 84.52, "J_1KI": 150.53382248429602, "W_1KI": 9.49449561896203, "W_D": 68.1745, "J_D": 1080.896605024457, "W_D_1KI": 7.658335205571781, "J_D_1KI": 0.8602937773053}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output
new file mode 100644
index 0000000..266a604
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.13720321655273438}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     497,     987,  ..., 4998984,
+                            4999490, 5000000]),
+       col_indices=tensor([  31,   32,   48,  ..., 9945, 9978, 9990]),
+       values=tensor([0.2379, 0.1839, 0.8156,  ..., 0.3545, 0.1897, 0.0490]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7954, 0.2457, 0.2337,  ..., 0.1008, 0.2602, 0.7172])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 0.13720321655273438 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7652', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 9.025035858154297}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     488,     990,  ..., 4999027,
+                            4999510, 5000000]),
+       col_indices=tensor([  13,   24,   30,  ..., 9939, 9983, 9997]),
+       values=tensor([0.8521, 0.2131, 0.0790,  ..., 0.0763, 0.7991, 0.5452]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7324, 0.5581, 0.6877,  ..., 0.3893, 0.7172, 0.4223])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 9.025035858154297 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8902', '-ss', '10000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.860300064086914}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     512,     999,  ..., 4999039,
+                            4999553, 5000000]),
+       col_indices=tensor([  40,   52,   78,  ..., 9943, 9982, 9986]),
+       values=tensor([0.6271, 0.3251, 0.6536,  ..., 0.8006, 0.2414, 0.7322]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7511, 0.5744, 0.2092,  ..., 0.0810, 0.1870, 0.5605])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.860300064086914 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     512,     999,  ..., 4999039,
+                            4999553, 5000000]),
+       col_indices=tensor([  40,   52,   78,  ..., 9943, 9982, 9986]),
+       values=tensor([0.6271, 0.3251, 0.6536,  ..., 0.8006, 0.2414, 0.7322]),
+       size=(10000, 10000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7511, 0.5744, 0.2092,  ..., 0.0810, 0.1870, 0.5605])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000000
+Density: 0.05
+Time: 10.860300064086914 seconds
+
+[18.66, 18.42, 18.15, 17.87, 18.05, 18.01, 18.31, 17.87, 17.97, 18.0]
+[84.52]
+15.854851961135864
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8902, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.860300064086914, 'TIME_S_1KI': 1.2199842803961933, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1340.0520877552033, 'W': 84.52}
+[18.66, 18.42, 18.15, 17.87, 18.05, 18.01, 18.31, 17.87, 17.97, 18.0, 18.28, 17.95, 18.41, 17.97, 19.13, 18.12, 18.3, 17.86, 18.09, 17.92]
+326.91
+16.3455
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8902, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.860300064086914, 'TIME_S_1KI': 1.2199842803961933, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1340.0520877552033, 'W': 84.52, 'J_1KI': 150.53382248429602, 'W_1KI': 9.49449561896203, 'W_D': 68.1745, 'J_D': 1080.896605024457, 'W_D_1KI': 7.658335205571781, 'J_D_1KI': 0.8602937773053}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json
new file mode 100644
index 0000000..4142b7d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2952, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.514222383499146, "TIME_S_1KI": 3.5617284496948325, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1409.0968302583694, "W": 80.82, "J_1KI": 477.3363246132688, "W_1KI": 27.378048780487802, "W_D": 64.16999999999999, "J_D": 1118.8040534234044, "W_D_1KI": 21.737804878048774, "J_D_1KI": 7.363755039989422}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output
new file mode 100644
index 0000000..f33aa65
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.1.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.3909003734588623}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      946,     1929,  ...,  9998013,
+                             9999030, 10000000]),
+       col_indices=tensor([   1,   29,   66,  ..., 9951, 9961, 9963]),
+       values=tensor([0.1920, 0.8019, 0.0618,  ..., 0.8349, 0.9652, 0.3956]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.5191, 0.4835, 0.4753,  ..., 0.1633, 0.4541, 0.9422])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 0.3909003734588623 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2686', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 9.552263259887695}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1009,     2066,  ...,  9998034,
+                             9999022, 10000000]),
+       col_indices=tensor([   1,    6,   11,  ..., 9982, 9996, 9999]),
+       values=tensor([0.5825, 0.5025, 0.2695,  ..., 0.8369, 0.3596, 0.5616]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.8229, 0.3714, 0.9138,  ..., 0.5827, 0.9903, 0.1706])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 9.552263259887695 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2952', '-ss', '10000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.514222383499146}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1012,     2027,  ...,  9998023,
+                             9999029, 10000000]),
+       col_indices=tensor([  20,   21,   65,  ..., 9939, 9966, 9982]),
+       values=tensor([0.6149, 0.2165, 0.5741,  ..., 0.9222, 0.5603, 0.9724]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1720, 0.3458, 0.5186,  ..., 0.7493, 0.6588, 0.1643])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.514222383499146 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1012,     2027,  ...,  9998023,
+                             9999029, 10000000]),
+       col_indices=tensor([  20,   21,   65,  ..., 9939, 9966, 9982]),
+       values=tensor([0.6149, 0.2165, 0.5741,  ..., 0.9222, 0.5603, 0.9724]),
+       size=(10000, 10000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.1720, 0.3458, 0.5186,  ..., 0.7493, 0.6588, 0.1643])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 10000000
+Density: 0.1
+Time: 10.514222383499146 seconds
+
+[18.22, 18.02, 18.22, 18.83, 18.28, 18.32, 18.36, 18.07, 18.03, 17.97]
+[80.82]
+17.435001611709595
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2952, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.514222383499146, 'TIME_S_1KI': 3.5617284496948325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1409.0968302583694, 'W': 80.82}
+[18.22, 18.02, 18.22, 18.83, 18.28, 18.32, 18.36, 18.07, 18.03, 17.97, 19.01, 19.01, 17.78, 18.5, 20.32, 20.23, 18.29, 18.21, 17.96, 17.94]
+333.0
+16.65
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2952, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.514222383499146, 'TIME_S_1KI': 3.5617284496948325, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1409.0968302583694, 'W': 80.82, 'J_1KI': 477.3363246132688, 'W_1KI': 27.378048780487802, 'W_D': 64.16999999999999, 'J_D': 1118.8040534234044, 'W_D_1KI': 21.737804878048774, 'J_D_1KI': 7.363755039989422}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.json
new file mode 100644
index 0000000..047807c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1497, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.375812530517578, "TIME_S_1KI": 6.931070494667721, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2016.6524360942842, "W": 63.56, "J_1KI": 1347.1292158278452, "W_1KI": 42.458249832999336, "W_D": 46.965500000000006, "J_D": 1490.1367210098506, "W_D_1KI": 31.373079492317977, "J_D_1KI": 20.95730093007213}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.output
new file mode 100644
index 0000000..e1c6993
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.2.output
@@ -0,0 +1,65 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.7011280059814453}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     1997,     4011,  ..., 19995978,
+                            19998029, 20000000]),
+       col_indices=tensor([   0,    6,   10,  ..., 9977, 9981, 9997]),
+       values=tensor([0.1409, 0.7742, 0.3684,  ..., 0.6455, 0.2528, 0.1779]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.4126, 0.0545, 0.9867,  ..., 0.1672, 0.9147, 0.2153])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 0.7011280059814453 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1497', '-ss', '10000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 20000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.375812530517578}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2004,     3927,  ..., 19995948,
+                            19997998, 20000000]),
+       col_indices=tensor([   0,    7,    8,  ..., 9981, 9983, 9993]),
+       values=tensor([0.6769, 0.3987, 0.0257,  ..., 0.1977, 0.2040, 0.8027]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.3084, 0.6863, 0.1716,  ..., 0.2861, 0.5214, 0.4353])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.375812530517578 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2004,     3927,  ..., 19995948,
+                            19997998, 20000000]),
+       col_indices=tensor([   0,    7,    8,  ..., 9981, 9983, 9993]),
+       values=tensor([0.6769, 0.3987, 0.0257,  ..., 0.1977, 0.2040, 0.8027]),
+       size=(10000, 10000), nnz=20000000, layout=torch.sparse_csr)
+tensor([0.3084, 0.6863, 0.1716,  ..., 0.2861, 0.5214, 0.4353])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 20000000
+Density: 0.2
+Time: 10.375812530517578 seconds
+
+[18.42, 17.88, 18.24, 17.8, 17.91, 17.84, 18.25, 18.2, 17.93, 17.84]
+[63.56]
+31.728326559066772
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1497, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.375812530517578, 'TIME_S_1KI': 6.931070494667721, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2016.6524360942842, 'W': 63.56}
+[18.42, 17.88, 18.24, 17.8, 17.91, 17.84, 18.25, 18.2, 17.93, 17.84, 18.76, 17.93, 18.32, 21.74, 19.38, 18.29, 18.56, 18.06, 18.67, 18.76]
+331.89
+16.5945
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1497, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 20000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.375812530517578, 'TIME_S_1KI': 6.931070494667721, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2016.6524360942842, 'W': 63.56, 'J_1KI': 1347.1292158278452, 'W_1KI': 42.458249832999336, 'W_D': 46.965500000000006, 'J_D': 1490.1367210098506, 'W_D_1KI': 31.373079492317977, 'J_D_1KI': 20.95730093007213}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.json
new file mode 100644
index 0000000..b205393
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 949, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.941918134689331, "TIME_S_1KI": 11.529945347407093, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 3564.162018098831, "W": 52.14, "J_1KI": 3755.7028641715815, "W_1KI": 54.94204425711275, "W_D": 35.83725, "J_D": 2449.7461696032283, "W_D_1KI": 37.763171759747095, "J_D_1KI": 39.79259405663551}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.output
new file mode 100644
index 0000000..d986f3d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_0.3.output
@@ -0,0 +1,105 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 1.9010562896728516}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2954,     5989,  ..., 29993992,
+                            29996937, 30000000]),
+       col_indices=tensor([   2,    3,    4,  ..., 9991, 9993, 9999]),
+       values=tensor([0.8641, 0.5764, 0.3491,  ..., 0.5822, 0.6256, 0.4859]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.5324, 0.9227, 0.4901,  ..., 0.4747, 0.1770, 0.2536])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 1.9010562896728516 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '552', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 6.5163209438323975}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3039,     6048,  ..., 29994088,
+                            29997031, 30000000]),
+       col_indices=tensor([   5,   17,   19,  ..., 9983, 9988, 9994]),
+       values=tensor([0.5897, 0.1355, 0.4586,  ..., 0.2138, 0.9666, 0.8141]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.3973, 0.5491, 0.6398,  ..., 0.0595, 0.1069, 0.4910])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 6.5163209438323975 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '889', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.831693172454834}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3022,     6006,  ..., 29994154,
+                            29996992, 30000000]),
+       col_indices=tensor([   2,    5,   11,  ..., 9993, 9994, 9997]),
+       values=tensor([0.4549, 0.7646, 0.3501,  ..., 0.7301, 0.5346, 0.2783]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.0238, 0.3406, 0.5500,  ..., 0.0227, 0.0108, 0.7785])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 9.831693172454834 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '949', '-ss', '10000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 30000000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.941918134689331}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3007,     5955,  ..., 29993893,
+                            29996918, 30000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9996, 9997, 9998]),
+       values=tensor([0.2348, 0.5341, 0.6896,  ..., 0.7208, 0.8300, 0.7790]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.7258, 0.9160, 0.6029,  ..., 0.0530, 0.7513, 0.2296])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.941918134689331 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     3007,     5955,  ..., 29993893,
+                            29996918, 30000000]),
+       col_indices=tensor([   0,    1,    2,  ..., 9996, 9997, 9998]),
+       values=tensor([0.2348, 0.5341, 0.6896,  ..., 0.7208, 0.8300, 0.7790]),
+       size=(10000, 10000), nnz=30000000, layout=torch.sparse_csr)
+tensor([0.7258, 0.9160, 0.6029,  ..., 0.0530, 0.7513, 0.2296])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 30000000
+Density: 0.3
+Time: 10.941918134689331 seconds
+
+[18.62, 18.03, 18.0, 17.99, 18.26, 17.85, 18.27, 17.94, 18.3, 18.09]
+[52.14]
+68.35753774642944
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 949, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.941918134689331, 'TIME_S_1KI': 11.529945347407093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3564.162018098831, 'W': 52.14}
+[18.62, 18.03, 18.0, 17.99, 18.26, 17.85, 18.27, 17.94, 18.3, 18.09, 18.58, 17.83, 17.98, 17.87, 18.18, 18.09, 18.02, 17.83, 18.92, 18.1]
+326.055
+16.30275
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 949, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 30000000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.941918134689331, 'TIME_S_1KI': 11.529945347407093, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 3564.162018098831, 'W': 52.14, 'J_1KI': 3755.7028641715815, 'W_1KI': 54.94204425711275, 'W_D': 35.83725, 'J_D': 2449.7461696032283, 'W_D_1KI': 37.763171759747095, 'J_D_1KI': 39.79259405663551}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json
new file mode 100644
index 0000000..00c8b98
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 284305, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.350545883178711, "TIME_S_1KI": 0.03640648558125503, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1004.1447986841201, "W": 73.05, "J_1KI": 3.5319280304043903, "W_1KI": 0.2569423682313009, "W_D": 56.574, "J_D": 777.6658157529831, "W_D_1KI": 0.19899052074356766, "J_D_1KI": 0.0006999191739278861}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output
new file mode 100644
index 0000000..cbcf9b7
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_1e-05.output
@@ -0,0 +1,1307 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.019997835159301758}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ...,  999, 1000, 1000]),
+       col_indices=tensor([7109, 2385, 2417, 5435, 3828, 4759, 3881, 3125, 6359,
+                           8100, 8295, 5616, 9929, 4079, 7787,  359, 8328, 6707,
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.8851, 0.6493, 0.1793,  ..., 0.2200, 0.7121, 0.0570])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 0.019997835159301758 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52505', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.9391217231750488}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
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+                      0.6919, 0.7665, 0.1665, 0.5380, 0.1084, 0.7142]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.5397, 0.8345, 0.2583,  ..., 0.2923, 0.3741, 0.4815])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 1.9391217231750488 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '284305', '-ss', '10000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 1000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.350545883178711}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1000, 1000, 1000]),
+       col_indices=tensor([5455, 5592, 4109,  222, 9693, 3577, 9334, 9406, 2137,
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+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4825, 0.6787, 0.3927,  ..., 0.9418, 0.0965, 0.0788])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.350545883178711 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
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+                      3.7007e-02, 1.2024e-01, 9.1569e-01, 8.0422e-01,
+                      4.8728e-01, 3.3584e-01, 5.0960e-01, 3.6273e-01,
+                      6.1914e-01, 3.8996e-01, 5.9564e-01, 4.2090e-01,
+                      8.0087e-01, 6.1589e-01, 5.8173e-01, 6.2723e-01,
+                      6.9793e-01, 1.5997e-01, 1.5756e-01, 9.2695e-02,
+                      7.7600e-01, 5.7205e-01, 5.7808e-01, 5.9198e-02,
+                      8.2480e-01, 2.8009e-01, 8.4499e-02, 3.5912e-01,
+                      7.7211e-01, 1.0716e-01, 7.1486e-01, 5.0595e-01,
+                      6.5373e-02, 7.7498e-01, 9.1086e-03, 4.0288e-01,
+                      6.5420e-01, 2.4228e-01, 8.2790e-01, 1.6179e-02,
+                      4.5517e-01, 2.3154e-01, 4.0528e-01, 9.9680e-01,
+                      1.9648e-01, 8.9156e-01, 4.9073e-01, 6.1991e-01,
+                      5.2047e-01, 2.0881e-01, 9.4192e-01, 4.9260e-01]),
+       size=(10000, 10000), nnz=1000, layout=torch.sparse_csr)
+tensor([0.4825, 0.6787, 0.3927,  ..., 0.9418, 0.0965, 0.0788])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 1000
+Density: 1e-05
+Time: 10.350545883178711 seconds
+
+[18.41, 22.21, 18.1, 18.25, 17.88, 18.06, 18.18, 18.5, 18.02, 17.84]
+[73.05]
+13.74599313735962
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 284305, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.350545883178711, 'TIME_S_1KI': 0.03640648558125503, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1004.1447986841201, 'W': 73.05}
+[18.41, 22.21, 18.1, 18.25, 17.88, 18.06, 18.18, 18.5, 18.02, 17.84, 18.25, 17.97, 18.0, 17.86, 18.03, 17.9, 18.06, 17.99, 17.95, 18.62]
+329.52
+16.476
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 284305, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 1000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.350545883178711, 'TIME_S_1KI': 0.03640648558125503, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1004.1447986841201, 'W': 73.05, 'J_1KI': 3.5319280304043903, 'W_1KI': 0.2569423682313009, 'W_D': 56.574, 'J_D': 777.6658157529831, 'W_D_1KI': 0.19899052074356766, 'J_D_1KI': 0.0006999191739278861}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.json
new file mode 100644
index 0000000..3fe7967
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 264429, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.455932378768921, "TIME_S_1KI": 0.03954154944718212, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1008.905144047737, "W": 73.49, "J_1KI": 3.8154103522977323, "W_1KI": 0.2779195927829398, "W_D": 57.09475, "J_D": 783.8234722155332, "W_D_1KI": 0.21591712709271677, "J_D_1KI": 0.0008165410264861901}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.output
new file mode 100644
index 0000000..39a6146
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_10000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.01993107795715332}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    1,    1,  ..., 4997, 4998, 5000]),
+       col_indices=tensor([9328, 8573, 6400,  ..., 9443, 3853, 6322]),
+       values=tensor([0.9995, 0.8210, 0.4187,  ..., 0.1342, 0.0596, 0.9033]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.6106, 0.0696, 0.9188,  ..., 0.7595, 0.3313, 0.0671])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 0.01993107795715332 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52681', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 2.0918641090393066}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 4998, 4998, 5000]),
+       col_indices=tensor([2599, 3812, 3885,  ...,  206, 4492, 5501]),
+       values=tensor([0.4008, 0.6783, 0.6051,  ..., 0.8606, 0.8114, 0.5557]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.4634, 0.0164, 0.5073,  ..., 0.3776, 0.2676, 0.0715])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 2.0918641090393066 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '264429', '-ss', '10000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [10000, 10000], "MATRIX_ROWS": 10000, "MATRIX_SIZE": 100000000, "MATRIX_NNZ": 5000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.455932378768921}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([3612, 1318, 1874,  ..., 9308, 8111, 3978]),
+       values=tensor([0.0263, 0.8701, 0.1587,  ..., 0.1521, 0.0294, 0.0367]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.4791, 0.9785, 0.1725,  ..., 0.5898, 0.0968, 0.4149])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.455932378768921 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 5000, 5000, 5000]),
+       col_indices=tensor([3612, 1318, 1874,  ..., 9308, 8111, 3978]),
+       values=tensor([0.0263, 0.8701, 0.1587,  ..., 0.1521, 0.0294, 0.0367]),
+       size=(10000, 10000), nnz=5000, layout=torch.sparse_csr)
+tensor([0.4791, 0.9785, 0.1725,  ..., 0.5898, 0.0968, 0.4149])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([10000, 10000])
+Rows: 10000
+Size: 100000000
+NNZ: 5000
+Density: 5e-05
+Time: 10.455932378768921 seconds
+
+[19.02, 17.97, 18.1, 18.25, 17.93, 18.7, 18.16, 18.15, 18.22, 17.98]
+[73.49]
+13.728468418121338
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 264429, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.455932378768921, 'TIME_S_1KI': 0.03954154944718212, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1008.905144047737, 'W': 73.49}
+[19.02, 17.97, 18.1, 18.25, 17.93, 18.7, 18.16, 18.15, 18.22, 17.98, 18.41, 18.7, 18.23, 17.8, 17.94, 18.94, 18.01, 18.04, 18.02, 18.08]
+327.905
+16.395249999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 264429, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [10000, 10000], 'MATRIX_ROWS': 10000, 'MATRIX_SIZE': 100000000, 'MATRIX_NNZ': 5000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.455932378768921, 'TIME_S_1KI': 0.03954154944718212, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1008.905144047737, 'W': 73.49, 'J_1KI': 3.8154103522977323, 'W_1KI': 0.2779195927829398, 'W_D': 57.09475, 'J_D': 783.8234722155332, 'W_D_1KI': 0.21591712709271677, 'J_D_1KI': 0.0008165410264861901}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.json
new file mode 100644
index 0000000..35c6334
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 693, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.433570146560669, "TIME_S_1KI": 15.055656777143822, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2886.4447816610336, "W": 54.5, "J_1KI": 4165.1439850808565, "W_1KI": 78.64357864357864, "W_D": 38.16675, "J_D": 2021.3984655130505, "W_D_1KI": 55.074675324675326, "J_D_1KI": 79.47283596634246}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.output
new file mode 100644
index 0000000..f2efbf0
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_0.0001.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.8274271488189697}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       57,       98,  ..., 24999897,
+                            24999949, 25000000]),
+       col_indices=tensor([  2496,   8518,  12544,  ..., 449306, 467869,
+                           486714]),
+       values=tensor([0.2667, 0.8213, 0.8309,  ..., 0.8074, 0.6926, 0.7796]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.0179, 0.7693, 0.5874,  ..., 0.4128, 0.7472, 0.6195])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 1.8274271488189697 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '574', '-ss', '500000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 8.696394205093384}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       45,       88,  ..., 24999889,
+                            24999948, 25000000]),
+       col_indices=tensor([ 21942,  37292,  56785,  ..., 479111, 486535,
+                           489318]),
+       values=tensor([0.4753, 0.7614, 0.3285,  ..., 0.3162, 0.0061, 0.9591]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.2582, 0.0329, 0.5597,  ..., 0.0495, 0.5298, 0.4237])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 8.696394205093384 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '693', '-ss', '500000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.433570146560669}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       38,       84,  ..., 24999899,
+                            24999948, 25000000]),
+       col_indices=tensor([ 30660,  43953,  94811,  ..., 484319, 487924,
+                           499108]),
+       values=tensor([0.4696, 0.5982, 0.2681,  ..., 0.1240, 0.7008, 0.8579]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3176, 0.2592, 0.6200,  ..., 0.0886, 0.2852, 0.1534])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.433570146560669 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       38,       84,  ..., 24999899,
+                            24999948, 25000000]),
+       col_indices=tensor([ 30660,  43953,  94811,  ..., 484319, 487924,
+                           499108]),
+       values=tensor([0.4696, 0.5982, 0.2681,  ..., 0.1240, 0.7008, 0.8579]),
+       size=(500000, 500000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.3176, 0.2592, 0.6200,  ..., 0.0886, 0.2852, 0.1534])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 25000000
+Density: 0.0001
+Time: 10.433570146560669 seconds
+
+[18.16, 18.09, 17.95, 18.13, 17.83, 18.01, 18.2, 17.99, 18.23, 17.93]
+[54.5]
+52.962289571762085
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.433570146560669, 'TIME_S_1KI': 15.055656777143822, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2886.4447816610336, 'W': 54.5}
+[18.16, 18.09, 17.95, 18.13, 17.83, 18.01, 18.2, 17.99, 18.23, 17.93, 18.48, 17.91, 18.03, 18.08, 18.03, 18.09, 18.34, 19.26, 18.16, 18.1]
+326.66499999999996
+16.33325
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 693, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.433570146560669, 'TIME_S_1KI': 15.055656777143822, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2886.4447816610336, 'W': 54.5, 'J_1KI': 4165.1439850808565, 'W_1KI': 78.64357864357864, 'W_D': 38.16675, 'J_D': 2021.3984655130505, 'W_D_1KI': 55.074675324675326, 'J_D_1KI': 79.47283596634246}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json
new file mode 100644
index 0000000..c977413
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 8054, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.420526504516602, "TIME_S_1KI": 1.293832444067122, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1257.315396785736, "W": 87.34, "J_1KI": 156.11067752492377, "W_1KI": 10.844300968462875, "W_D": 70.58375000000001, "J_D": 1016.0984158217908, "W_D_1KI": 8.763813012167867, "J_D_1KI": 1.0881317372942474}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output
new file mode 100644
index 0000000..2324509
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_1e-05.output
@@ -0,0 +1,89 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.1425309181213379}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,       8,  ..., 2499994,
+                            2499996, 2500000]),
+       col_indices=tensor([ 55676, 267462, 335220,  ...,  59414, 387658,
+                           467981]),
+       values=tensor([0.3669, 0.3244, 0.1572,  ..., 0.0615, 0.1330, 0.1317]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.2232, 0.7011, 0.4607,  ..., 0.0263, 0.5751, 0.5574])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 0.1425309181213379 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '7366', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.601978540420532}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,      17,  ..., 2499992,
+                            2499998, 2500000]),
+       col_indices=tensor([ 78904, 197792, 264056,  ..., 387862, 102468,
+                           277870]),
+       values=tensor([0.6219, 0.1671, 0.9871,  ..., 0.4185, 0.9305, 0.2778]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0210, 0.3595, 0.6743,  ..., 0.0036, 0.8741, 0.0347])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 9.601978540420532 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8054', '-ss', '500000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.420526504516602}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,       7,  ..., 2499986,
+                            2499993, 2500000]),
+       col_indices=tensor([136514, 185390, 204506,  ..., 365577, 371722,
+                           449843]),
+       values=tensor([0.2143, 0.0864, 0.9943,  ..., 0.6371, 0.6570, 0.8441]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0733, 0.2994, 0.4999,  ..., 0.8006, 0.1699, 0.8850])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.420526504516602 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,       5,       7,  ..., 2499986,
+                            2499993, 2500000]),
+       col_indices=tensor([136514, 185390, 204506,  ..., 365577, 371722,
+                           449843]),
+       values=tensor([0.2143, 0.0864, 0.9943,  ..., 0.6371, 0.6570, 0.8441]),
+       size=(500000, 500000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0733, 0.2994, 0.4999,  ..., 0.8006, 0.1699, 0.8850])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 2500000
+Density: 1e-05
+Time: 10.420526504516602 seconds
+
+[18.47, 17.91, 18.18, 18.64, 18.09, 18.11, 18.17, 18.18, 22.06, 17.96]
+[87.34]
+14.395642280578613
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8054, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.420526504516602, 'TIME_S_1KI': 1.293832444067122, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1257.315396785736, 'W': 87.34}
+[18.47, 17.91, 18.18, 18.64, 18.09, 18.11, 18.17, 18.18, 22.06, 17.96, 18.46, 18.6, 18.06, 19.85, 20.28, 18.18, 18.02, 17.82, 18.61, 17.84]
+335.125
+16.75625
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 8054, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.420526504516602, 'TIME_S_1KI': 1.293832444067122, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1257.315396785736, 'W': 87.34, 'J_1KI': 156.11067752492377, 'W_1KI': 10.844300968462875, 'W_D': 70.58375000000001, 'J_D': 1016.0984158217908, 'W_D_1KI': 8.763813012167867, 'J_D_1KI': 1.0881317372942474}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.json
new file mode 100644
index 0000000..72c367a
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1363, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.215147256851196, "TIME_S_1KI": 7.494605470910636, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1461.7694299888612, "W": 77.68, "J_1KI": 1072.4647322001917, "W_1KI": 56.99192956713133, "W_D": 61.057, "J_D": 1148.960557245493, "W_D_1KI": 44.796038151137196, "J_D_1KI": 32.865765334656786}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.output
new file mode 100644
index 0000000..097beff
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_500000_5e-05.output
@@ -0,0 +1,68 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '500000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.7700090408325195}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       24,       55,  ..., 12499948,
+                            12499975, 12500000]),
+       col_indices=tensor([  4417,  27723,  55822,  ..., 442008, 448310,
+                           496598]),
+       values=tensor([0.6543, 0.3065, 0.8363,  ..., 0.0104, 0.9892, 0.0257]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.5546, 0.9568, 0.6697,  ..., 0.4050, 0.9738, 0.6596])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 0.7700090408325195 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1363', '-ss', '500000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [500000, 500000], "MATRIX_ROWS": 500000, "MATRIX_SIZE": 250000000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.215147256851196}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       32,       58,  ..., 12499956,
+                            12499981, 12500000]),
+       col_indices=tensor([  7736,  12237,  33305,  ..., 443222, 470958,
+                           475326]),
+       values=tensor([0.9583, 0.2636, 0.0225,  ..., 0.4084, 0.8296, 0.8114]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4848, 0.9833, 0.6868,  ..., 0.4430, 0.1817, 0.0586])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.215147256851196 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,       32,       58,  ..., 12499956,
+                            12499981, 12500000]),
+       col_indices=tensor([  7736,  12237,  33305,  ..., 443222, 470958,
+                           475326]),
+       values=tensor([0.9583, 0.2636, 0.0225,  ..., 0.4084, 0.8296, 0.8114]),
+       size=(500000, 500000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.4848, 0.9833, 0.6868,  ..., 0.4430, 0.1817, 0.0586])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([500000, 500000])
+Rows: 500000
+Size: 250000000000
+NNZ: 12500000
+Density: 5e-05
+Time: 10.215147256851196 seconds
+
+[18.64, 18.68, 18.32, 18.11, 18.55, 17.92, 17.92, 19.0, 18.5, 17.99]
+[77.68]
+18.817835092544556
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1363, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.215147256851196, 'TIME_S_1KI': 7.494605470910636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1461.7694299888612, 'W': 77.68}
+[18.64, 18.68, 18.32, 18.11, 18.55, 17.92, 17.92, 19.0, 18.5, 17.99, 18.42, 18.14, 18.35, 18.41, 18.1, 18.07, 18.11, 21.75, 17.97, 18.07]
+332.46000000000004
+16.623
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1363, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [500000, 500000], 'MATRIX_ROWS': 500000, 'MATRIX_SIZE': 250000000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.215147256851196, 'TIME_S_1KI': 7.494605470910636, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1461.7694299888612, 'W': 77.68, 'J_1KI': 1072.4647322001917, 'W_1KI': 56.99192956713133, 'W_D': 61.057, 'J_D': 1148.960557245493, 'W_D_1KI': 44.796038151137196, 'J_D_1KI': 32.865765334656786}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json
new file mode 100644
index 0000000..393ceed
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 80365, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.785846710205078, "TIME_S_1KI": 0.1342107473428119, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1142.0150223541261, "W": 83.68, "J_1KI": 14.210353043664854, "W_1KI": 1.041249300068438, "W_D": 67.33675000000001, "J_D": 918.9720370041133, "W_D_1KI": 0.8378865177627077, "J_D_1KI": 0.01042601278868547}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output
new file mode 100644
index 0000000..ace565c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.0001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.031140804290771484}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,     10,  ..., 249988, 249996,
+                            250000]),
+       col_indices=tensor([ 3938, 11827, 18410,  ..., 25331, 39292, 43613]),
+       values=tensor([0.0696, 0.4480, 0.5528,  ..., 0.5051, 0.2445, 0.3952]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.1610, 0.2872, 0.6159,  ..., 0.3941, 0.7048, 0.2210])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 0.031140804290771484 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '33717', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 4.405243873596191}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      8,     16,  ..., 249992, 249993,
+                            250000]),
+       col_indices=tensor([ 4798,  5191,  8748,  ..., 31389, 41207, 45142]),
+       values=tensor([0.4595, 0.5274, 0.3770,  ..., 0.1787, 0.1620, 0.3761]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.7848, 0.4939, 0.9259,  ..., 0.5597, 0.9743, 0.2454])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 4.405243873596191 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '80365', '-ss', '50000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.785846710205078}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,      6,  ..., 249987, 249993,
+                            250000]),
+       col_indices=tensor([ 3036, 18920, 36950,  ..., 33008, 35825, 42083]),
+       values=tensor([0.1216, 0.1218, 0.6996,  ..., 0.2614, 0.2596, 0.0475]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0022, 0.5368, 0.2941,  ..., 0.2951, 0.8515, 0.2587])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.785846710205078 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,      6,  ..., 249987, 249993,
+                            250000]),
+       col_indices=tensor([ 3036, 18920, 36950,  ..., 33008, 35825, 42083]),
+       values=tensor([0.1216, 0.1218, 0.6996,  ..., 0.2614, 0.2596, 0.0475]),
+       size=(50000, 50000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.0022, 0.5368, 0.2941,  ..., 0.2951, 0.8515, 0.2587])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 250000
+Density: 0.0001
+Time: 10.785846710205078 seconds
+
+[18.57, 17.87, 18.3, 17.99, 18.1, 18.03, 18.33, 18.16, 18.09, 17.89]
+[83.68]
+13.647407054901123
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 80365, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.785846710205078, 'TIME_S_1KI': 0.1342107473428119, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1142.0150223541261, 'W': 83.68}
+[18.57, 17.87, 18.3, 17.99, 18.1, 18.03, 18.33, 18.16, 18.09, 17.89, 18.23, 17.91, 18.25, 19.1, 18.2, 17.88, 18.14, 18.11, 18.11, 17.9]
+326.865
+16.34325
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 80365, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.785846710205078, 'TIME_S_1KI': 0.1342107473428119, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1142.0150223541261, 'W': 83.68, 'J_1KI': 14.210353043664854, 'W_1KI': 1.041249300068438, 'W_D': 67.33675000000001, 'J_D': 918.9720370041133, 'W_D_1KI': 0.8378865177627077, 'J_D_1KI': 0.01042601278868547}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json
new file mode 100644
index 0000000..3d88fb8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 17258, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.52219271659851, "TIME_S_1KI": 0.6096994273147822, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1290.1128985500336, "W": 87.73, "J_1KI": 74.75448479256191, "W_1KI": 5.083439564260054, "W_D": 71.3875, "J_D": 1049.7883796334268, "W_D_1KI": 4.136487426121219, "J_D_1KI": 0.23968521416857222}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output
new file mode 100644
index 0000000..c067392
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.001.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.07522106170654297}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      54,      95,  ..., 2499899,
+                            2499951, 2500000]),
+       col_indices=tensor([  245,   316,   650,  ..., 46425, 47933, 49262]),
+       values=tensor([0.3598, 0.8732, 0.2112,  ..., 0.2076, 0.2855, 0.8514]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6227, 0.9680, 0.6855,  ..., 0.8971, 0.9917, 0.2060])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 0.07522106170654297 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '13958', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 8.492224931716919}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      52,      97,  ..., 2499905,
+                            2499950, 2500000]),
+       col_indices=tensor([ 3061,  5035,  6476,  ..., 48999, 49661, 49813]),
+       values=tensor([0.5243, 0.0379, 0.3507,  ..., 0.2954, 0.9764, 0.8519]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.8088, 0.8530, 0.3688,  ..., 0.5327, 0.1148, 0.7333])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 8.492224931716919 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '17258', '-ss', '50000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.52219271659851}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      60,      98,  ..., 2499898,
+                            2499955, 2500000]),
+       col_indices=tensor([  336,   813,  2467,  ..., 44805, 45101, 46338]),
+       values=tensor([0.1989, 0.3364, 0.1097,  ..., 0.3897, 0.4637, 0.0665]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6060, 0.7426, 0.0681,  ..., 0.1484, 0.6293, 0.7345])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.52219271659851 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,      60,      98,  ..., 2499898,
+                            2499955, 2500000]),
+       col_indices=tensor([  336,   813,  2467,  ..., 44805, 45101, 46338]),
+       values=tensor([0.1989, 0.3364, 0.1097,  ..., 0.3897, 0.4637, 0.0665]),
+       size=(50000, 50000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.6060, 0.7426, 0.0681,  ..., 0.1484, 0.6293, 0.7345])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 2500000
+Density: 0.001
+Time: 10.52219271659851 seconds
+
+[18.24, 17.95, 18.03, 17.9, 18.31, 18.2, 18.96, 17.99, 17.74, 18.24]
+[87.73]
+14.705492973327637
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17258, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.52219271659851, 'TIME_S_1KI': 0.6096994273147822, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1290.1128985500336, 'W': 87.73}
+[18.24, 17.95, 18.03, 17.9, 18.31, 18.2, 18.96, 17.99, 17.74, 18.24, 18.79, 18.06, 18.38, 18.13, 18.12, 18.05, 18.08, 18.09, 18.27, 17.91]
+326.85
+16.3425
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 17258, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.52219271659851, 'TIME_S_1KI': 0.6096994273147822, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1290.1128985500336, 'W': 87.73, 'J_1KI': 74.75448479256191, 'W_1KI': 5.083439564260054, 'W_D': 71.3875, 'J_D': 1049.7883796334268, 'W_D_1KI': 4.136487426121219, 'J_D_1KI': 0.23968521416857222}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.json
new file mode 100644
index 0000000..00dd221
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 1116, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.347081184387207, "TIME_S_1KI": 9.27157812221076, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 2825.247347984314, "W": 53.89, "J_1KI": 2531.5836451472346, "W_1KI": 48.288530465949826, "W_D": 37.37625, "J_D": 1959.4943624067305, "W_D_1KI": 33.491263440860216, "J_D_1KI": 30.010092688942844}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.output
new file mode 100644
index 0000000..1b20dcd
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_0.01.output
@@ -0,0 +1,86 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 1.2946875095367432}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      488,      979,  ..., 24999008,
+                            24999504, 25000000]),
+       col_indices=tensor([  111,   301,   401,  ..., 49766, 49891, 49958]),
+       values=tensor([2.3418e-01, 9.8131e-01, 8.1298e-03,  ...,
+                      6.0106e-01, 4.2789e-04, 8.6966e-01]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.1063, 0.9255, 0.6653,  ..., 0.3278, 0.7920, 0.4701])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 1.2946875095367432 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '811', '-ss', '50000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 7.6258299350738525}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      521,     1028,  ..., 24999005,
+                            24999496, 25000000]),
+       col_indices=tensor([  169,   333,   382,  ..., 49620, 49646, 49746]),
+       values=tensor([0.1336, 0.2367, 0.6093,  ..., 0.7411, 0.2218, 0.9154]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.7321, 0.9416, 0.5259,  ..., 0.9099, 0.7583, 0.2580])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 7.6258299350738525 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '1116', '-ss', '50000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.347081184387207}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      467,      956,  ..., 24998994,
+                            24999500, 25000000]),
+       col_indices=tensor([   21,   163,   165,  ..., 49855, 49860, 49938]),
+       values=tensor([0.3050, 0.9077, 0.0930,  ..., 0.0680, 0.0415, 0.3010]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.5141, 0.6637, 0.6626,  ..., 0.2332, 0.8993, 0.6899])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.347081184387207 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,      467,      956,  ..., 24998994,
+                            24999500, 25000000]),
+       col_indices=tensor([   21,   163,   165,  ..., 49855, 49860, 49938]),
+       values=tensor([0.3050, 0.9077, 0.0930,  ..., 0.0680, 0.0415, 0.3010]),
+       size=(50000, 50000), nnz=25000000, layout=torch.sparse_csr)
+tensor([0.5141, 0.6637, 0.6626,  ..., 0.2332, 0.8993, 0.6899])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000000
+Density: 0.01
+Time: 10.347081184387207 seconds
+
+[18.41, 17.99, 18.33, 18.02, 17.96, 17.91, 21.56, 18.23, 18.08, 17.76]
+[53.89]
+52.42618942260742
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1116, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.347081184387207, 'TIME_S_1KI': 9.27157812221076, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2825.247347984314, 'W': 53.89}
+[18.41, 17.99, 18.33, 18.02, 17.96, 17.91, 21.56, 18.23, 18.08, 17.76, 18.54, 18.29, 18.11, 17.98, 18.09, 18.71, 18.38, 18.01, 18.26, 18.02]
+330.27500000000003
+16.51375
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 1116, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.347081184387207, 'TIME_S_1KI': 9.27157812221076, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 2825.247347984314, 'W': 53.89, 'J_1KI': 2531.5836451472346, 'W_1KI': 48.288530465949826, 'W_D': 37.37625, 'J_D': 1959.4943624067305, 'W_D_1KI': 33.491263440860216, 'J_D_1KI': 30.010092688942844}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json
new file mode 100644
index 0000000..3827350
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 114513, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.690773725509644, "TIME_S_1KI": 0.09335860317614283, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1104.0986349916457, "W": 76.13, "J_1KI": 9.641688148870832, "W_1KI": 0.6648153484757189, "W_D": 59.82149999999999, "J_D": 867.5796202962398, "W_D_1KI": 0.5223992035838725, "J_D_1KI": 0.004561920511940762}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output
new file mode 100644
index 0000000..1524e8c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_1e-05.output
@@ -0,0 +1,100 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.025936126708984375}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([20193,  9953, 42880,  ..., 12429,  7497, 42914]),
+       values=tensor([0.2197, 0.8269, 0.1857,  ..., 0.3505, 0.1509, 0.9771]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.1918, 0.1249, 0.1152,  ..., 0.8034, 0.5794, 0.7689])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 0.025936126708984375 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '40484', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 3.938389301300049}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     2,  ..., 24997, 24998, 25000]),
+       col_indices=tensor([10460, 10153,  3528,  ..., 24271,  7757, 10191]),
+       values=tensor([0.9506, 0.4415, 0.5851,  ..., 0.4538, 0.6997, 0.3353]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8879, 0.8298, 0.7702,  ..., 0.5204, 0.0041, 0.9281])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 3.938389301300049 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '107932', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 9.896521091461182}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     1,     1,  ..., 24999, 24999, 25000]),
+       col_indices=tensor([28383, 18616, 44948,  ..., 18982, 36427, 31817]),
+       values=tensor([0.7119, 0.6692, 0.1695,  ..., 0.9276, 0.2360, 0.8672]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.3408, 0.5698, 0.4224,  ..., 0.7704, 0.6104, 0.7559])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 9.896521091461182 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '114513', '-ss', '50000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.690773725509644}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 6936, 24119, 42629,  ..., 33437,  2193,  4338]),
+       values=tensor([0.2031, 0.4334, 0.3517,  ..., 0.1037, 0.1982, 0.4022]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0121, 0.3083, 0.6972,  ..., 0.1499, 0.8195, 0.8871])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.690773725509644 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     0,     0,  ..., 25000, 25000, 25000]),
+       col_indices=tensor([ 6936, 24119, 42629,  ..., 33437,  2193,  4338]),
+       values=tensor([0.2031, 0.4334, 0.3517,  ..., 0.1037, 0.1982, 0.4022]),
+       size=(50000, 50000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.0121, 0.3083, 0.6972,  ..., 0.1499, 0.8195, 0.8871])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 25000
+Density: 1e-05
+Time: 10.690773725509644 seconds
+
+[18.43, 18.05, 18.18, 17.94, 17.98, 18.17, 18.18, 17.96, 18.08, 18.28]
+[76.13]
+14.502806186676025
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 114513, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.690773725509644, 'TIME_S_1KI': 0.09335860317614283, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1104.0986349916457, 'W': 76.13}
+[18.43, 18.05, 18.18, 17.94, 17.98, 18.17, 18.18, 17.96, 18.08, 18.28, 18.27, 17.98, 18.37, 18.47, 18.2, 17.9, 18.1, 18.12, 18.02, 17.96]
+326.17
+16.308500000000002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 114513, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.690773725509644, 'TIME_S_1KI': 0.09335860317614283, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1104.0986349916457, 'W': 76.13, 'J_1KI': 9.641688148870832, 'W_1KI': 0.6648153484757189, 'W_D': 59.82149999999999, 'J_D': 867.5796202962398, 'W_D_1KI': 0.5223992035838725, 'J_D_1KI': 0.004561920511940762}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.json
new file mode 100644
index 0000000..7635079
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 87123, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.505861759185791, "TIME_S_1KI": 0.12058654728585783, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1083.1826461410521, "W": 80.24, "J_1KI": 12.43279783915903, "W_1KI": 0.9209967517188342, "W_D": 63.674249999999994, "J_D": 859.5568619896172, "W_D_1KI": 0.7308546537653662, "J_D_1KI": 0.00838876822154157}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.output
new file mode 100644
index 0000000..089ad96
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_50000_5e-05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.029273509979248047}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      5,      7,  ..., 124995, 124998,
+                            125000]),
+       col_indices=tensor([  551, 18742, 22548,  ..., 32794, 16422, 37041]),
+       values=tensor([0.7421, 0.1633, 0.4685,  ..., 0.4955, 0.1690, 0.9373]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.5671, 0.1718, 0.7006,  ..., 0.0603, 0.4651, 0.4190])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 0.029273509979248047 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '35868', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 4.322763681411743}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      6,      7,  ..., 124993, 124996,
+                            125000]),
+       col_indices=tensor([10292, 17103, 19384,  ..., 21480, 22459, 30474]),
+       values=tensor([0.3992, 0.1013, 0.2691,  ..., 0.1460, 0.2288, 0.5075]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.9479, 0.9531, 0.4260,  ..., 0.3198, 0.2541, 0.8697])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 4.322763681411743 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '87123', '-ss', '50000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [50000, 50000], "MATRIX_ROWS": 50000, "MATRIX_SIZE": 2500000000, "MATRIX_NNZ": 125000, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.505861759185791}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ..., 124993, 124998,
+                            125000]),
+       col_indices=tensor([ 2935, 11122, 10966,  ..., 49613,  4331, 15007]),
+       values=tensor([0.3574, 0.4392, 0.6710,  ..., 0.1033, 0.1015, 0.5102]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.0826, 0.0461, 0.6873,  ..., 0.1063, 0.7746, 0.5118])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.505861759185791 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,      0,      2,  ..., 124993, 124998,
+                            125000]),
+       col_indices=tensor([ 2935, 11122, 10966,  ..., 49613,  4331, 15007]),
+       values=tensor([0.3574, 0.4392, 0.6710,  ..., 0.1033, 0.1015, 0.5102]),
+       size=(50000, 50000), nnz=125000, layout=torch.sparse_csr)
+tensor([0.0826, 0.0461, 0.6873,  ..., 0.1063, 0.7746, 0.5118])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([50000, 50000])
+Rows: 50000
+Size: 2500000000
+NNZ: 125000
+Density: 5e-05
+Time: 10.505861759185791 seconds
+
+[18.58, 18.04, 18.0, 17.91, 17.9, 20.59, 18.05, 18.07, 17.88, 18.18]
+[80.24]
+13.499285221099854
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87123, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.505861759185791, 'TIME_S_1KI': 0.12058654728585783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1083.1826461410521, 'W': 80.24}
+[18.58, 18.04, 18.0, 17.91, 17.9, 20.59, 18.05, 18.07, 17.88, 18.18, 18.74, 21.37, 18.26, 18.13, 18.12, 17.84, 18.06, 17.91, 18.35, 18.17]
+331.315
+16.56575
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 87123, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [50000, 50000], 'MATRIX_ROWS': 50000, 'MATRIX_SIZE': 2500000000, 'MATRIX_NNZ': 125000, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.505861759185791, 'TIME_S_1KI': 0.12058654728585783, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1083.1826461410521, 'W': 80.24, 'J_1KI': 12.43279783915903, 'W_1KI': 0.9209967517188342, 'W_D': 63.674249999999994, 'J_D': 859.5568619896172, 'W_D_1KI': 0.7308546537653662, 'J_D_1KI': 0.00838876822154157}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json
new file mode 100644
index 0000000..c76a143
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 334626, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.109118700027466, "TIME_S_1KI": 0.030210200940833844, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1025.1804789018631, "W": 73.29, "J_1KI": 3.063660561049838, "W_1KI": 0.219020637965968, "W_D": 56.847750000000005, "J_D": 795.1862951220274, "W_D_1KI": 0.16988443814885876, "J_D_1KI": 0.0005076845139016656}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output
new file mode 100644
index 0000000..81b5928
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.0001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 0.020055532455444336}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2499, 2500, 2500]),
+       col_indices=tensor([2122, 3396, 4900,  ..., 3006, 1251, 2017]),
+       values=tensor([0.1868, 0.0259, 0.3673,  ..., 0.0909, 0.5358, 0.3608]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.5194, 0.1996, 0.4802,  ..., 0.7962, 0.4168, 0.5561])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 0.020055532455444336 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '52354', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 1.6427783966064453}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2498, 2499, 2500]),
+       col_indices=tensor([2755, 4785,  642,  ...,  761, 1671, 4009]),
+       values=tensor([0.6216, 0.3711, 0.5927,  ..., 0.4412, 0.8122, 0.2675]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.4766, 0.5997, 0.2696,  ..., 0.3490, 0.2681, 0.0383])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 1.6427783966064453 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '334626', '-ss', '5000', '-sd', '0.0001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500, "MATRIX_DENSITY": 0.0001, "TIME_S": 10.109118700027466}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([3166, 2984, 4242,  ..., 1801,  191, 2968]),
+       values=tensor([0.8061, 0.6429, 0.9344,  ..., 0.0259, 0.3545, 0.7535]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.9083, 0.2521, 0.7196,  ..., 0.0784, 0.7825, 0.2390])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.109118700027466 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 2500, 2500, 2500]),
+       col_indices=tensor([3166, 2984, 4242,  ..., 1801,  191, 2968]),
+       values=tensor([0.8061, 0.6429, 0.9344,  ..., 0.0259, 0.3545, 0.7535]),
+       size=(5000, 5000), nnz=2500, layout=torch.sparse_csr)
+tensor([0.9083, 0.2521, 0.7196,  ..., 0.0784, 0.7825, 0.2390])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500
+Density: 0.0001
+Time: 10.109118700027466 seconds
+
+[20.29, 18.13, 18.23, 18.27, 18.36, 17.99, 18.37, 18.01, 18.07, 17.87]
+[73.29]
+13.987999439239502
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 334626, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.109118700027466, 'TIME_S_1KI': 0.030210200940833844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1025.1804789018631, 'W': 73.29}
+[20.29, 18.13, 18.23, 18.27, 18.36, 17.99, 18.37, 18.01, 18.07, 17.87, 18.97, 18.06, 18.34, 18.0, 18.27, 18.07, 18.01, 18.54, 18.55, 18.02]
+328.845
+16.44225
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 334626, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500, 'MATRIX_DENSITY': 0.0001, 'TIME_S': 10.109118700027466, 'TIME_S_1KI': 0.030210200940833844, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1025.1804789018631, 'W': 73.29, 'J_1KI': 3.063660561049838, 'W_1KI': 0.219020637965968, 'W_D': 56.847750000000005, 'J_D': 795.1862951220274, 'W_D_1KI': 0.16988443814885876, 'J_D_1KI': 0.0005076845139016656}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json
new file mode 100644
index 0000000..417052f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 248882, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.432960033416748, "TIME_S_1KI": 0.041919303257836035, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1049.6097160863876, "W": 75.02, "J_1KI": 4.2172986237911445, "W_1KI": 0.3014279859531826, "W_D": 58.4725, "J_D": 818.0925636345148, "W_D_1KI": 0.2349406546074043, "J_D_1KI": 0.0009439841153936576}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output
new file mode 100644
index 0000000..121e30b
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.001.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 0.018964052200317383}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,    14,  ..., 24990, 24995, 25000]),
+       col_indices=tensor([ 484, 2538, 3016,  ..., 2694, 4380, 4909]),
+       values=tensor([0.3483, 0.1743, 0.1939,  ..., 0.2265, 0.8602, 0.7977]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.6164, 0.4905, 0.7241,  ..., 0.3672, 0.4239, 0.9077])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 0.018964052200317383 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '55367', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 2.335857391357422}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,     4,     9,  ..., 24986, 24994, 25000]),
+       col_indices=tensor([1163, 1240, 1422,  ..., 4522, 4571, 4830]),
+       values=tensor([0.2729, 0.7937, 0.6768,  ..., 0.2019, 0.8649, 0.0759]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.9266, 0.1779, 0.1408,  ..., 0.8566, 0.7762, 0.0695])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 2.335857391357422 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '248882', '-ss', '5000', '-sd', '0.001']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 25000, "MATRIX_DENSITY": 0.001, "TIME_S": 10.432960033416748}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    10,    15,  ..., 24986, 24991, 25000]),
+       col_indices=tensor([ 131,  267,  600,  ..., 3068, 3643, 3839]),
+       values=tensor([0.7605, 0.7816, 0.2401,  ..., 0.7557, 0.2099, 0.5290]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8976, 0.4401, 0.6879,  ..., 0.0741, 0.3573, 0.5052])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.432960033416748 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([    0,    10,    15,  ..., 24986, 24991, 25000]),
+       col_indices=tensor([ 131,  267,  600,  ..., 3068, 3643, 3839]),
+       values=tensor([0.7605, 0.7816, 0.2401,  ..., 0.7557, 0.2099, 0.5290]),
+       size=(5000, 5000), nnz=25000, layout=torch.sparse_csr)
+tensor([0.8976, 0.4401, 0.6879,  ..., 0.0741, 0.3573, 0.5052])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 25000
+Density: 0.001
+Time: 10.432960033416748 seconds
+
+[18.55, 18.1, 18.04, 18.77, 18.18, 18.14, 18.08, 18.33, 18.19, 18.07]
+[75.02]
+13.991065263748169
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 248882, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.432960033416748, 'TIME_S_1KI': 0.041919303257836035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.6097160863876, 'W': 75.02}
+[18.55, 18.1, 18.04, 18.77, 18.18, 18.14, 18.08, 18.33, 18.19, 18.07, 18.74, 17.95, 18.05, 18.34, 18.14, 19.14, 18.15, 18.14, 20.44, 18.18]
+330.95
+16.5475
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 248882, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 25000, 'MATRIX_DENSITY': 0.001, 'TIME_S': 10.432960033416748, 'TIME_S_1KI': 0.041919303257836035, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1049.6097160863876, 'W': 75.02, 'J_1KI': 4.2172986237911445, 'W_1KI': 0.3014279859531826, 'W_D': 58.4725, 'J_D': 818.0925636345148, 'W_D_1KI': 0.2349406546074043, 'J_D_1KI': 0.0009439841153936576}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json
new file mode 100644
index 0000000..e80d2db
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 163647, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.69955587387085, "TIME_S_1KI": 0.06538192495964393, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1158.7786533069611, "W": 84.04, "J_1KI": 7.080964840827887, "W_1KI": 0.5135443973919473, "W_D": 67.71475000000001, "J_D": 933.6792814614178, "W_D_1KI": 0.41378546505588254, "J_D_1KI": 0.002528524599020346}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output
new file mode 100644
index 0000000..483c400
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.01.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 0.021794557571411133}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     52,    109,  ..., 249903, 249954,
+                            250000]),
+       col_indices=tensor([  37,   94,  126,  ..., 4726, 4735, 4938]),
+       values=tensor([0.6014, 0.9404, 0.8499,  ..., 0.7854, 0.8553, 0.6608]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8748, 0.2409, 0.9137,  ..., 0.2396, 0.5569, 0.4924])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 0.021794557571411133 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '48177', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 3.0911431312561035}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     42,    103,  ..., 249895, 249947,
+                            250000]),
+       col_indices=tensor([ 128,  146,  225,  ..., 4883, 4900, 4933]),
+       values=tensor([0.9316, 0.8049, 0.8923,  ..., 0.5892, 0.9266, 0.8295]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.8118, 0.4201, 0.8184,  ..., 0.0833, 0.7283, 0.6165])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 3.0911431312561035 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '163647', '-ss', '5000', '-sd', '0.01']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250000, "MATRIX_DENSITY": 0.01, "TIME_S": 10.69955587387085}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     65,    110,  ..., 249906, 249948,
+                            250000]),
+       col_indices=tensor([  32,  165,  212,  ..., 4365, 4391, 4539]),
+       values=tensor([0.5399, 0.4522, 0.1183,  ..., 0.3103, 0.6929, 0.7632]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3241, 0.6966, 0.4101,  ..., 0.4425, 0.6108, 0.0322])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.69955587387085 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([     0,     65,    110,  ..., 249906, 249948,
+                            250000]),
+       col_indices=tensor([  32,  165,  212,  ..., 4365, 4391, 4539]),
+       values=tensor([0.5399, 0.4522, 0.1183,  ..., 0.3103, 0.6929, 0.7632]),
+       size=(5000, 5000), nnz=250000, layout=torch.sparse_csr)
+tensor([0.3241, 0.6966, 0.4101,  ..., 0.4425, 0.6108, 0.0322])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250000
+Density: 0.01
+Time: 10.69955587387085 seconds
+
+[18.19, 18.02, 18.18, 18.28, 17.94, 18.08, 18.33, 18.44, 17.82, 18.2]
+[84.04]
+13.788418054580688
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 163647, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.69955587387085, 'TIME_S_1KI': 0.06538192495964393, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1158.7786533069611, 'W': 84.04}
+[18.19, 18.02, 18.18, 18.28, 17.94, 18.08, 18.33, 18.44, 17.82, 18.2, 18.17, 18.08, 18.2, 17.87, 17.92, 18.23, 18.75, 17.8, 18.09, 18.39]
+326.505
+16.32525
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 163647, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250000, 'MATRIX_DENSITY': 0.01, 'TIME_S': 10.69955587387085, 'TIME_S_1KI': 0.06538192495964393, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1158.7786533069611, 'W': 84.04, 'J_1KI': 7.080964840827887, 'W_1KI': 0.5135443973919473, 'W_D': 67.71475000000001, 'J_D': 933.6792814614178, 'W_D_1KI': 0.41378546505588254, 'J_D_1KI': 0.002528524599020346}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json
new file mode 100644
index 0000000..868cce2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 46354, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.460933923721313, "TIME_S_1KI": 0.22567489156753062, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1249.411584854126, "W": 87.62, "J_1KI": 26.95369514721763, "W_1KI": 1.8902360098373387, "W_D": 71.2095, "J_D": 1015.4071473598481, "W_D_1KI": 1.5362104672735903, "J_D_1KI": 0.03314083935094254}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output
new file mode 100644
index 0000000..056b89d
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.05.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 0.03854489326477051}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     268,     547,  ..., 1249522,
+                            1249752, 1250000]),
+       col_indices=tensor([  55,   88,   92,  ..., 4943, 4993, 4995]),
+       values=tensor([0.0205, 0.9162, 0.9855,  ..., 0.3784, 0.4892, 0.4396]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.2348, 0.0514, 0.2847,  ..., 0.2791, 0.0660, 0.1214])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 0.03854489326477051 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '27240', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 6.170231342315674}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     248,     482,  ..., 1249549,
+                            1249769, 1250000]),
+       col_indices=tensor([  15,   21,   37,  ..., 4863, 4919, 4974]),
+       values=tensor([0.5137, 0.1549, 0.4159,  ..., 0.2935, 0.1856, 0.8128]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.7661, 0.7592, 0.8422,  ..., 0.1260, 0.8520, 0.6179])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 6.170231342315674 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '46354', '-ss', '5000', '-sd', '0.05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250000, "MATRIX_DENSITY": 0.05, "TIME_S": 10.460933923721313}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     262,     517,  ..., 1249525,
+                            1249760, 1250000]),
+       col_indices=tensor([   1,   93,  112,  ..., 4957, 4968, 4975]),
+       values=tensor([0.1837, 0.8744, 0.5620,  ..., 0.5820, 0.1645, 0.1343]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.3700, 0.8319, 0.5287,  ..., 0.8208, 0.8249, 0.9051])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.460933923721313 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     262,     517,  ..., 1249525,
+                            1249760, 1250000]),
+       col_indices=tensor([   1,   93,  112,  ..., 4957, 4968, 4975]),
+       values=tensor([0.1837, 0.8744, 0.5620,  ..., 0.5820, 0.1645, 0.1343]),
+       size=(5000, 5000), nnz=1250000, layout=torch.sparse_csr)
+tensor([0.3700, 0.8319, 0.5287,  ..., 0.8208, 0.8249, 0.9051])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250000
+Density: 0.05
+Time: 10.460933923721313 seconds
+
+[18.2, 17.97, 18.75, 20.89, 18.05, 18.04, 17.92, 18.15, 18.09, 18.27]
+[87.62]
+14.25943374633789
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46354, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.460933923721313, 'TIME_S_1KI': 0.22567489156753062, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1249.411584854126, 'W': 87.62}
+[18.2, 17.97, 18.75, 20.89, 18.05, 18.04, 17.92, 18.15, 18.09, 18.27, 18.35, 17.98, 17.96, 18.31, 17.83, 17.89, 18.02, 18.18, 17.85, 17.84]
+328.21000000000004
+16.410500000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 46354, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250000, 'MATRIX_DENSITY': 0.05, 'TIME_S': 10.460933923721313, 'TIME_S_1KI': 0.22567489156753062, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1249.411584854126, 'W': 87.62, 'J_1KI': 26.95369514721763, 'W_1KI': 1.8902360098373387, 'W_D': 71.2095, 'J_D': 1015.4071473598481, 'W_D_1KI': 1.5362104672735903, 'J_D_1KI': 0.03314083935094254}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json
new file mode 100644
index 0000000..30ee0bd
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 19580, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.499455213546753, "TIME_S_1KI": 0.5362336676990169, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1272.879629366398, "W": 87.53, "J_1KI": 65.00917412494371, "W_1KI": 4.470377936670071, "W_D": 70.8715, "J_D": 1030.6282263525725, "W_D_1KI": 3.6195863125638406, "J_D_1KI": 0.18486140513604907}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output
new file mode 100644
index 0000000..e7e4d14
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.1.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 0.06696105003356934}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     497,     967,  ..., 2499018,
+                            2499482, 2500000]),
+       col_indices=tensor([   1,    5,   13,  ..., 4954, 4967, 4978]),
+       values=tensor([0.9405, 0.9423, 0.1495,  ..., 0.5665, 0.9976, 0.7425]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.7464, 0.9379, 0.0503,  ..., 0.7608, 0.2384, 0.8928])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 0.06696105003356934 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '15680', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 8.408252954483032}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     497,     993,  ..., 2499009,
+                            2499527, 2500000]),
+       col_indices=tensor([  20,   30,   45,  ..., 4969, 4978, 4985]),
+       values=tensor([0.7997, 0.6173, 0.7759,  ..., 0.4558, 0.1544, 0.5880]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.0537, 0.7107, 0.3297,  ..., 0.5168, 0.3739, 0.3386])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 8.408252954483032 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '19580', '-ss', '5000', '-sd', '0.1']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 2500000, "MATRIX_DENSITY": 0.1, "TIME_S": 10.499455213546753}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     479,    1002,  ..., 2498942,
+                            2499462, 2500000]),
+       col_indices=tensor([   2,    9,   12,  ..., 4986, 4993, 4996]),
+       values=tensor([0.9703, 0.1385, 0.8259,  ..., 0.8503, 0.3399, 0.6161]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4198, 0.0425, 0.0591,  ..., 0.3064, 0.5860, 0.9864])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.499455213546753 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     479,    1002,  ..., 2498942,
+                            2499462, 2500000]),
+       col_indices=tensor([   2,    9,   12,  ..., 4986, 4993, 4996]),
+       values=tensor([0.9703, 0.1385, 0.8259,  ..., 0.8503, 0.3399, 0.6161]),
+       size=(5000, 5000), nnz=2500000, layout=torch.sparse_csr)
+tensor([0.4198, 0.0425, 0.0591,  ..., 0.3064, 0.5860, 0.9864])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 2500000
+Density: 0.1
+Time: 10.499455213546753 seconds
+
+[18.41, 17.85, 18.5, 18.03, 18.16, 17.87, 17.95, 21.49, 18.34, 18.02]
+[87.53]
+14.54220986366272
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19580, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.499455213546753, 'TIME_S_1KI': 0.5362336676990169, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1272.879629366398, 'W': 87.53}
+[18.41, 17.85, 18.5, 18.03, 18.16, 17.87, 17.95, 21.49, 18.34, 18.02, 18.22, 18.11, 19.31, 20.46, 18.5, 18.28, 17.98, 18.24, 17.73, 18.09]
+333.17
+16.6585
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 19580, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 2500000, 'MATRIX_DENSITY': 0.1, 'TIME_S': 10.499455213546753, 'TIME_S_1KI': 0.5362336676990169, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1272.879629366398, 'W': 87.53, 'J_1KI': 65.00917412494371, 'W_1KI': 4.470377936670071, 'W_D': 70.8715, 'J_D': 1030.6282263525725, 'W_D_1KI': 3.6195863125638406, 'J_D_1KI': 0.18486140513604907}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.json
new file mode 100644
index 0000000..e35fdc8
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 9170, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.682870388031006, "TIME_S_1KI": 1.1649804130895316, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1294.0732465410233, "W": 85.83, "J_1KI": 141.1203104188684, "W_1KI": 9.359869138495092, "W_D": 69.25649999999999, "J_D": 1044.1918187005517, "W_D_1KI": 7.552508178844056, "J_D_1KI": 0.8236104884235611}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.output
new file mode 100644
index 0000000..5537faf
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.2.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 0.13022398948669434}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1019,    1995,  ..., 4997994,
+                            4998986, 5000000]),
+       col_indices=tensor([   3,    6,   16,  ..., 4985, 4988, 4992]),
+       values=tensor([0.7011, 0.4191, 0.0262,  ..., 0.8760, 0.6600, 0.8030]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.6407, 0.2177, 0.8923,  ..., 0.2240, 0.7772, 0.6410])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 0.13022398948669434 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '8063', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 9.23195219039917}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,     967,    1963,  ..., 4997972,
+                            4999023, 5000000]),
+       col_indices=tensor([   1,    8,   12,  ..., 4992, 4997, 4998]),
+       values=tensor([0.9256, 0.0587, 0.0398,  ..., 0.7563, 0.3156, 0.8023]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.2454, 0.6913, 0.0165,  ..., 0.7228, 0.0396, 0.9796])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 9.23195219039917 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '9170', '-ss', '5000', '-sd', '0.2']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 5000000, "MATRIX_DENSITY": 0.2, "TIME_S": 10.682870388031006}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1039,    2045,  ..., 4997951,
+                            4999001, 5000000]),
+       col_indices=tensor([   1,    3,   21,  ..., 4981, 4993, 4997]),
+       values=tensor([0.7269, 0.5102, 0.5857,  ..., 0.9126, 0.4820, 0.1281]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7504, 0.3556, 0.6036,  ..., 0.9776, 0.8116, 0.9002])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.682870388031006 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1039,    2045,  ..., 4997951,
+                            4999001, 5000000]),
+       col_indices=tensor([   1,    3,   21,  ..., 4981, 4993, 4997]),
+       values=tensor([0.7269, 0.5102, 0.5857,  ..., 0.9126, 0.4820, 0.1281]),
+       size=(5000, 5000), nnz=5000000, layout=torch.sparse_csr)
+tensor([0.7504, 0.3556, 0.6036,  ..., 0.9776, 0.8116, 0.9002])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 5000000
+Density: 0.2
+Time: 10.682870388031006 seconds
+
+[18.71, 17.74, 17.83, 18.37, 18.26, 18.61, 17.98, 17.99, 18.09, 22.02]
+[85.83]
+15.07716703414917
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.682870388031006, 'TIME_S_1KI': 1.1649804130895316, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1294.0732465410233, 'W': 85.83}
+[18.71, 17.74, 17.83, 18.37, 18.26, 18.61, 17.98, 17.99, 18.09, 22.02, 18.37, 17.98, 18.13, 19.94, 19.96, 18.01, 18.17, 17.86, 18.08, 17.84]
+331.47
+16.573500000000003
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 9170, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 5000000, 'MATRIX_DENSITY': 0.2, 'TIME_S': 10.682870388031006, 'TIME_S_1KI': 1.1649804130895316, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1294.0732465410233, 'W': 85.83, 'J_1KI': 141.1203104188684, 'W_1KI': 9.359869138495092, 'W_D': 69.25649999999999, 'J_D': 1044.1918187005517, 'W_D_1KI': 7.552508178844056, 'J_D_1KI': 0.8236104884235611}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.json
new file mode 100644
index 0000000..b09a9ac
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 5638, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.486673831939697, "TIME_S_1KI": 1.859998905984338, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1324.2176235437394, "W": 84.45, "J_1KI": 234.87364731176646, "W_1KI": 14.978715856686769, "W_D": 67.81275, "J_D": 1063.3373434098958, "W_D_1KI": 12.027802412202908, "J_D_1KI": 2.1333455857046664}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.output
new file mode 100644
index 0000000..933e0af
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.3.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 0.19828367233276367}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1476,    3005,  ..., 7497107,
+                            7498540, 7500000]),
+       col_indices=tensor([   1,    2,    5,  ..., 4997, 4998, 4999]),
+       values=tensor([0.6696, 0.7406, 0.6295,  ..., 0.1006, 0.8443, 0.3530]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.2303, 0.2102, 0.7986,  ..., 0.7152, 0.6703, 0.2250])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 0.19828367233276367 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5295', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 9.860368490219116}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1522,    3011,  ..., 7496960,
+                            7498495, 7500000]),
+       col_indices=tensor([   0,    6,    7,  ..., 4993, 4995, 4996]),
+       values=tensor([0.9810, 0.8874, 0.6033,  ..., 0.0373, 0.4053, 0.1135]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.3541, 0.8542, 0.0162,  ..., 0.1880, 0.1632, 0.2816])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 9.860368490219116 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '5638', '-ss', '5000', '-sd', '0.3']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 7500000, "MATRIX_DENSITY": 0.3, "TIME_S": 10.486673831939697}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1590,    3134,  ..., 7496893,
+                            7498424, 7500000]),
+       col_indices=tensor([   1,    9,   13,  ..., 4990, 4995, 4996]),
+       values=tensor([0.2379, 0.8284, 0.2265,  ..., 0.1431, 0.4495, 0.9988]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.8528, 0.9292, 0.5816,  ..., 0.4785, 0.5785, 0.0165])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.486673831939697 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([      0,    1590,    3134,  ..., 7496893,
+                            7498424, 7500000]),
+       col_indices=tensor([   1,    9,   13,  ..., 4990, 4995, 4996]),
+       values=tensor([0.2379, 0.8284, 0.2265,  ..., 0.1431, 0.4495, 0.9988]),
+       size=(5000, 5000), nnz=7500000, layout=torch.sparse_csr)
+tensor([0.8528, 0.9292, 0.5816,  ..., 0.4785, 0.5785, 0.0165])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 7500000
+Density: 0.3
+Time: 10.486673831939697 seconds
+
+[18.49, 17.91, 18.44, 17.9, 18.58, 17.81, 17.92, 21.43, 18.39, 17.95]
+[84.45]
+15.680492877960205
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5638, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.486673831939697, 'TIME_S_1KI': 1.859998905984338, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1324.2176235437394, 'W': 84.45}
+[18.49, 17.91, 18.44, 17.9, 18.58, 17.81, 17.92, 21.43, 18.39, 17.95, 18.43, 20.84, 19.1, 18.06, 18.27, 17.75, 18.15, 17.72, 18.16, 17.76]
+332.745
+16.63725
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 5638, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 7500000, 'MATRIX_DENSITY': 0.3, 'TIME_S': 10.486673831939697, 'TIME_S_1KI': 1.859998905984338, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1324.2176235437394, 'W': 84.45, 'J_1KI': 234.87364731176646, 'W_1KI': 14.978715856686769, 'W_D': 67.81275, 'J_D': 1063.3373434098958, 'W_D_1KI': 12.027802412202908, 'J_D_1KI': 2.1333455857046664}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.json
new file mode 100644
index 0000000..6bb4210
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2997, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.852682113647461, "TIME_S_1KI": 3.6211818864355894, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1423.3116033363342, "W": 83.38, "J_1KI": 474.9121132253367, "W_1KI": 27.82115448782115, "W_D": 66.859, "J_D": 1141.295160559654, "W_D_1KI": 22.30864197530864, "J_D_1KI": 7.443657649418965}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.output
new file mode 100644
index 0000000..7229c6c
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.4.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 0.37653517723083496}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2029,     4093,  ...,  9995991,
+                             9998004, 10000000]),
+       col_indices=tensor([   2,    3,    6,  ..., 4991, 4993, 4995]),
+       values=tensor([0.7344, 0.2463, 0.3008,  ..., 0.7679, 0.9534, 0.0176]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.9829, 0.9205, 0.8754,  ..., 0.7887, 0.3539, 0.3362])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 0.37653517723083496 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2788', '-ss', '5000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 9.764836072921753}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2035,     4051,  ...,  9995975,
+                             9998000, 10000000]),
+       col_indices=tensor([   1,    7,   13,  ..., 4993, 4995, 4999]),
+       values=tensor([0.5646, 0.0016, 0.0540,  ..., 0.1360, 0.4218, 0.1397]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2261, 0.1429, 0.2182,  ..., 0.9969, 0.6483, 0.4023])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 9.764836072921753 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2997', '-ss', '5000', '-sd', '0.4']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 10000000, "MATRIX_DENSITY": 0.4, "TIME_S": 10.852682113647461}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2037,     4021,  ...,  9995904,
+                             9997943, 10000000]),
+       col_indices=tensor([   4,    9,   10,  ..., 4993, 4994, 4995]),
+       values=tensor([0.7234, 0.7434, 0.9107,  ..., 0.4914, 0.1939, 0.0446]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2935, 0.8834, 0.9590,  ..., 0.1952, 0.7236, 0.3428])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.852682113647461 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2037,     4021,  ...,  9995904,
+                             9997943, 10000000]),
+       col_indices=tensor([   4,    9,   10,  ..., 4993, 4994, 4995]),
+       values=tensor([0.7234, 0.7434, 0.9107,  ..., 0.4914, 0.1939, 0.0446]),
+       size=(5000, 5000), nnz=10000000, layout=torch.sparse_csr)
+tensor([0.2935, 0.8834, 0.9590,  ..., 0.1952, 0.7236, 0.3428])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 10000000
+Density: 0.4
+Time: 10.852682113647461 seconds
+
+[18.3, 18.02, 18.13, 22.02, 18.21, 18.26, 17.95, 17.94, 17.94, 18.03]
+[83.38]
+17.07017993927002
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2997, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.852682113647461, 'TIME_S_1KI': 3.6211818864355894, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1423.3116033363342, 'W': 83.38}
+[18.3, 18.02, 18.13, 22.02, 18.21, 18.26, 17.95, 17.94, 17.94, 18.03, 18.77, 18.01, 18.52, 18.59, 18.17, 17.81, 18.04, 18.12, 18.22, 17.84]
+330.41999999999996
+16.520999999999997
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2997, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 10000000, 'MATRIX_DENSITY': 0.4, 'TIME_S': 10.852682113647461, 'TIME_S_1KI': 3.6211818864355894, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1423.3116033363342, 'W': 83.38, 'J_1KI': 474.9121132253367, 'W_1KI': 27.82115448782115, 'W_D': 66.859, 'J_D': 1141.295160559654, 'W_D_1KI': 22.30864197530864, 'J_D_1KI': 7.443657649418965}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.json
new file mode 100644
index 0000000..65b89f9
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 2368, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.176312685012817, "TIME_S_1KI": 4.297429343333116, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1437.3391367673873, "W": 78.7, "J_1KI": 606.984432756498, "W_1KI": 33.2347972972973, "W_D": 62.306000000000004, "J_D": 1137.9269663968087, "W_D_1KI": 26.311655405405407, "J_D_1KI": 11.111340965120526}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.output
new file mode 100644
index 0000000..4c78e30
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_0.5.output
@@ -0,0 +1,85 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 0.48082637786865234}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2493,     4988,  ..., 12494973,
+                            12497489, 12500000]),
+       col_indices=tensor([   0,    3,    4,  ..., 4995, 4996, 4997]),
+       values=tensor([0.7019, 0.9323, 0.5533,  ..., 0.8475, 0.3948, 0.0670]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.2037, 0.7210, 0.2412,  ..., 0.0549, 0.3207, 0.0757])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 0.48082637786865234 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2183', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 9.679495573043823}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2494,     4974,  ..., 12494974,
+                            12497479, 12500000]),
+       col_indices=tensor([   2,    3,    5,  ..., 4990, 4992, 4993]),
+       values=tensor([0.3550, 0.2209, 0.9878,  ..., 0.1100, 0.7010, 0.8735]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.0897, 0.5505, 0.7451,  ..., 0.8823, 0.0649, 0.5912])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 9.679495573043823 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '2368', '-ss', '5000', '-sd', '0.5']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 12500000, "MATRIX_DENSITY": 0.5, "TIME_S": 10.176312685012817}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2500,     5025,  ..., 12495060,
+                            12497549, 12500000]),
+       col_indices=tensor([   3,    7,    9,  ..., 4994, 4997, 4998]),
+       values=tensor([0.3762, 0.0915, 0.3071,  ..., 0.1948, 0.2052, 0.2572]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.2717, 0.6125, 0.0994,  ..., 0.4315, 0.1911, 0.5863])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.176312685012817 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([       0,     2500,     5025,  ..., 12495060,
+                            12497549, 12500000]),
+       col_indices=tensor([   3,    7,    9,  ..., 4994, 4997, 4998]),
+       values=tensor([0.3762, 0.0915, 0.3071,  ..., 0.1948, 0.2052, 0.2572]),
+       size=(5000, 5000), nnz=12500000, layout=torch.sparse_csr)
+tensor([0.2717, 0.6125, 0.0994,  ..., 0.4315, 0.1911, 0.5863])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 12500000
+Density: 0.5
+Time: 10.176312685012817 seconds
+
+[18.38, 18.14, 18.13, 18.09, 18.53, 18.05, 18.22, 17.96, 18.23, 17.96]
+[78.7]
+18.263521432876587
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2368, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.176312685012817, 'TIME_S_1KI': 4.297429343333116, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1437.3391367673873, 'W': 78.7}
+[18.38, 18.14, 18.13, 18.09, 18.53, 18.05, 18.22, 17.96, 18.23, 17.96, 18.81, 17.96, 18.41, 17.98, 18.95, 18.19, 18.23, 17.96, 18.21, 18.13]
+327.88
+16.394
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 2368, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 12500000, 'MATRIX_DENSITY': 0.5, 'TIME_S': 10.176312685012817, 'TIME_S_1KI': 4.297429343333116, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1437.3391367673873, 'W': 78.7, 'J_1KI': 606.984432756498, 'W_1KI': 33.2347972972973, 'W_D': 62.306000000000004, 'J_D': 1137.9269663968087, 'W_D_1KI': 26.311655405405407, 'J_D_1KI': 11.111340965120526}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json
new file mode 100644
index 0000000..2ac6398
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 357325, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.499107599258423, "TIME_S_1KI": 0.029382516194664303, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 1011.0901748657226, "W": 72.8, "J_1KI": 2.829609388835717, "W_1KI": 0.20373609459175818, "W_D": 46.4985, "J_D": 645.7991276922226, "W_D_1KI": 0.1301294339886658, "J_D_1KI": 0.0003641766850588842}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output
new file mode 100644
index 0000000..109e8d2
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_1e-05.output
@@ -0,0 +1,329 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 0.020415544509887695}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([1333, 4323,  980, 4513,  212, 4800,  425, 2650, 4181,
+                            969,  725, 4708, 2978, 4829, 2835, 2475, 4298,  490,
+                            871, 3121, 4762, 4479,  218, 2461, 4825, 2919,  512,
+                           1363, 1944, 2772,  493, 2448, 4663, 4533, 3227, 2251,
+                           3206, 1036, 4160, 3108, 4112,  520, 4884, 1695, 3846,
+                           3433, 4341,  314, 4963, 4557, 1460, 2013, 3131, 1087,
+                           1051, 4820, 1214, 2275,   33, 4231,  755, 1677, 4993,
+                              6,  698, 1317, 4059, 4178, 3988, 3341, 1529, 1327,
+                           4807, 1459, 3985, 2360, 1563, 1141, 2987, 4820, 3117,
+                           3988,  665, 4532, 3163, 3252, 4621, 4938, 2411, 4883,
+                           2036, 4582,  143, 3182,  722, 2609, 1076, 3554,  197,
+                             34, 2097, 1008, 2860, 1655, 2265, 3241, 2350, 1493,
+                           3432,  135, 3780, 2490, 3232, 1376, 4188, 3874, 3039,
+                            211, 1650, 4645, 4026, 3400, 1898,  591, 1094, 2347,
+                           3740, 1237,  925, 2787, 2157, 2054, 2757, 4014, 2493,
+                           4673, 4093,  396, 3012, 1306, 4796,   91, 1743,  543,
+                           4293, 1561, 1048, 3859, 1955, 3884, 1858,  722, 4225,
+                              3, 2890, 2664, 3170,  385, 1272,  188,  920, 1323,
+                           3875,  304, 4410, 2754, 3377, 4890, 4523, 4463, 2061,
+                           4108, 1554, 1408,  436, 1757,  483, 3973, 1483, 4992,
+                           3373,  401, 3150, 4902,  982, 3824, 3035,  683, 1843,
+                           1258,  996, 3656, 3918, 3943, 3598,  214,  724, 3021,
+                            312, 2986, 2034, 3929, 4868, 4093,  391, 1333, 1344,
+                             94, 1127,  616,   21, 1697,  646,  658, 1404, 4972,
+                           1106, 2493,  822, 2442, 3750, 3893,  297, 3607, 4990,
+                           1137, 2122, 3346,   36,  515, 2447, 1921, 2437, 4525,
+                            918, 1225, 2348, 1665, 3413,  132, 2821,  111,  225,
+                           4735, 1677, 1746, 1399, 3492, 1994, 2191]),
+       values=tensor([0.3602, 0.2302, 0.1896, 0.7717, 0.8956, 0.2078, 0.9487,
+                      0.0390, 0.3051, 0.4281, 0.9325, 0.8375, 0.6844, 0.7644,
+                      0.9804, 0.7332, 0.5227, 0.6093, 0.2260, 0.2957, 0.5954,
+                      0.5378, 0.5320, 0.4643, 0.3270, 0.8112, 0.8555, 0.5438,
+                      0.7934, 0.2222, 0.8906, 0.2729, 0.9780, 0.8597, 0.4400,
+                      0.4858, 0.2262, 0.4282, 0.8462, 0.2562, 0.8815, 0.7446,
+                      0.8391, 0.9365, 0.8781, 0.6986, 0.6943, 0.7579, 0.7636,
+                      0.9590, 0.7739, 0.5121, 0.8335, 0.2962, 0.5344, 0.4995,
+                      0.3316, 0.3046, 0.9720, 0.3429, 0.6108, 0.9040, 0.4735,
+                      0.4252, 0.9794, 0.6492, 0.0484, 0.2038, 0.5032, 0.1575,
+                      0.2125, 0.6202, 0.9042, 0.6689, 0.5066, 0.9113, 0.9195,
+                      0.2614, 0.9604, 0.9164, 0.9297, 0.6647, 0.8507, 0.2096,
+                      0.8186, 0.7548, 0.5297, 0.5479, 0.9328, 0.6830, 0.1651,
+                      0.9770, 0.1478, 0.1631, 0.6486, 0.7340, 0.9314, 0.8298,
+                      0.0625, 0.5779, 0.9267, 0.8341, 0.6247, 0.5959, 0.2706,
+                      0.5015, 0.0222, 0.8356, 0.2739, 0.1607, 0.6428, 0.2275,
+                      0.4531, 0.9656, 0.4285, 0.6913, 0.6388, 0.2488, 0.5417,
+                      0.6641, 0.6693, 0.7008, 0.2798, 0.3263, 0.3780, 0.6106,
+                      0.1001, 0.9342, 0.1338, 0.9089, 0.1127, 0.8287, 0.1281,
+                      0.0152, 0.2533, 0.8228, 0.6184, 0.5915, 0.5316, 0.8294,
+                      0.0163, 0.4275, 0.8868, 0.8268, 0.7667, 0.0040, 0.2374,
+                      0.5011, 0.5032, 0.5731, 0.6046, 0.1518, 0.5693, 0.3308,
+                      0.3440, 0.5026, 0.8207, 0.2844, 0.6651, 0.1261, 0.7301,
+                      0.3788, 0.3102, 0.2854, 0.7833, 0.8403, 0.4733, 0.1726,
+                      0.2895, 0.2693, 0.2450, 0.4577, 0.3046, 0.8813, 0.0902,
+                      0.8416, 0.8712, 0.6652, 0.0828, 0.2244, 0.6326, 0.9806,
+                      0.2561, 0.7608, 0.6760, 0.2091, 0.8004, 0.6240, 0.1978,
+                      0.2246, 0.8102, 0.6207, 0.2204, 0.2354, 0.0013, 0.2440,
+                      0.6407, 0.4005, 0.5265, 0.2470, 0.8146, 0.4334, 0.7311,
+                      0.4576, 0.4028, 0.8969, 0.8062, 0.4521, 0.2212, 0.0569,
+                      0.5145, 0.3716, 0.8367, 0.2637, 0.4458, 0.4981, 0.9509,
+                      0.3003, 0.2789, 0.8010, 0.0297, 0.6055, 0.0149, 0.5383,
+                      0.2695, 0.1287, 0.0767, 0.7664, 0.3198, 0.2386, 0.2384,
+                      0.1616, 0.1390, 0.9600, 0.6032, 0.7446, 0.5942, 0.1408,
+                      0.5912, 0.3909, 0.1909, 0.4245, 0.3596, 0.3316, 0.0552,
+                      0.1715, 0.2483, 0.6015, 0.5276, 0.6350]),
+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.0146, 0.8199, 0.1846,  ..., 0.5715, 0.8048, 0.4041])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 0.020415544509887695 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '51431', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 1.5112977027893066}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([4110, 4882, 2528,  560, 2850, 1135, 2189,  267,  591,
+                           4248,  392, 4389,  952, 4426,  675,  791, 4134, 2003,
+                            330, 4266, 1795, 3038, 2537, 1414, 4513, 1875, 1420,
+                           3040, 4172, 4236, 4029,   37, 2565, 1038, 1280,  832,
+                           4901, 4729,   13,   26,  702, 4420, 4734, 1556,  899,
+                           2280, 4293, 3965, 1974, 4811, 4611, 1009,  692, 1950,
+                            707, 1172, 1767, 2120, 1996,  634, 3311, 4113, 1903,
+                           2733, 3685, 3363, 3449, 2814, 1438, 4520,  218,  740,
+                            498,  655, 1313, 1745, 4116,   95,  357, 4580, 2628,
+                           4483, 1655, 4732,  165, 2596,  205, 1356, 2811, 2330,
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+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.4917, 0.3630, 0.9281,  ..., 0.8752, 0.5291, 0.1405])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 1.5112977027893066 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '357325', '-ss', '5000', '-sd', '1e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 250, "MATRIX_DENSITY": 1e-05, "TIME_S": 10.499107599258423}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([2104, 1861,    7, 1520, 3728, 1941, 2107,  138, 2161,
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+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.5944, 0.7034, 0.9760,  ..., 0.8304, 0.5842, 0.4500])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.499107599258423 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([  0,   0,   0,  ..., 250, 250, 250]),
+       col_indices=tensor([2104, 1861,    7, 1520, 3728, 1941, 2107,  138, 2161,
+                           3365,  332, 4735, 2493, 4284,  393, 1314, 1302, 4705,
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+       size=(5000, 5000), nnz=250, layout=torch.sparse_csr)
+tensor([0.5944, 0.7034, 0.9760,  ..., 0.8304, 0.5842, 0.4500])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 250
+Density: 1e-05
+Time: 10.499107599258423 seconds
+
+[18.49, 21.77, 18.79, 18.12, 18.06, 18.03, 18.09, 17.74, 18.25, 17.89]
+[72.8]
+13.888601303100586
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 357325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.499107599258423, 'TIME_S_1KI': 0.029382516194664303, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1011.0901748657226, 'W': 72.8}
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+526.03
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+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 357325, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 250, 'MATRIX_DENSITY': 1e-05, 'TIME_S': 10.499107599258423, 'TIME_S_1KI': 0.029382516194664303, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 1011.0901748657226, 'W': 72.8, 'J_1KI': 2.829609388835717, 'W_1KI': 0.20373609459175818, 'W_D': 46.4985, 'J_D': 645.7991276922226, 'W_D_1KI': 0.1301294339886658, 'J_D_1KI': 0.0003641766850588842}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.json b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.json
new file mode 100644
index 0000000..e1d6586
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.json
@@ -0,0 +1 @@
+{"CPU": "Xeon 4216", "CORES": 16, "ITERATIONS": 344337, "MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.209988117218018, "TIME_S_1KI": 0.029651150231366417, "BASELINE_TIME_S": 10, "BASELINE_DELAY_S": 10, "J": 996.8449161434174, "W": 73.42, "J_1KI": 2.894968929111357, "W_1KI": 0.21322135001466586, "W_D": 47.158, "J_D": 640.2780244550705, "W_D_1KI": 0.13695304309441042, "J_D_1KI": 0.0003977296749823877}
diff --git a/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.output b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.output
new file mode 100644
index 0000000..395a17f
--- /dev/null
+++ b/pytorch/output_synthetic_maxcore_old2/xeon_4216_max_csr_10_10_10_synthetic_5000_5e-05.output
@@ -0,0 +1,81 @@
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '100', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 0.019530296325683594}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    1,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([2184, 3902, 2442,  ..., 1965, 3430, 1316]),
+       values=tensor([0.1298, 0.8618, 0.5661,  ..., 0.6807, 0.5041, 0.2985]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.7563, 0.3154, 0.3739,  ..., 0.6493, 0.1597, 0.7226])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 0.019530296325683594 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '53762', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 1.6393845081329346}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1248, 1249, 1250]),
+       col_indices=tensor([ 543, 3603,  176,  ..., 1860, 3976,  394]),
+       values=tensor([0.3999, 0.3952, 0.5467,  ..., 0.7650, 0.4806, 0.1331]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.3582, 0.6649, 0.9739,  ..., 0.1792, 0.8232, 0.7194])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 1.6393845081329346 seconds
+
+['apptainer', 'run', 'pytorch-xeon_4216.sif', 'numactl', '--cpunodebind=0', '--membind=0', 'python3', 'spmv.py', 'synthetic', 'csr', '344337', '-ss', '5000', '-sd', '5e-05']
+{"MATRIX_TYPE": "synthetic", "MATRIX_FORMAT": "csr", "MATRIX_SHAPE": [5000, 5000], "MATRIX_ROWS": 5000, "MATRIX_SIZE": 25000000, "MATRIX_NNZ": 1250, "MATRIX_DENSITY": 5e-05, "TIME_S": 10.209988117218018}
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([2469, 1957, 4805,  ...,  986, 4084, 3397]),
+       values=tensor([0.1203, 0.9340, 0.5005,  ..., 0.1600, 0.9840, 0.6585]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.7772, 0.5114, 0.7654,  ..., 0.7246, 0.7942, 0.6121])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.209988117218018 seconds
+
+/nfshomes/vut/ampere_research/pytorch/spmv.py:75: UserWarning: Sparse CSR tensor support is in beta state. If you miss a functionality in the sparse tensor support, please submit a feature request to https://github.com/pytorch/pytorch/issues. (Triggered internally at ../aten/src/ATen/SparseCsrTensorImpl.cpp:53.)
+  matrix = matrix.to_sparse_csr().type(torch.float32)
+tensor(crow_indices=tensor([   0,    0,    0,  ..., 1249, 1250, 1250]),
+       col_indices=tensor([2469, 1957, 4805,  ...,  986, 4084, 3397]),
+       values=tensor([0.1203, 0.9340, 0.5005,  ..., 0.1600, 0.9840, 0.6585]),
+       size=(5000, 5000), nnz=1250, layout=torch.sparse_csr)
+tensor([0.7772, 0.5114, 0.7654,  ..., 0.7246, 0.7942, 0.6121])
+Matrix Type: synthetic
+Matrix Format: csr
+Shape: torch.Size([5000, 5000])
+Rows: 5000
+Size: 25000000
+NNZ: 1250
+Density: 5e-05
+Time: 10.209988117218018 seconds
+
+[40.04, 39.64, 39.87, 39.84, 41.87, 42.36, 39.49, 37.87, 39.91, 39.97]
+[73.42]
+13.577293872833252
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 344337, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.209988117218018, 'TIME_S_1KI': 0.029651150231366417, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 996.8449161434174, 'W': 73.42}
+[40.04, 39.64, 39.87, 39.84, 41.87, 42.36, 39.49, 37.87, 39.91, 39.97, 18.46, 17.91, 18.96, 18.18, 18.6, 18.04, 18.09, 18.2, 18.22, 17.91]
+525.24
+26.262
+{'CPU': 'Xeon 4216', 'CORES': 16, 'ITERATIONS': 344337, 'MATRIX_TYPE': 'synthetic', 'MATRIX_FORMAT': 'csr', 'MATRIX_SHAPE': [5000, 5000], 'MATRIX_ROWS': 5000, 'MATRIX_SIZE': 25000000, 'MATRIX_NNZ': 1250, 'MATRIX_DENSITY': 5e-05, 'TIME_S': 10.209988117218018, 'TIME_S_1KI': 0.029651150231366417, 'BASELINE_TIME_S': 10, 'BASELINE_DELAY_S': 10, 'J': 996.8449161434174, 'W': 73.42, 'J_1KI': 2.894968929111357, 'W_1KI': 0.21322135001466586, 'W_D': 47.158, 'J_D': 640.2780244550705, 'W_D_1KI': 0.13695304309441042, 'J_D_1KI': 0.0003977296749823877}